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Data Analyst Bootcamp for Beginners (SQL, Tableau, Power BI, Python, Excel, Pandas, Projects, more)

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you are in the right place to learn to

become a data analyst in this massive

boot camp Alex the analyst will cover

all the core topics that data analysts

need to know and along the way you'll

build plenty of projects to gain

hands-on experience hello everybody my

name is Alex freeberg better known as

Alex the analyst on YouTube and in this

video you're going to be taking my

entire data analyst boot camp this boot

camp is comprised of videos that I've

made over the past 3 years and they

cover a lot of different topics like SQL

Excel powerbi tableau and python

throughout the boot camp there are a lot

of Hands-On guided projects that will

really help you learn these skills well

and speaking of projects there's an

entire Part near the end where you can

build a free portfolio website where you

can put all of your projects on so that

hiring managers and recruiters can go

and look at all these projects that

you've built if you wanted to go even

more in depth into the skills that we

learn in this boot camp I have a data

analytics learning platform called

analyst Builder analy Builder was

designed specifically for data analyst

so all of the courses and all the

content are just for you and it has a

coding section where you can learn and

practice for technical interviews and

lastly before we jump into the boot camp

I want to give a huge shout out to free

code camp for putting this all together

personally learned a ton from free code

camp and so I'm really honored that my

boot camp is going to be here for you

guys to learn and I really hope you

enjoy it what's going on everybody it is

2023 and in this video I'm going to help

you become a data [Music]

[Music]

analyst we're going to start at the very

beginning assuming you haven't started

this process at all of becoming a data

analyst if you already have you can kind

of find IDE identify where you are in

this process and then go from there now

before we dive into everything I want to

warn you I will be mentioning my own

channel a lot in this video I have

videos and playlists on just about every

single topic that we're going to be

talking about today I'll have all the

links to those videos in the description

so you can dive into those topics more

in depth so I hope that's okay and it's

all completely free I've been building

this out for the past 3 years and

honestly you can probably get 90% of the

way to learning everything you need for

data analytics just on my channel so now

that I've warned you let's J been of

number one and that is learn the data

analyst skills now there are literally a

hundred different things that you can

learn for data analytics you can learn

things like alter X or a cloud platform

or different programming languages but

there are some core skills that I

recommend you start out with before kind

of branching into some of those other

skills the number one skill that I

always recommend people start with is

SQL SQL is just one of those fundamental

skills I think everybody should learn

even if you don't use SQL you'll use

some variation of SQL if your company

has a large enough data set SQL is used

to actually query and retrieve data from

a database so if your company collects

data which every company does they're

going to put it somewhere to store it's

usually stored in a database and sqls

how you get that data from the database

I think SQL is also fairly easy to learn

which makes it really good when you're

just starting out I have several

playlists dedicated to SQL starting from

beginner all the way to Advanced and you

can learn all of that for free one other

reason why I think you should learn SQL

first is that a lot of companies

interview or have a technical interview

during the interview process on SQL

that's something that really caught me

off guard when I was first starting out

out because I thought it was going to be

more behavioral I didn't even know what

a technical interview was so knowing SQL

actually became a really important part

of interviewing and getting a job as a

data analyst the second skill that I

would learn is a business intelligence

tool like Tableau or powerbi now there

are a ton of different bi tools I can

literally name 10 off the top of my head

that I've used throughout my career but

what I will say is that learning

something like Tableau or powerbi is

pretty transferable to almost all those

other bi tools they're all fairly

similar and how they do things and how

they show display the data you most

likely won't have a technical interview

asking you about Tableau or powerbi like

to build something for them that usually

does not happen but the combination of

SQL where you can query your data and

then taking that data to build something

that is a really really great

combination to learn right away I have

entire series on both Tableau and

powerbi with projects on my channel the

third skill that I would learn is Excel

now most people have used Excel they

know what Excel is and how it's used but

it can be used a little bit differently

for a data analyst for example example

in Excel a lot of people haven't cleaned

data in Excel or built charts and graphs

using Excel and those are things that

data analysts would probably do excel is

also just a fundamental skill that every

company is going to expect you to know

so I have an entire playlist dedicated

to excel to actually walk you through

how to use it for data analysis the

fourth skill that I recommend you learn

is python now a lot of people will have

python higher up on their list they only

use Python they don't use SQL or a bi

tool they just do everything in Python

now python is a fantastic tool you can

use it to manipulate your data to create

data visualizations and a ton more like

web scraping and regular expression and

a hundred different other things but it

can be kind of hard to learn it took me

a long time to really learn the basics

very well that's really the only reason

why it is farther back I feel like SQL

and a bi tool are really easy to learn

and really pack a big punch whereas

python can be quite tough to learn in my

experience and you may not use it as

often as you would something like SQL or

a bi tool if you're interested in

learning py python I have an entire

series dedicated to python as well as

projects that you can build again I

warned you there's going to be a lot of

self-promotion in this video I have

videos on just about every single one of

these topics the fifth and the last

skill that I recommend you learning and

this is the only one that I don't have a

series on yet I will make those is

learning a cloud platform like AWS

Google Cloud platform or Azure there's

no denying that these platforms have

played a huge impact in how we use data

as a whole in the data analyst industry

they can be kind of tough to learn

though if you aren't using it Hands-On

in an actual job I think that learning a

cloud platform is already something that

most people should start working towards

because in the future it's only going to

become more prevalent now where can you

go and actually learn all of these

skills that you need to become a data

analyst well the number one place I'd

recommend of course is my channel I have

free tutorials on all these skills and a

lot of other topics and I think it's

just a really great place to start the

next place that I recommend you looking

at is udemy I recommend udemy especially

if you're just starting out because it's

pretty pretty cheap you can buy an

entire course entire SQL course for $10

or $15 and they have courses on every

single one of these skills and I just

recently made a video called DIY data

analysts curriculum using udemy for

under $75 so you can create an entire

curriculum to learn all of these skills

for under $75 which is just amazing the

next place I'm going to recommend you

look is corsera now udemy is fantastic

they have really good instructors and

good courses but as a whole I find that

sometimes corsera just has more

professional or better content corsera

is a bit more expensive though you're

looking at $59 per month for all of

their courses or you can pay upfront an

annual fee of $399 so again it's just a

lot more expensive I moved to corsera

once I started having a data analyst job

and had a bit more money but when I was

first starting out I just couldn't

afford it so I went to udemy and it was

a really great place to start there's

also places like data camp and data

Quest that kind of gamify learning and

they're more text based so all these

other platforms udem me corsera and me

they're all video based but if you like

reading data camp and data Quest are a

lot more of text where you can learn it

by reading it and doing it after you

learn all of these skills the next thing

that I recommend you do is actually

build projects with those skills now

what is building a project actually mean

it means taking a skill and then

building something out of it that you

can then show a potential employer for

example if you went through and learn

Tableau you go and take a data set and

you could build a visualization and a

dashboard in tableau and that would be a

project with these projects you can

build something called a portfolio and I

usually call it a portfolio website a

portfolio website is a website that you

create where you store all of your

projects and then you can share that

with recruiters and hiring managers so

that they can see all of your work now

do you absolutely need a portfolio to

show employers no you don't but it does

help in two different ways the first

thing that it may do is actually help

you land the interview if you have a

link on your resume and they click on it

they may see your skills and see your

projects and be like man this person

really knows what they're doing this is

exactly what we need the second reason

that I recommend building projects is

because most likely during your

interview you're going to get asked

questions like how have you used SQL how

have you used Tableau and if you don't

have any experience in that you're just

going to say well you know I've taken

courses to learn it but with a project

you can be a lot more specific you'll be

able to say well I actually just built

out this project in Tableau I took the

data and cleaned it in Excel and then I

put it in Tableau and built out this

Dash board and here are the insights

that I found from this data set it's

just a much better answer and as a

hiring manager myself I can tell you

that it is definitely beneficial to

build out these projects The Next Step

that I recommend you take in becoming a

data analyst is building a data analyst

resume the resume to say the least is

extremely important it's what's going to

actually allow you to land an interview

to potentially get a job now if you were

like me when I was first starting out I

had a resume it just had nothing to do

with data analytics so how do you make a

data analyst resume if you don't have

any experience as a data analyst well

you are asking the perfect questions

because the very first things that we

talked about are what are going to go on

your resume those skills and those

projects if you have no experience or

degree like myself who has a

recreational therapy degree if you have

no background in this it can be really

daunting to kind of display that you

know what you're doing and that a

company should hire you so what I

usually recommend is right beneath your

contact at the top you put your skills

and your projects that you built out on

your resume things like work experience

and education should go on your resume

as well but just a little bit lower you

want them to see those things before

they see that your last work experience

was at Domino's and you have a degree in

Marine Biology it's just not relevant to

data analysis and if you put those

things at the top they're probably going

to rule you out right away the fourth

step to become a data analyst is

actually applying you have the skills

you have the projects you have the

resume now you're ready to start

applying for those data analyst jobs now

there's there's a lot of different

opinions on how you need to go about

applying for data analyst jobs but I'll

give you my take on it and this has been

the most successful for me in my career

the first thing I want to mention is

actually what I would not do which is

just blindly apply on glass door monster

zip recruiter and all these other

platforms to just any data analyst job

that you can find now I'm not against

this I think you should do that but I

don't think that's the only thing that

you should do because the chances of you

getting a call back or actually hearing

something back are extremely low to

really increase your chance of becoming

a data analyst I highly highly highly

recommend working with a recruiter a

recruiter is literally someone who is

there to help you find a job now when I

first started out I didn't understand

what a technical recruiter was at all I

was kind of nervous or scared to work

with him but it's actually pretty simple

a company has a position that they want

to fill and they don't want to spend

hours and hours and hours to find

someone to fill that position so they

hire a recruiter a recruiter is going to

go out and try to find someone to fill

that position AKA you and so if you go

into talk to that recruiter and they

have a position that opens up they will

help you get that interview and then if

you get a job let's say for

$50,000 the company is going to pay that

recruiter let's say 10% of your salary

so they'll give them $5,000 so you don't

actually lose or have anything to lose

using a recruiter you can reach out to

Recruiters in several ways and I've done

every variation but I'll tell you my

most successful way which was using

LinkedIn there are tens of thousands of

Recruiters on LinkedIn I made an entire

video of how you can reach out to

recruiters and what to St Recruiters on

LinkedIn to help you land a job so be

sure to check out that video when you

actually get to that point but you can

also just cold email and cold call these

recruiting companies but to me it's just

not as effective as reaching out

directly on LinkedIn and this is just a

bonus one the last thing that you need

to do is accept a job offer so on step

number four after you apply to those

jobs you do actually have to go in

interview and then get a job offer which

you will accept I just thought I'd

mentioned that just in case that was not

super clear now that was a lot of stuff

let's talk about time frames to actually

complete all of these things now doing

all of these things from scratch is

going to take a while but let's break it

down by each step and see how long I

generally think it's going to take let's

start with step number one which is

actually learning the skills now just to

be up front this one probably is going

to take the longest for most people for

most people to learn all of these skills

it's going to take around 3 to four

months now if you don't learn a cloud

platform and python which are the last

ones that I recommend and you just focus

on SQL a to in Excel I think you can do

that in under 3 months that is very

dependent though on how much time you

have to study that time frame is more

for someone who has several hours per

day maybe 3 hours in the end of a night

after you go to work that is someone who

has quite a bit of time to dedicate to

learning during their week of course

that time frame is going to take longer

if you don't have as much time to

dedicate to learning now let's look at

number two which was creating projects

and a portfolio of projects from my

experience when you're first starting

out it takes a lot longer to actually

create these projects it can take one

one or two weeks per project I usually

recommend people doing three to five

projects in their portfolio before they

start applying and since they can take

anywhere from 1 to two weeks you're

looking at anywhere from 3 to 6 weeks

The Next Step was to create a data

analyst resume now in my opinion this

one should take the shortest out of

every single step here because you're

really just kind of reformatting a

resume or creating a resume you're just

adding skills you're adding your

projects and then kind of reformatting

it to make it look nice this should

hopefully take under a week but if you

use something like a professional

service so they help you build a resume

it could take one to two weeks the two

last steps which kind of go hand inand

are step four and five which is actually

applying for jobs and then Landing a job

now this process can take as little as a

month or it can take as long as 6 months

or a year it really depends on how

you're applying where you're applying

and just the kind of luck that you're

having with actually Landing interviews

I've seen people who have never had any

experience land a job within a month of

starting to apply and it's incredible

it's amazing but it doesn't happen too

often you're usually looking at around 2

to 4 months on average to land your

first data analyst job if you put all of

those together and kind of average

everything out you're looking at around

6 months total for the entire process

now I don't want that to discourage you

okay 2023 is a long year you have a lot

of time and it doesn't have to take 6

months you could do it faster you could

do it in three months and just prove me

wrong but if you are really focused and

you are really driven to become a data

analyst this year I know that you can do

it now to maybe boost your spirits and

make you feel a little bit better I

didn't know any of these things when I

first started out I didn't have anyone

telling me kind of a plan on what to do

I had to go out and figure all these

things out by myself and it took me

almost a year to land my first real data

analyst job so with all that being said

I hope that this video is helpful I hope

you now have a path on how to become a

data analyst this year and that my

channel can be a big part of that so

thank you guys so much for watching I

really appreciate it if you like this

video be sure to like And subscribe

video [Music]

what's going on everybody my name is

Alex freeberg and in today's video we're

going to be starting our basics of SQL

series now in this series we're going to

be going over everything you need just

to get started and then in future videos

we're going to be going over some

intermediate Concepts and some more

advanced concepts and then in the final

series we're going to be going over some

portfolio projects in this video in

particular we're going to be downloading

SQL Server Studio we're going to be

creating our tables inserting data into

our tables and in future videos we're

going to actually learn how to query

those tables if you already have SQL

Server management Studio downloaded you

can skip ahead to where we actually

create the tables and insert the data

into the tables if you don't care about

that at all and you're just looking at a

query I would skip to the next video

where we actually start quering the data

that we inserted into those tables so to

download SQL Server management Studio we

actually have to download two things and

I have both links right here I'm going

to leave those in the descriptions that

you guys have those but this one is to

actually download SQL Server management

studio so let's go down here I actually

deleted it off my computer so I can walk

through this with you guys so we're

going to download that let's also go

over here this is actually a server so

we have to download a SQL server and if

you go down right here there's a free

version now I don't need the developer

version I'm just going to download the

express version it's actually smaller so

let's download that as

well now once this is done running we're

going to open it up and I'll show you

what to do next so it just finished

running let's click on

it all right so we need to install it

we're going to click yes and this is

going to take a little while so this

popped up I clicked install and it's

been running for the past couple minutes

apparently I was not recording so I

apologize for that but that's all I did

so now it's been installed I'm actually

going to pull it up right

here and let's open it

up now when it pulls up it's going to

ask you to connect to a server and

that's why we downloaded the SQL Express

that and there you go it's as easy as

that so now we have SQL Server

management Studio set up and we are good

to go so the first thing that we need to

do is actually create a database so

let's go over here to databases and

let's click new

database and let's just do SQL

SQL

tutorial keep it simple and if we click

that it's going to create our database

for us now when you open up the database

there's going to be a lot of stuff you

really do not need to know all this

really what we're going to be sticking

to is this tables right here uh as of

right now we do not have any tables so

we need to create tables now there's two

ways that you can do that you can click

right here and you can go to new and

create table we're not actually going to

do that we're going to create it using a

script or a t-sql so we're going to go

over here and do new query and we will

get started on actually creating uh the

two tables that we're going to be using

for all the stuff going forward all

right so let's get rid of me CU you

really don't need to be seeing me

anymore let's get started by doing our

very first table which is going to be

our employee demographics table so let's

start off by saying create table and we

have to name it so let's do

employee demographics and enter down we

want to do an open parenthesis now we

need to specify what our column names

are going to be and what the data type

is for each column so let's start off

with employee ID and we want that to be

an integer so that'll be like 1 2 3 4 uh anything

anything

numeric now we want to

do first name and let's make that varar

50 if you don't know what these data

types are that's okay uh that will

probably be covered in a different video

that's not really necessary for this

video uh let's do last name we'll also

make that varar 50 let's do age make

that an integer and very last let's do

gender and we will make that varar 50 as

well so now we have our very first

table let's run that and we'll see if it

works we'll go over here we'll refresh our

our

tables and there you go so we have our

very first table let's go up here let's

get rid of this one and now let's create

our second table so we're going to do

basically the exact same thing but we're

going to have a little bit different

information in it this is going to be

our employee salary table so let's do

it and enter and open parenthesis so now

we're going to do the same thing we're

going to do employee ID let's make that

an integer now we want the job title

because we want to know what they

do and this one is going to be varar 50

because we keep it pretty simple

whoops and then for our very last one

we're going to do salary and that will

be integer as

well and I'll just do PR here so let's

create this

table let's see if it is there and there

we go so let's open up one of these

tables really

quick see what's in there see what it

looks like as you can see we do not have

any information in there uh when you

create a new table sometimes when you

open it up you're going to see this if

you want to get rid of that you just

need to do a I think it's called A Hard

refresh or something like that but you

can do control shift R let's see if it

works for me I just did it all right it

goes away so now it recognizes it as a

table so we're good there let's go back

here and let's get rid of all this we've

already created our tables now we want

to insert the data into our tables so

let's see what that looks like let's do

insert into and now we need to specify

what table we're inserting our data into

so let's start off with employee

demographics let's do

values so now we have to select what

values we're going to put into um into

this table

so now we're going to have to do the

employee ID so let's do

101 then we're do first name so let's do

Jim last name

Halpert and then his age let's say he's

30 and he is a

male now just for fun let's execute that

let's go back to this table right here

and execute

and as you can see all of our

information actually went in there so

now we have his employee ID his first

name his last name age and gender now we

need a lot more information uh for this

table in order to actually learn a lot

of the concepts of quering the table so

I'm actually going to go through and add

a ton more information I'm not going to

bore you through that but I will show

you the final product before I actually

hit execute so stick with me I'm

actually just going to cut to the end

where I insert all my stuff down on here

and then if you want that I'll probably

leave it in the description or maybe put

in my GitHub or something so you can

easily just go copy and paste that if

that's what you want to do so I'll see

you in a few

seconds all right so I have all my

values right here I actually going to

take this one out cu I already did that

one but this is our additional

information let's insert that into our

table real quick and go back here and

take a look at it and there you go this

is going to be our core information that

we are querying off of

uh in future videos so that table is

completely finished let's go back here

we're going to get rid of this because

now we want to insert our information to

our other table so let's do insert into

and let's do

employee and now we're going to do

salary so let's do values to specify

that we're inserting values into

there and in this one we have employee

ID so again let's do in th1 that's

gym his job title is

$45,000 and let's execute that and you

can't see it but down here it says it's

done let's go to that

table and as you can see that is

inserted I'm going to do the exact same

thing as I did before I am going to fill

out all these and in a second it will be

done uh on your side and then again I

will leave it in the description or I'm

going to put it on my GitHub and you

guys can just copy and paste that if

that's what you want to do or you can

write it out whatever you want to do all

right just like before I'm going to get

rid of this first one that is Jim he is

already done now let's insert this

information Ed is finished let's go back

here and there we go now we have both of

our tables and we are good to go for

future videos so thank you so much for

sticking all the way through this one in

the next video we're going to actually

begin uh quering the table and learning

the select the from the where the group

by and the order by statement everything

is in these upcoming videos so stick

around and we will learn all of that

together thank you so much for joining

me if you like this type of content be

sure to subscribe below and I'll see you

in the next video what is going on

everybody my name is Alex freeberg and

in today's video we're going to be going

over the select and the from statement

so if you joined us for our last video

we went over creating our tables and

inserting data into those tables and so

we have this employee demographics table

and we also have this employee salary

table and today we're going to be

walking through the select statement in

the fir statement on these tables so

here are some of the concepts that we're

going to be going over today let's just

get it started by doing select

everything and let's do this from the

employee demographics table so let's

execute this if we wanted to only show

the first names we can just do first

name and run that

and if we want first name and last name

we can just separate that by using a

comma and it will return those well if

we want to return all columns and all

rows then all we have to do is use this

star so that's what the star does now we

have nine rows of data here and if we

only wanted to return let's say the top

five we can easily do that and we can

just say top five of everything now the

reason this could be useful is say you

have a table that has millions of rows

in it and you only want a small sample

you can say select top 1,000 and when

you do that it will only select the top

five rows now let's get everything back

in here really quick because we're going

to move on to this distinct feature so

when we use distinct we're actually

saying that we want the unique values in

a specific column so if we say distinct

and then let's do employee ID

D everything should be returned so all

nine rows should be returned and that's

because every single one of these are

unique now let's try gender so there's

only going to be two results the male

and the female and that's because

there's only two distinct values in that

column now let's look at all of our data

again so now we want to look at count

now count is very simple all is going to

do is going to show us all the non null

values in a column so let's look at last

name for example if we do count of last

name all that's going to give us is a

count of nine because we have nine last

names if for whatever reason somebody's

last name was left out and that was null

then it would have returned maybe eight

or seven depending on how many were

actually in there so if an entire column

was null we it would be a Return To

Zero and if you notice we are not given

a column name that's because this is

derived information based off the last

name so if we want to actually give this

a name so that that column does not say

no column name we can use this as right

here so once you put as you can actually

name it so since this is the count of

the last name we'll write last name

count keep it simple and if we execute

that as you can see we have last name

count right there so that's how you use

that as let's look at all of our data

again we want to look at some Max mins

and averages right now and the only

column here where it would be useful to

do it on is age but let's actually go

over and let's look at our salary table

and at our salary table we have some

really interesting salaries that I think

would be a little bit more useful for

this information so let's go over

to employee

salary all all right and let's look at

this table really quick so we have our

salary now we want to look at the maximum

maximum

salary that is

in uh that column and that is going to be

$65,000 now let's say we wanted to know

what the minimum salary was let's

execute this and the person who makes

the least money is making

$36,000 now what's the average what is

the average salary for all employees

that's going to be $

48,5 so so super easy to use all of

these things they're extremely useful I

use them every single day so I know that

each of these are very very useful and

are definitely among the basics that you

have to know let's look real quick at

everything really quick so we just

learned the select statement but

learning this from statement really

quick is also important up here this

actually shows us that we're already

Hitting off the SQL tutorial database

but let's say we change it to master

when we try to run this it's going to

give us an error and that's because now

we're hitting off this database and this

database does not have this table in it

so in order to do this in order to still

hit off that table while up here we're

actually hitting off a different table

we can change this information so the

from statement you have to specify three

separate things the first thing that you

need to specify is the database so let's

say we want to hit off the SQL tutorial database

database

now we want to select what table we're

going to do this is actually a dbo so

let's put dbo there's there's a lot that

can go into that um it's not worth

getting into now but dbo do and let's do employee

employee

salary when we execute this our

information comes up even though up here

we're still hitting off the master

database when we specify it right here

then we actually are choosing what

database and what table a hit off of and

so it does not matter what it is up here

so that's how you use the from statement

in the next video we're going to be

going over the wear statement and then

after that the group by and order by

statement and that will be the complete

basics of SQL tutorial and then we'll

start getting into a little bit more fun

stuff some more advanced concepts which

I think it be really really exciting for

everybody to learn thank you guys so

much for joining me I really appreciate

I hope this has been helpful if you like

this type of content subscribe below and

I'll see you in the next video thanks

and goodbye

what's going on everybody my name is

Alex freeberg and in this video we're

going to be going over the we statement

and SQL in the very first video we

created our table inserted data into our

table in the second video we went over

the select and the from statement and

now we are on to the wear statements now

what does the wear statement do it helps

limit the amount of data and specify

what data you want returned we have

quite a few Concepts that we're going to

be covering today let's just start out

with something really easy let's

do where first name

equals gym really simple so we're

selecting everything where our first

name equals gym and this is our output

so really really simple now let's try

where it does not equal this right here

says does not equal gym and let's

execute that and as you can see we have

everybody except Jim Halbert in there so

now let's look at the greater than or

less than so in this table I think the

one that we're going to look at is age

so let's look at age and let's do where

it's greater than

30 and when we execute that we're going

to get everyone who is over the age of

30 now as you can see we're not

including people who are 30 years old if

we want to include people who actually

are 30 years old we're going to add the

equal sign right there so we should be

seeing people who are now 30 so before

Pam and Jim were not in there and now

they are if we do the exact same thing

let's do less than 32

here's everyone that's going to be

included but if we want to include the

people who are 32y old then we are just

going to add that equal sign and now the

people who are 32 years old like Toby

and Meredith are now

included if we want to go even further

we want people who are less than or

equal than

32 and who are male we can say where gender

gender equals

equals

male so now we have two two things that

we are specifying that we need we need

somebody whose age is less than 32 and

we need their gender to be male so let's

execute that and we have four people who

meet that criteria so that's what the

and statement does if we write or then

only one of these criteria has to be

correct in order for it to be met so if

we hit execute now we're saying anybody

who's under the age or equal to 32 or

their gender equals male so if we look

down here Michael Scott is actually 35

years old so he's over 32 but since he

is male he is now included let's get rid

of everything really quick I want to

look at this like really quick so let's

execute just that and if you do that you

highlight just that hit execute then it

uh will only run what you have

highlighted so now let's look at this

whole table now when you're using like

you typically are doing this for

sometimes numerical but most of the time

you're using it for text

information so if we're looking at this

right here if I'm looking at last names

and let's say I want everybody whose

last name starts with s you can't really

do that with anything else so I'm going

to say where it's like and then I'm

going to say s and after that I'm going

to put a percent sign that's actually

called a wild card and if I close that

off what this is saying is is I want

every last name where it starts with

where it's like

where it only starts with an S so let's

run this really quick now we have two

people whose last names start with s now

if I put a wild card at the beginning we

are now saying where there's an S

anywhere in anybody's name so let's

execute this and see what we get so now

even if the S is like flenderson towards

the end it's still counts so you can

specify multiple things in here as well

so let's say I want it to start with s

that would return shre and Scott but now

I want something that also has an o in

it so so it has an S at the beginning

and then somewhere in there there's an O

now let's execute that and there's only

one person that meets that criteria so

you can do that for multiple things you

can even say OT

TT and let's execute that and he's still

going to be returned and if we put C at

the back it's not going to be returned

because it follows it in order so isn't

s o TT C the C would actually need to

go over here so now we have s c o t t

and although there's a bunch of wild

cards in here it is going to return

Scott so that is a little bit a little

hint at how you can use like there is a

little bit more that goes into it you

can use it for numerics um there's a lot

of things that you can use this for but

this is just the basics how you can use

it today how you get started on using

the like a nutshell that is how you use

like and as I said before you can use

like with numerical data as well but for

demonstration purposes I wanted to use

text Data let's get rid of this really

quick um let's look at our entire

table and I wanted to show you how to

use null and not null I can't really

show you how to use null because I do

not have any null Fields I could easily

update this table and make n but that's

in a future video where it's a little

bit more advanced where you can start

altering your data but just for purposes

of showing you what null and not null is

let's do where first

name is

null and if we see that is not going to

return anything but if we say is not

null it's going to return everything

because nothing in here is null nothing

in this first name column is null so

that's how you use it um there are a lot

of use cases where you actually will use

null and not null that will be in future

videos probably in the project section

or the portfolio section we weren't able

to show really how to use this super

well but just as a demonstration that's

really all it does it looks at the whole

column and whether it is null or not

null that's really all it's used for

this is actually super useful and you

can use it in a ton of situations but

again for demonstration purposes that's

really all it does so let's get rid of

this let's look at in really quick so in

is kind of like the equal statement but

it's multiple equal statements so let's

say we want to say we first name equals

gy and then we were like wait we also

want to include Michael

Scott so then we would have to write and

where first name equals and then we

would do Michael and then etc etc for

anybody that we wanted to include but if

we said in we could do an open

parentheses and then we can say

gy we can say

Michael and we can say as many people as

we want going down the road just

separating it by commas and if we had

execute everything would be returned so

it really is just a condensed way to say

equal for multiple

things so that is the we statement I

think the wear statement can get

extremely complex but this really is

highlighting the basics so if you can

learn all of these Concepts you will

absolutely have the basics down and will

be set to go over some more intermediate

and more advanced things with the we

statement later on in the next video

we're going to be going over the group

buy and the order buy and then we are

done with the SQL Basics and then you

can practice and work your way up into

my intermediate level videos which are

going to be coming out very shortly

after these videos thank you guys so

much for joining me if you like this

tutorial Series be sure to subscribe

below and I'll see you in the next

video going on everybody my name is Alex

freeberg and in today's video we're

going to be going over the group by and

the order by statements in previous

videos we created tables we went over to

select the from and the where and now we

are at the very end of our SQL basic

series if you stayed with us for the

whole time hopefully you have learned a

lot and learned the basics of SQL in

future videos we're going to be going

over intermediate and even more advanced

concepts and even going through

portfolio projects that you can use to

put on your resume if you like this type

of content be sure to subscribe below

but let's get into it for today the

group by statement is similar to

distinct in the select statement in that

it's going to show the unique values in

a column the difference is is if we say distinct

distinct

gender what's going to be returned is

the very first unique value of female

and the very first unique value of

male but if we say

gender and we say Group by

gender it's only going to return two

values but in these two values we

actually have all the males rolled up

into this one row and all the females

rolled up into this one row now let me

further show you what that means if I

say count of

gender now you can see that this whole

time there were six males in this one

row and there were three females in this

one row so with a distinct it really is

only showing us what value is in there

that's unique but with the group by it's

showing us what the unique value is but

it's also rolling them all up into one

column that we can use it for other

things now real quick I want to be able

to see both of these at the same time so

let's just put this up here and let's

run this so we can actually see both now

let's add age to this statement down

here or this

query and let's only run this one and I

want to show you what happens and why it

happens we're now looking at gender age

and then the count of gender so if we

look down here we only have one male who

is 29 we have one male who is female

that's age 30 and so on and so forth so

none of these people are both the same

gender and the same age if for example

we had two or three people who were male

and who were 30 years old then we would

have a two or a three over here so this

count is actually being counted at each

row that's being returned so for our

data that we have today this isn't a

fantastic example CU it really split it

out there any that were the same but as

you can see you can put multiple columns

as long as you put multiple down here

now why did we not have to put this

count gender down here in this group by

that's because this count gender is

actually a derived field or derived

column it's derived based off the gender

column so it's technically not a real

column that's in the table it's one that

we're creating that's fictional uh per

se so the age and the gender are actual

fields or actual columns that are in our

table they have to be down here and like

I said before it's the comparison to

that distinct in the select statement

because we're looking at the distinct of

gender and age so we're saying distinct

across multiple columns both gender and

age now as we had it before we were only

looking at gender it's going to roll all

of those up into just male and female

but if we want to add more we can easily

add more in this group by statement we

can still do things like where age is

greater than

31 we can still do those things so let's

execute this and our numbers are going

to change now we're doing it based off

gender and we're looking at the count of

people whose age is greater than 31

which is smaller than before now let's

look at order bu I'll do it down here

really quick for demonstration but I am

eventually going to come up here and use

it because I think it'll be a little bit

better to completely round out this

query down here let me give this a name

let's do count of gender and then let's

come down here and let's order

by uh let's order by count

gender and when we run that it's going

to do 1 three and that's because as a

default SQL has an ascending feature

which is going to be smallest to largest

going down if we want to change that we

can change it to descending that's going

to be largest to smallest so now we have

31 and if we want to do it based off

gender and we do it descending now we

have Z to

A and so that's going to be male female

and if we get rid of that it's going to

do the the default

ascending and let's see what that brings

female male now for what we're trying to

do let's look at this large table so I

think it's going to be a little bit more

descriptive or a little bit better

visually let's do order by and let's do

age let's run this and it's going to

order smallest to

largest if we do

descending it's going to do largest to

smallest now you don't only have to do

just one thing you can do multiple

columns so if I wanted to do age and

then gender I can do that as well so

let's do

gender and let's run that so now we have

the age but under the age we also have

it ordered by female and that's an

ascending order so AB BC d f so females

first so it's going to be female first

and then it's going to be male and again

female and male now we don't have to

just let it be ascending for each one if

I wanted to do it reverse in this column

I can do descending now let's run that

and when we have 30 now male is first

and female second and if I wanted to do

that over here I can do descending and

now we have them both descending so it's

going to go top to bottom and we have 32

it's going to be male 32 female so you

can specify lots of different things in

here and we don't actually have to use

column names we could just use numbers

so if I wanted to do 1 2 3 4 5 I could

but let's try to replicate the exact

same thing before this would be column 1

2 three four so let's do where four

descending and then let's do

five descending and if we execute that

it's going to give us the exact same

result as if we' actually put in the

column name and I I do use this a lot

oftentimes I don't use the column name I

just if it's a small table I'll just use

the number so in my actual queries I do

this a lot where I just use the number

instead of the column name so that is

the group buy and the order by statement

and if you have walked through my

previous videos you should be completely

done with the basics of SQL so

congratulations the next thing to do is

really just practice the basics because

the basics are what you're going to be

using day in day out and so what I would

recommend is create a few more tables

query those tables try to think of use

cases and what you would actually want

to know from that information after that

I would move on to my intermediate

videos if those are already out and then

I would move on to my Advanced videos

those are going to go over some more

challenging topics but things that would

be very useful for anybody to know in my

next video I'm going to be going over

intermediate SQL topics things like

joins and subqueries and a ton more so

if I already have posted those be sure

to go check those out on my page and if

I haven't I hope to have those up soon

thank you thank you guys so much for

watching I really appreciate it if you

learned anything in this basics of

sequel Series be sure to subscribe below

and I'll see you in the next video

what's going on everybody my name is

Alex freeberg and today we're going to

be starting our intermediate SQL series

if you joined us for our last series we

walked through the basics of SQL which

is everything you needed just to get

started and in this series we're going

to be walking through some intermediate

Concepts to really take your skills up

to the next level now today we're going

to be walking through joins but let me

show you what you can expect from the

entire series for this intermediate course

course

so we're going walking through joins

today and then in future videos we're

walking through unions case statements

updating and deleting data Partition by

data types aliasing creating views

having versus the group by statement the

get date function primary care of your

foreign key and then we're going to have

an advanced course and this is not set

in stone yet but these are some of the

things that I think I will be going

through or walking through we're going

through CTE CIS tables or system tables

subqueries temp tables string functions

regular expression store procedures and

then importing and exporting data so

with all that being said let's get into

it all right now let's get rid of me

because we do not need to be seeing me

for the rest of the series at the very

top here are some of the things that

we're going to be going through today

which are inner joins and then outer

joins and in the outer joins we have a

few different styles or a few different

types of outer joins now a join is a way

to combine multiple tables

into a single

output for now we're going to be using

the employee demographics and the

employee salary table so let's get a

look at both of these tables and see

what's in them in our employee

demographics table we have employee ID

first name last name age and gender and

then down here in our employee salary

table we have employee ID job title and

salary if you notice they have a similar

column and that's going to be the

employee ID now when you're doing a join

you have to do this based off a similar

column and typically you want it to be a

unique field so we're going to be using

the employee ID from both tables to join

these tables together to create one

output so let's get rid of this real

quick and let's start building our query

to join these two tables

together so the first thing we're going

to do is an inner join so let's do select

select

everything and let's do it from SQL tutorial.

tutorial.

db. employee

demographics and let's do join we can

also say inner join but join by default

is going to say

iner and we're going to do SQL tutorial.

db. employee

salary now we have to join them together

which is what we talked about earlier

and we're going to be doing that based

off the employee ID so for that we have

to say on and then we're going to

say employee

demographics dot employee ID is equal to employee

employee

salary dot employee ID so let's run this

real quick and take a look at the

output and let me pull this up real quick

quick

so what we are looking at is actually

both tables combined we have the

employee ID first name last name age

gender and then here's the salary

employee ID job title salary now an in

join is really only going to show

everything that is the same so in both

tables there are employee IDs of

10001 all the way down to

10009 but if you notice there is data

that is missing real quick let's go down

to this graphic and let's look at this

inner join an inner join is going to

show everything that is common or

overlapping between table a and table B

so what we are looking at here is

exactly that we're only looking at the

things that are similar based off this

employee ID in both tables now let's

change this join to a full outer

join and let's run this and see what we

get now if you notice the output is very

different so let's take a look at it and

see why it's so different if you notice

everything down till here is the exact

same so employees 101 down to 1009 are

exactly the same but once we get down to

row 10 it starts to get very different

now we are joining these tables based

off the employee ID so for example right

here Ryan Howard has an employee ID of

101 but as you can see in this table for

salaries there is no 101 employee ID so

it has nothing to link it to so because

of that it fills in everything as null

because it has nothing to match on this

table and vice versa in the employee

salary table there's a person in here

that's a Salesman and there's no

employee ID at all which means all this

information is going to be null and we

can see that in this diagram right here

so this is the full outer join right

here and what it is saying is we are

going to show everything from table a

and table B regardless of if it has a

match based on what we were joining them

on so even if table a has an employee ID

but there's no employee ID in table B

we're still going to show it and vice

versa so now let's look at a left outer

join a left outer join is going to take

the left table and say we want

everything from the left table and

everything that's overlook lapping but

if it's only in the right table we do

not want it now what is the left and the

right table the left table is going to

be our first table that we use our right

table is going to be the second table

that we use so we're going to look at

everything in the employee demographics

table regardless of whether or not it

has a match on the employee ID in the

employee salary table so this is what

that looks like so as you can see this

is our entire table for employee

demographics and down here we have three

that have information in the employee

demographics table but have absolutely

no information in any of the employee

salary table because there's nothing to

match it on so this 101 is not in this

table this 13 is not in this table and

this one does not even have an employee

ID so we're not going to have a match at

all and if we change that to the

right you'll see the exact opposite it's

going to show us everything in the

employee salary table so now we have all

of our information right here from the

employee salary table and if it doesn't

match in this table it's just going to

give nulls so down here we have 1,0 and

obviously there's not going to be

anything associated with that because

there's no 10,0 in the employee

demographics table and for this one we

have a Salesman with no employee ID and

since there's no employee ID to tie it

to this demographics table we're going

to have nothing and we can see that in

the diagram right here so for the left

outer join we're looking at everything

in table a which is our demographics

table and in our right outer join

looking at everything at table B which

is our salary table now let's pull this

down a little bit so so far we've only

been using the select star so we've been

selecting everything and I only did that

just for demonstration purposes but you

most likely would not be doing this when

you actually use these joins what you're

probably going to want to do is Select

exactly what columns you want in your

output so for example let's do employee

ID let's do first

name last

name and let's do job

title and let's do

salary and let's try to run that really

quick and as you can see it is not going

to work now why is that not working it's

not working because we have two Fields

one in each of these tables and we have

to specify what employee ID we want

because that is going to drastically

change what our output is so we have an

employee ID in this table and in this

table which one do we want to use so for

this demonstration let's use employeed

demographics. employee ID and let's

actually just do an inner join because

it's easier for the

output now let's run this and see what

we get so as you can see we now have the

employee ID first name last name job

title and salary now we're doing this

with an injoin based off the employee ID

from the employee demographics table but

if we use the employee salary table it

should give us the exact same output and

that's cuz we're using an in join and an

in joint is only going to show us

everything that overlaps between both

tables but now let's try a write outer

join and let's run this now we're using

this employee ID from our employee

salary table and since we're doing a

write outer join we're going to get all

the information from our employee salary

table and it does not have to be in our

left table which is our employee

demographics table so if you look at the

information down here this 110 is in the

employee salary table but it's in this

position because that's what we're

looking at in our select statement and

then over here we have our salary and

since we have information right here

which is in our employee salary table

but there is no employee employe ID our

employee ID is null now let's change

this to look at the employee

demographics employee ID and execute it

as you can see that 110 is gone now we

just have this information right down

here and we didn't have the employee ID

for either of these so it's going to

show it regardless and that's again

because we have a right outer join and

that's why we have no employee ID down

here now let's do a left

join and it's basically going to do the

opposite of what we just looked at now

we're looking at everything from our

left table regardless of if it's in our

right table and so our left table is our

employee demographics table and we are

looking at our employee demographics ID

so with the employee demographics ID

it's going to show us the first name and

the last name which is everything in our

left table our employee demographics

table and since for these IDs or lack of

IDs it's just going to give us NES in

all of these places if I change it right

up here to the employee salary employee

ID and I execute it because we're

showing everything from our left table

which is our employee demographics table

we are still going to see our names but

since we're using the employee ID from

our right table now we're just going to

have blanks in this information and this

information now let's look at a use case

for these joins let's say Robert

California is pressuring Michael Scott

to meet his quarterly quota and Michael

Scott is almost there he needs like a

thousand more dollars and he comes up

with the genius idea to deduct pay from

the highest paid employee at his Branch

besides himself so how does he go about

doing this and identifying the person

that makes the most money well of course

he's going to come to SQL first so we

actually want to look at a

full outer join real quick

and let's just look at

everything so here's what we have we

have the employee ID first name last

name age gender employee ID job title

and salary now what information do we

need to know to get the information that

Michael Scott needs well we need the

employee ID we want the first name and

last name so let's write all that real

quick so employee ID we need first name

name we

need last name and then we're also going

to need the

salary cuz we need to know who is the

highest paid

employee so now let's do an injin

because we really only want to look at

the employee IDs where we know what

their name is and their salary is and

let's do this based off the employee

demographics table really doesn't matter

for an in join but let's do that real

quick so let's look at this so we have

our employee ID we have our first name

our last name and our salary and we want

to do it where it's not Michael Scott and that's because Michael doesn't want

and that's because Michael doesn't want to take away his own money he wants to

to take away his own money he wants to take away his employees money so let's

take away his employees money so let's do

do where first name does not equal Michael

where first name does not equal Michael and he knows that he's the only one that

and he knows that he's the only one that is not named Michael so now we have our

is not named Michael so now we have our list and let's do order

list and let's do order bu and let's do

bu and let's do salary and let's execute

salary and let's execute this and let's do descending so that we

this and let's do descending so that we can get at the very

can get at the very top and this is tough tough news for

top and this is tough tough news for Dwight shut because it looks like he is

Dwight shut because it looks like he is the highest paid employee besides

the highest paid employee besides Michael and so it looks like he is going

Michael and so it looks like he is going to get a cut in his pay this quarter so

to get a cut in his pay this quarter so that Michael can meet his quota so

that Michael can meet his quota so that's just one use case let's look at

that's just one use case let's look at one more use case let's start out by

one more use case let's start out by getting rid of this and looking at

getting rid of this and looking at everything

again so for our next use case Kevin Malone who is an accountant thinks that

Malone who is an accountant thinks that he may have made a mistake when looking

he may have made a mistake when looking at the average salary for our salesman

at the average salary for our salesman now Angela Martin is very good at SQL

now Angela Martin is very good at SQL and so what she is going to do is she

and so what she is going to do is she wants to go in and calculate the average

wants to go in and calculate the average salary for our salesman so let's try to

salary for our salesman so let's try to get that information so all we're going

get that information so all we're going to need is the job title and the salary

to need is the job title and the salary so let's come up here and let's get job

so let's come up here and let's get job title and let's get

title and let's get salary and let's look at

salary and let's look at this and now we only want to look at

this and now we only want to look at where the job title is equal to

salesman now the very last thing we want to do is we want to say we want the

to do is we want to say we want the average of salary now since we're going

average of salary now since we're going to need to do a group buy we're going to

to need to do a group buy we're going to have to get rid of this

have to get rid of this salary and just take job title write

salary and just take job title write down here and do group by job title so

down here and do group by job title so we're going to have job title and then

we're going to have job title and then the average

the average salary and there you go we have the

salary and there you go we have the salesman and the average salary is

salesman and the average salary is 52,000 so Angela now knows to go back

52,000 so Angela now knows to go back and fix what Kevin made a mistake on so

and fix what Kevin made a mistake on so that's how you use joins I will includ

that's how you use joins I will includ include this image in the description so

include this image in the description so you can go and look that up yourself if

you can go and look that up yourself if you are curious and want to look at that

you are curious and want to look at that that really helped me out when I was

that really helped me out when I was first getting started to kind of

first getting started to kind of conceptualize and understand what kind

conceptualize and understand what kind of data I was pulling based on what join

of data I was pulling based on what join I was using so I hope that was useful to

I was using so I hope that was useful to you as well in the very next video we're

you as well in the very next video we're going to be looking at the union so if

going to be looking at the union so if that is posted be sure to check that out

that is posted be sure to check that out next thank you guys so much for joining

next thank you guys so much for joining me I really appreciate it if you like

me I really appreciate it if you like this type of content or got anything out

this type of content or got anything out of it today be sure to smash the like

of it today be sure to smash the like button smash the Subscribe button and

button smash the Subscribe button and I'll see see in the next video what's

I'll see see in the next video what's going on everybody my name is Alex free

going on everybody my name is Alex free in today's video we're going to be

in today's video we're going to be looking at unions now in the very last

looking at unions now in the very last video we walked through joins and I

video we walked through joins and I thought it was appropriate to look at

thought it was appropriate to look at unions next because unions and joins are

unions next because unions and joins are somewhat similar or closely related and

somewhat similar or closely related and that's because in both instances they're

that's because in both instances they're combining two tables to create one

combining two tables to create one output now what's the difference the

output now what's the difference the difference is that a join combines both

difference is that a join combines both tables based off a common column and in

tables based off a common column and in last video that was the employee ID so

last video that was the employee ID so in both tables we had an employee ID and

in both tables we had an employee ID and when you're selecting your data you have

when you're selecting your data you have to choose either to only select one

to choose either to only select one employee ID or you can choose both

employee ID or you can choose both employee IDs but they're in separate

employee IDs but they're in separate columns and with a union you're actually

columns and with a union you're actually able to select all the data from both

able to select all the data from both tables and put it into one output where

tables and put it into one output where all the data is in each column and not

all the data is in each column and not separate it out and you don't have to

separate it out and you don't have to choose which table you're choosing it

choose which table you're choosing it from now that may not have made1 100%

from now that may not have made1 100% sense but let's look at it real quick in

sense but let's look at it real quick in stages so let's go down here and let's

stages so let's go down here and let's actually join this table

actually join this table together and see what we get now the two

together and see what we get now the two tables that we're looking at is employee

tables that we're looking at is employee demographics and warehouse employee

demographics and warehouse employee demographics so over here we have our

demographics so over here we have our employee demographics information and

employee demographics information and then over here or actually down here we

then over here or actually down here we have our warehouse employee demographics

have our warehouse employee demographics now right now I'm doing a full outer

now right now I'm doing a full outer join so we're looking at all the data

join so we're looking at all the data and if we were to pull this in to an

and if we were to pull this in to an Excel spreadsheet we could just copy

Excel spreadsheet we could just copy this and paste it over here and we would

this and paste it over here and we would be good to go and that's because we have

be good to go and that's because we have all the same columns first name last

all the same columns first name last name age gender first name last name age

name age gender first name last name age gender but if we tried to combine this

gender but if we tried to combine this in a query where we have this

in a query where we have this information right here it wouldn't work

information right here it wouldn't work we cannot get it in the same column and

we cannot get it in the same column and that's where a union comes into play so

that's where a union comes into play so let's go back up here and let's actually

let's go back up here and let's actually run both of

run both of these now as you can see they have the

these now as you can see they have the exact same columns and that makes it

exact same columns and that makes it super easy for what we're about to do

super easy for what we're about to do all we're going to do is between these

all we're going to do is between these two queries which are completely

two queries which are completely separate right now all we're going to do

separate right now all we're going to do is write

is write Union so let's run just

Union so let's run just this now because of the Union you can

this now because of the Union you can look down here and the information that

look down here and the information that used to be in the other table which were

used to be in the other table which were in separate columns are now added Down

in separate columns are now added Down Below in the exact same order now Daryl

Below in the exact same order now Daryl filin was actually in both tables and

filin was actually in both tables and the reason he isn't showing up multiple

the reason he isn't showing up multiple times is because this Union is actually

times is because this Union is actually taking out and removing the duplicates

taking out and removing the duplicates kind of like a distinct statement now

kind of like a distinct statement now there's actually another thing called

there's actually another thing called Union all and if we do Union all it is

Union all and if we do Union all it is going to show us all of the information

going to show us all of the information regardless if it is a duplicate or not

regardless if it is a duplicate or not so let's run that real quick and they

so let's run that real quick and they they are both there but let's order

they are both there but let's order by and let's do employee

by and let's do employee ID so now let's run it and as you can

ID so now let's run it and as you can see right here these are exact

see right here these are exact duplicates and so the union got rid of

duplicates and so the union got rid of it because they were the exact same but

it because they were the exact same but the union all kept it in because it is

the union all kept it in because it is showing just the data as is now let's

showing just the data as is now let's get rid of this Union all because the

get rid of this Union all because the only reason why it works so well is

only reason why it works so well is because those two tables were exact same

because those two tables were exact same they were employee ID first name last

they were employee ID first name last name age gender so they're basically the

name age gender so they're basically the same tables just with different

same tables just with different information so it made it really easy

information so it made it really easy but we have another table

but we have another table employee uh

employee uh salary and let's look at these two

salary and let's look at these two tables so these two tables are obviously

tables so these two tables are obviously very different they hold different

very different they hold different information now we would still be able

information now we would still be able to combine them so let's do employee

to combine them so let's do employee ID first name and let's do

ID first name and let's do age now down here on the employee salary

age now down here on the employee salary table we will do employee ID job title

table we will do employee ID job title and

and salary now let's use a union really

salary now let's use a union really quick and run this

quick and run this one and it is still going to work now

one and it is still going to work now why does this work well first off the

why does this work well first off the the reason it's working is because these

the reason it's working is because these data types are the exact same or at

data types are the exact same or at least similar so text and text age which

least similar so text and text age which is an integer salary which is an integer

is an integer salary which is an integer it has the same amount of columns so

it has the same amount of columns so three and three so we have employee ID

three and three so we have employee ID first name and age and it's taking that

first name and age and it's taking that from the first select statement and it's

from the first select statement and it's still using a union to take the data

still using a union to take the data from the second select statement so it's

from the second select statement so it's still inserting this information now

still inserting this information now this is not what you want to do because

this is not what you want to do because right here we have first name and it's

right here we have first name and it's salesman salesman and then our age we

salesman salesman and then our age we have 30 45,000 and 45,000 is obviously

have 30 45,000 and 45,000 is obviously not an age so you want to be careful

not an age so you want to be careful when you're using a union to combine two

when you're using a union to combine two separate tables and make sure that the

separate tables and make sure that the data you're selecting is the same in the

data you're selecting is the same in the very next video we're going to be

very next video we're going to be walking through case statements thank

walking through case statements thank you guys so much for joining me I really

you guys so much for joining me I really appreciate it if you like this type of

appreciate it if you like this type of content be sure to subscribe below and

content be sure to subscribe below and I'll see you in the next video what is

I'll see you in the next video what is going on everybody my name is Alex

going on everybody my name is Alex freeberg and today we're going to be

freeberg and today we're going to be walking through cas statements in SQL a

walking through cas statements in SQL a case statement allows you to specify a

case statement allows you to specify a condition and then it also allows you to

condition and then it also allows you to specify what you want returned when that

specify what you want returned when that condition is met so we're going to be

condition is met so we're going to be using this employee demographics table

using this employee demographics table that we're looking at right here we're

that we're looking at right here we're going to walk through the syntax of how

going to walk through the syntax of how to create a case statement and then

to create a case statement and then we're going to actually go into some use

we're going to actually go into some use cases at the end so let's start off by

cases at the end so let's start off by specifying what columns we want let's

specifying what columns we want let's say we want the first name we want the

say we want the first name we want the last name and we want want the age now

last name and we want want the age now let's just get that

let's just get that information now for our case statement

information now for our case statement we're going to be using this age column

we're going to be using this age column so we actually want the age to be in

so we actually want the age to be in there so let's

there so let's specify where age is not

specify where age is not null and run that so now we have a

null and run that so now we have a pretty good look at it and let's just

pretty good look at it and let's just order

order by H just to clean it up a little bit so

by H just to clean it up a little bit so now let's start building our case

now let's start building our case statement so we're going to say case and

statement so we're going to say case and then we want to say when now we need to

then we want to say when now we need to specify what condition we want to look

specify what condition we want to look for so let's do when age is greater than

for so let's do when age is greater than 30 then then what do we want to be

30 then then what do we want to be returned so we want to return that they

returned so we want to return that they are old else so that means anything that

are old else so that means anything that is not over the age of 30 we want to

is not over the age of 30 we want to return

return young and then you need to specify that

young and then you need to specify that you done with the case statement and so

you done with the case statement and so you will write end at the very bottom so

you will write end at the very bottom so this is our first case statement let's

this is our first case statement let's run it and see what we get so as you can

run it and see what we get so as you can see a new column was created and if the

see a new column was created and if the person is over the age of 30 so 31 and

person is over the age of 30 so 31 and up they are given old and if they're not

up they are given old and if they're not over the age of 30 they are given

over the age of 30 they are given young now we can do as many when and

young now we can do as many when and then statements as we want so if we want

then statements as we want so if we want to we can also do when the age is

to we can also do when the age is between 27 and 30

between 27 and 30 then we want to return

then we want to return young and anyone else we're going to

young and anyone else we're going to call a

call a baby so now we have Ryan Howard as the

baby so now we have Ryan Howard as the baby anyone between 27 and 30 they're

baby anyone between 27 and 30 they're considered young and anyone over the age

considered young and anyone over the age of 30 is old now something to note is

of 30 is old now something to note is that the very first condition that is

that the very first condition that is met is going to be returned so if there

met is going to be returned so if there are multiple conditions that meet the

are multiple conditions that meet the criteria only the very first one is

criteria only the very first one is going to be return returned and let's

going to be return returned and let's demonstrate that real quick so if the

demonstrate that real quick so if the age equals

age equals 38 then return Stanley because that is

38 then return Stanley because that is Stanley uh and let's execute this real

Stanley uh and let's execute this real quick so right here I'm specifying that

quick so right here I'm specifying that if it's 38 it should return Stanley but

if it's 38 it should return Stanley but he is right here and it still says old

he is right here and it still says old and that's because this condition was

and that's because this condition was already met now if we were to put this

already met now if we were to put this right

right here it should work correctly and let's

here it should work correctly and let's try it out so now because this condition

try it out so now because this condition is met first it is going to return

is met first it is going to return Stanley down here so now let's get into

Stanley down here so now let's get into our first use case let's start off by

our first use case let's start off by copying this and then commenting it out

copying this and then commenting it out I only did that because I don't want to

I only did that because I don't want to rewrite it because I'm

rewrite it because I'm lazy uh let's get rid of that and let's

lazy uh let's get rid of that and let's look at this real quick we are going to

look at this real quick we are going to join on another table that we have

join on another table that we have really fast um that's going to be SQL

really fast um that's going to be SQL tutorial if you watched my other videos

tutorial if you watched my other videos then you know this table and we're going

then you know this table and we're going to do that on employee

to do that on employee demographics. employee ID is equal to

demographics. employee ID is equal to employee

employee salary. employee ID okay so let's just

salary. employee ID okay so let's just look at everything in these tables

look at everything in these tables really quick now we are going to be

really quick now we are going to be focusing on the job title in the salary

focusing on the job title in the salary column but we want their first name and

column but we want their first name and last name as well so let's start

last name as well so let's start building that out

building that out let's do first

let's do first name last

name last name job title and salary and let's look

name job title and salary and let's look at this really quick so now we have our

at this really quick so now we have our employees and here is the situation we

employees and here is the situation we had a fantastic year this year selling

had a fantastic year this year selling paper and corporate has allowed Michael

paper and corporate has allowed Michael Scott to give out a yearly raise to

Scott to give out a yearly raise to every single employee but not every

every single employee but not every employee is going to get the same raise

employee is going to get the same raise because our salesmen are genuinely the

because our salesmen are genuinely the people who made us our money and they're

people who made us our money and they're going to get the biggest raises well

going to get the biggest raises well other people really aren't going to get

other people really aren't going to get that big of a raise so now let's go

that big of a raise so now let's go through and create a case statement to

through and create a case statement to calculate what their salary will be

calculate what their salary will be after they get their

after they get their raise so let's start off by saying

raise so let's start off by saying case and

case and when and we want it to say when job

when and we want it to say when job title is equal to

title is equal to salesman so when they are a Salesman

salesman so when they are a Salesman what do we want to happen so this is

what do we want to happen so this is where the calculation occurs so we're

where the calculation occurs so we're going to take their

going to take their salary and then we're going to add their

salary and then we're going to add their salary times how much their raise is

salary times how much their raise is going to be so the salesman did really

going to be so the salesman did really really well and we want to give them a

really well and we want to give them a 10% raise this year now when their job

10% raise this year now when their job title is equal

title is equal to

to accountant then and we'll take their

accountant then and we'll take their salary we will give

salary we will give them let's give them a 5% raise still

them let's give them a 5% raise still very

very generous there we we go and when the job

generous there we we go and when the job title is equal to

title is equal to HR then it's going to be the salary

HR then it's going to be the salary plus the

plus the salary times and then we're going to do

salary times and then we're going to do 01 all right and else we are just going

01 all right and else we are just going to

to do

do salary plus salary oops let's do

salary plus salary oops let's do parentheses times and let's just give

parentheses times and let's just give everyone else a 3% rays and then we'll

everyone else a 3% rays and then we'll write end now let's take a look at our

write end now let's take a look at our results so here's what we have so far we

results so here's what we have so far we have our first name our last name our

have our first name our last name our job title and our salary that is our

job title and our salary that is our current salary and then we're going to

current salary and then we're going to have our salary after we get our raise

have our salary after we get our raise so I'm going to actually write that up

so I'm going to actually write that up here so let's do as

here so let's do as salary a after

salary a after raise and let's execute

raise and let's execute that so let's look at these raises

that so let's look at these raises really quick so we have 45,000 and since

really quick so we have 45,000 and since he is a Salesman he gets a 10% raise

he is a Salesman he gets a 10% raise which is a raise of

which is a raise of $4,500 so 45,000 plus

$4,500 so 45,000 plus $4,500 is $49,500 and as you can see

$4,500 is $49,500 and as you can see down here we have HR who is making

down here we have HR who is making $50,000 and now he is making

$50,000 and now he is making $5,000 5 so everybody got a raise so

$5,000 5 so everybody got a raise so that is our case statement I hope that

that is our case statement I hope that was helpful I find myself using the case

was helpful I find myself using the case statement a lot when I'm wanting to

statement a lot when I'm wanting to categorize things or label things and

categorize things or label things and that's kind of what we did in the first

that's kind of what we did in the first example and you can even do calculations

example and you can even do calculations like we did in this use case so I hope

like we did in this use case so I hope that was helpful thank you guys so much

that was helpful thank you guys so much for watching I really appreciate it if

for watching I really appreciate it if you learned anything from this video be

you learned anything from this video be sure to like And subscribe below and

sure to like And subscribe below and I'll see you in the next video what is

I'll see you in the next video what is going on everybody my name is Alex fre

going on everybody my name is Alex fre and today we're going to be looking at

and today we're going to be looking at the having Clause now the having Clause

the having Clause now the having Clause I feels a little bit unappreciated in

I feels a little bit unappreciated in the SQL Community I feel like it doesn't

the SQL Community I feel like it doesn't get a lot of love and so today I want to

get a lot of love and so today I want to describe how to use it and what it's

describe how to use it and what it's used for so before we use the having

used for so before we use the having Clause I want to set up our query here

Clause I want to set up our query here uh we want to use an aggregate function

uh we want to use an aggregate function in the group by statement and then I

in the group by statement and then I will show you how to use this having

will show you how to use this having Clause so let's look at the job title

Clause so let's look at the job title and let's look at the count of job

and let's look at the count of job titles and then down here we need to do

titles and then down here we need to do group by job title

group by job title and let's execute

and let's execute this and here is our job titles and

this and here is our job titles and here's the count of how many people have

here's the count of how many people have those job titles so now let's say we

those job titles so now let's say we want to look at all the jobs that have

want to look at all the jobs that have more than one person in that specific

more than one person in that specific job so let's do where uh the

job so let's do where uh the count of job title is greater oops is

count of job title is greater oops is greater than one and let's run

greater than one and let's run that and as you can see we're going to

that and as you can see we're going to get this this message right here now

get this this message right here now let's read it an aggregate may not

let's read it an aggregate may not appear in the wear Clause unless it is

appear in the wear Clause unless it is in a subquery contained in a having

in a subquery contained in a having clause or a select list and the column

clause or a select list and the column being aggregated is an outer

being aggregated is an outer reference what that is basically saying

reference what that is basically saying is is we cannot use this aggregate

is is we cannot use this aggregate function in the wear statement we need

function in the wear statement we need to use a having Clause so let's get rid

to use a having Clause so let's get rid of this and let's say

of this and let's say having the count of job

having the count of job title greater than one I did the same

title greater than one I did the same thing again and let's execute this and

thing again and let's execute this and we're still going to get an error now

we're still going to get an error now why are we getting that error the reason

why are we getting that error the reason is is because this having statement is

is is because this having statement is completely dependent on the group by

completely dependent on the group by statement because we are performing this

statement because we are performing this after it has been aggregated so this

after it has been aggregated so this having statement actually needs to go

having statement actually needs to go after the group by statement because we

after the group by statement because we can't look at the aggregated information

can't look at the aggregated information before it's actually aggregated in that

before it's actually aggregated in that group by statement so now let's run this

group by statement so now let's run this and and it worked

and and it worked perfectly so now we only have the jobs

perfectly so now we only have the jobs that have more than one employee for

that have more than one employee for that job

that job title so now let's look at one more

title so now let's look at one more example let's do the average let's say

example let's do the average let's say salary and let's get rid of this having

salary and let's get rid of this having Clause real quick and just to look at

Clause real quick and just to look at this

this information uh let's do order by and

information uh let's do order by and we'll do average

we'll do average salary so let's look at this and we have

salary so let's look at this and we have 36,000 to 65,000 so in the middle we got

36,000 to 65,000 so in the middle we got 44,500 so let's use this having

44,500 so let's use this having statement and let's

statement and let's say the

say the average of

average of salary where it is greater than

salary where it is greater than 45,000 and we actually need to put this

45,000 and we actually need to put this right here right after the group buy and

right here right after the group buy and before the order buy so let's run this

before the order buy so let's run this and see what we get and it worked

and see what we get and it worked perfectly so now we're looking at the

perfectly so now we're looking at the job titles that have an average salary

job titles that have an average salary of over

of over $45,000 so there you go that is the

$45,000 so there you go that is the having Clause definitely one that is

having Clause definitely one that is good to know and is very useful in

good to know and is very useful in specific situations thank you guys so

specific situations thank you guys so much for watching I really appreciate it

much for watching I really appreciate it if you like this video or learned

if you like this video or learned anything today be sure to subscribe

anything today be sure to subscribe below and I'll see you in the next

below and I'll see you in the next video what is going on everybody my name

video what is going on everybody my name is Alex freeberg and today we're going

is Alex freeberg and today we're going to be looking at updating and deleting

to be looking at updating and deleting data in a table now what's the

data in a table now what's the difference between inserting data into a

difference between inserting data into a table and updating data insert into is

table and updating data insert into is going to create a new row in your table

going to create a new row in your table while updating is going to alter a

while updating is going to alter a pre-existing row while deleting is going

pre-existing row while deleting is going to specify what rows you want to remove

to specify what rows you want to remove from your table so let's get going with

from your table so let's get going with the updating so down here Holly flax

the updating so down here Holly flax does not have an employee ID age or

does not have an employee ID age or gender now we want to update this table

gender now we want to update this table to give her that information so let's do

to give her that information so let's do update now we need to specify what table

update now we need to specify what table we are going to be hitting off of so

we are going to be hitting off of so let's do SQL tutorial. db. employee

let's do SQL tutorial. db. employee demographics so now we're going to use

demographics so now we're going to use something called set and set is going to

something called set and set is going to specify what column and what value you

specify what column and what value you actually want to insert into that cell

actually want to insert into that cell so let's set

so let's set her employee ID equal to and it's going

her employee ID equal to and it's going to be

to be 1,2 and we have to specify which one to

1,2 and we have to specify which one to do this to because if we ran just this

do this to because if we ran just this is going to set every single employee ID

is going to set every single employee ID to 112 because we haven't specified that

to 112 because we haven't specified that we only want Holly flax's row to be

we only want Holly flax's row to be updated so now we have to specify

updated so now we have to specify where first

where first name is equal to

name is equal to Holly

Holly and last name is equal to flex so now

and last name is equal to flex so now let's run this and see what we

let's run this and see what we get so one row has been affected

get so one row has been affected let's see what we got and there we go as

let's see what we got and there we go as you can see the employee ID was updated

you can see the employee ID was updated exactly how we specified it right here

exactly how we specified it right here so we also want to update age and gender

so we also want to update age and gender and let's do that in the same

and let's do that in the same query so let's set the age equal to 31

query so let's set the age equal to 31 and instead of using and we actually

and instead of using and we actually need to use a comma so let's say age

need to use a comma so let's say age equal to 31 comma gender is going to be

equal to 31 comma gender is going to be equal to female and let's write

equal to female and let's write this and see what we get there you go

this and see what we get there you go now let's look at our

now let's look at our table and as you can see it was updated

table and as you can see it was updated to 31 and

to 31 and female so very easy very easy to specify

female so very easy very easy to specify what you want often times uh tables like

what you want often times uh tables like this will have a unique key like

this will have a unique key like employee ID is our unique key in this

employee ID is our unique key in this table so I could easily just say uh

table so I could easily just say uh where the employee ID is equal to and

where the employee ID is equal to and then you know

then you know 102 so it's an easy way way to specify

102 so it's an easy way way to specify what employee you're trying to update so

what employee you're trying to update so now let's look at the delete statement

now let's look at the delete statement the delete statement is going to remove

the delete statement is going to remove an entire row from our table so let's do

an entire row from our table so let's do delete and we actually need to say from

delete and we actually need to say from and we have to specify what table we

and we have to specify what table we want to be removing this information

want to be removing this information from so let's do SQL tutorial. db.

from so let's do SQL tutorial. db. employee

employee demographics and now we need to specify

demographics and now we need to specify what row we want to remove so let's do

what row we want to remove so let's do where employee ID is equal to and let's

where employee ID is equal to and let's choose a completely random employee ID

choose a completely random employee ID 105 so let's run this and see what

105 so let's run this and see what happens so one row is

happens so one row is affected let's look at our table and as

affected let's look at our table and as you can see 105 is now gone now you have

you can see 105 is now gone now you have to be very careful when you use the

to be very careful when you use the delete statement because once you run it

delete statement because once you run it you cannot get that data back there's no

you cannot get that data back there's no way to reverse a delete statement so if

way to reverse a delete statement so if I had gotten rid of this wear statement

I had gotten rid of this wear statement and I ran this it would delete

and I ran this it would delete everything from the entire table and you

everything from the entire table and you could not get that data back so a little

could not get that data back so a little trick that I use before I actually run a

trick that I use before I actually run a delete statement is I make it a select

delete statement is I make it a select statement because you're going to select

statement because you're going to select everything where the employee ID is

everything where the employee ID is equal to let's just do

equal to let's just do 1,4 and now when you run this you are

1,4 and now when you run this you are going to see exactly what you will be

going to see exactly what you will be deleting and now we know that Angela

deleting and now we know that Angela Martin that entire row is going to be

Martin that entire row is going to be gone if I hadn't done that and I just

gone if I hadn't done that and I just went like this and I wrote delete and I

went like this and I wrote delete and I only had this running I would not know

only had this running I would not know that this information is going to be the

that this information is going to be the only one that's gone maybe I made a

only one that's gone maybe I made a mistake down here maybe I accidentally

mistake down here maybe I accidentally put something in there that wasn't

put something in there that wasn't supposed to be in there and now I'm

supposed to be in there and now I'm deleting much more than I thought I was

deleting much more than I thought I was actually going to

actually going to delete so using the select statement can

delete so using the select statement can be a very good Safeguard against

be a very good Safeguard against accidentally deleting data that you do

accidentally deleting data that you do not want to delete so that is update and

not want to delete so that is update and delete thank you guys so much for

delete thank you guys so much for watching I really appreciate it if you

watching I really appreciate it if you like this video be sure to subscribe

like this video be sure to subscribe below and I'll see you in the next video

below and I'll see you in the next video What's going going on everybody my name

What's going going on everybody my name is Alex free and today we're going to be

is Alex free and today we're going to be talking about aliasing now all aliasing

talking about aliasing now all aliasing really is is temporarily changing the

really is is temporarily changing the column name or the table name in your

column name or the table name in your script and it's not really going to

script and it's not really going to impact your output at all aliasing is

impact your output at all aliasing is really used for the readability of your

really used for the readability of your script so that if you hand this off to

script so that if you hand this off to somebody or somebody comes behind you

somebody or somebody comes behind you and starts working on this they can more

and starts working on this they can more easily understand it and it may not

easily understand it and it may not sound super useful especially for small

sound super useful especially for small scripts like what we have on the screen

scripts like what we have on the screen but when you start getting to larger

but when you start getting to larger scripts where you have six seven or

scripts where you have six seven or eight joins and you're selecting 10

eight joins and you're selecting 10 different column names it actually is

different column names it actually is very useful and very important so let's

very useful and very important so let's get into how that actually works and

get into how that actually works and then I'll have an example later of how

then I'll have an example later of how we can use aling with a little bit of a

we can use aling with a little bit of a larger query so in this table let's

larger query so in this table let's select first

select first name and

name and execute what we want to do is just write

execute what we want to do is just write as and let's do FN name and all that's

as and let's do FN name and all that's going to do is it's going to rename this

going to do is it's going to rename this column from first name which it was

column from first name which it was originally named to FN name now you can

originally named to FN name now you can can use as but you can also just get rid

can use as but you can also just get rid of that and do it exactly how I have it

of that and do it exactly how I have it and it's still going to work perfectly

and it's still going to work perfectly you can either use the as or you can not

you can either use the as or you can not use it I typically don't I just put a

use it I typically don't I just put a space in between the actual column and

space in between the actual column and the Alias now let's look at an example

the Alias now let's look at an example of how this might actually be useful so

of how this might actually be useful so we have a first name and a last name in

we have a first name and a last name in this column so what we're going to do is

this column so what we're going to do is actually combine those so let's do plus

actually combine those so let's do plus and let's add a space in there and let's

and let's add a space in there and let's do a plus and let's do last name so this

do a plus and let's do last name so this is going to take the first name add a

is going to take the first name add a space and then do the last name and

space and then do the last name and we're going to do that as and let's do

we're going to do that as and let's do full

full name and let's execute

name and let's execute this so now we have a column called full

this so now we have a column called full name which is our Alias so we've

name which is our Alias so we've combined the first name and the last

combined the first name and the last name column into one single column and

name column into one single column and we've renamed it full name if we had not

we've renamed it full name if we had not used this Alias at all it would have

used this Alias at all it would have just said this which is no column name

just said this which is no column name at all we don't typically want that when

at all we don't typically want that when we have an output we want to give this

we have an output we want to give this column a name so that somebody who's

column a name so that somebody who's actually looking at the script or who's

actually looking at the script or who's looking at the output of the script

looking at the output of the script actually understand what is contained

actually understand what is contained within this column so for that we're

within this column so for that we're just going to keep it as full name now

just going to keep it as full name now another time that you're often going to

another time that you're often going to use aliasing in the select statement is

use aliasing in the select statement is when you're using aggregate functions so

when you're using aggregate functions so in this table we have age so let's pull

in this table we have age so let's pull that up really

that up really quick so we have age right here and

quick so we have age right here and let's actually just do the average

let's actually just do the average age and when we execute this we're going

age and when we execute this we're going to get no column name and 31 so we want

to get no column name and 31 so we want to do

to do is give it average

is give it average age and when we do that we now have a

age and when we do that we now have a column name and again you want to have a

column name and again you want to have a column name in case someone comes up

column name in case someone comes up behind you and is reading the script so

behind you and is reading the script so that they understand what this column is

that they understand what this column is being used for now that we've looked at

being used for now that we've looked at aliasing column names let's look at

aliasing column names let's look at aliasing table names it basically is the

aliasing table names it basically is the exact same thing uh we're just going to

exact same thing uh we're just going to write as and let's do demo for

write as and let's do demo for demographics and let's do demo Dot and

demographics and let's do demo Dot and it's going to give us all of our options

it's going to give us all of our options and we'll do employee

and we'll do employee ID so when you alias in a table name

ID so when you alias in a table name when you are selecting in the select

when you are selecting in the select statement you actually need to preface

statement you actually need to preface your column name with a table name or

your column name with a table name or the table Alias Dot and then employee ID

the table Alias Dot and then employee ID and this is extremely important to do

and this is extremely important to do especially when you have a lot of joins

especially when you have a lot of joins that you're doing or you're selecting a

that you're doing or you're selecting a lot of columns when you have several

lot of columns when you have several joins because it can get very very messy

joins because it can get very very messy quick so let's actually join this to

quick so let's actually join this to employees

employees salary and let's do that

salary and let's do that on

on demo. employee ID is equal

to s. employee ID so now let's do demo.

s. employee ID so now let's do demo. employee ID comma s do and let's do

employee ID comma s do and let's do salary so looking at the script now is

salary so looking at the script now is very clean it is very easy to understand

very clean it is very easy to understand and that is what's so important with

and that is what's so important with aliasing if for for example we took this

aliasing if for for example we took this off every time we wanted to reference

off every time we wanted to reference this table we would have to put the

this table we would have to put the entire table name and putting the entire

entire table name and putting the entire table name is correct it just is very

table name is correct it just is very cumbersome and does not look clean at

cumbersome and does not look clean at all and so using something like demo as

all and so using something like demo as an alias makes it a lot more easily

an alias makes it a lot more easily readable and a lot more manageable when

readable and a lot more manageable when you're looking at it when you have a

you're looking at it when you have a very long script let's look at this

very long script let's look at this queer where we're joining together three

queer where we're joining together three Separate Tables and after each table we

Separate Tables and after each table we have an alias for employee demographics

have an alias for employee demographics we have a employee salary we have B and

we have a employee salary we have B and warehouse employee demographics we have

warehouse employee demographics we have C now unfortunately I have seen a lot of

C now unfortunately I have seen a lot of scripts that look exactly like this and

scripts that look exactly like this and this is what you do not want to do you

this is what you do not want to do you do not want to use your aliasing to just

do not want to use your aliasing to just write an a a b or a c that is very

write an a a b or a c that is very frowned upon when writing queries

frowned upon when writing queries because it really doesn't give any

because it really doesn't give any context to what the table that you're

context to what the table that you're referencing is and it gets really

referencing is and it gets really confusing as this query continues to

confusing as this query continues to grow and as you add more columns to your

grow and as you add more columns to your select statement it makes it more

select statement it makes it more difficult to understand where those

difficult to understand where those columns are coming from and so when I'm

columns are coming from and so when I'm reading that I say select a. employee ID

reading that I say select a. employee ID okay what's a a is employee demographics

okay what's a a is employee demographics so you really do not want to do that now

so you really do not want to do that now let's look at an example of what it

let's look at an example of what it should look like so for employee

should look like so for employee demographics instead of having an alias

demographics instead of having an alias of a a I used demo for demographics for

of a a I used demo for demographics for employee salary I used s and for

employee salary I used s and for warehouse employee demographics I used

warehouse employee demographics I used where now this is not perfect by any

where now this is not perfect by any means but in the select statement if

means but in the select statement if you're just glancing at it you can

you're just glancing at it you can easily understand which columns are

easily understand which columns are coming from which tables so when I look

coming from which tables so when I look at employee ID I know that's coming from

at employee ID I know that's coming from employee demographics CU I have demo as

employee demographics CU I have demo as the Alias so it's a lot easier to

the Alias so it's a lot easier to understand and when you hand this query

understand and when you hand this query off to somebody it is going to be a lot

off to somebody it is going to be a lot easier for them to read through it and

easier for them to read through it and understand where those columns and those

understand where those columns and those table names are coming from and so they

table names are coming from and so they will appreciate that in the long run so

will appreciate that in the long run so that is all I got that is aling again

that is all I got that is aling again not a super tough subject but a really

not a super tough subject but a really important one to understand especially

important one to understand especially as you start working in teams and as you

as you start working in teams and as you start creating more and more complex

start creating more and more complex queries you want to have it more

queries you want to have it more organized and more easily readable and

organized and more easily readable and so it may not come into play with those

so it may not come into play with those really simple queries but again as as

really simple queries but again as as you build out those more complex queries

you build out those more complex queries this becomes very useful I really hope

this becomes very useful I really hope you enjoyed this video if you did be

you enjoyed this video if you did be sure to comment and subscribe below

sure to comment and subscribe below thank you so much for watching and I'll

thank you so much for watching and I'll see you in the next video what's going

see you in the next video what's going on everybody welcome back to another

on everybody welcome back to another intermediate SQL tutorial today we're

intermediate SQL tutorial today we're going to be covering Partition by now

going to be covering Partition by now Partition by is often compared to the

Partition by is often compared to the group by statement the group by

group by statement the group by statement is a little bit different the

statement is a little bit different the group by statement is going to reduce

group by statement is going to reduce the number of rows in our output by

the number of rows in our output by actually rolling them up and then

actually rolling them up and then calculating the sums or averages for

calculating the sums or averages for each group whereas Partition by actually

each group whereas Partition by actually divides the result set into partitions

divides the result set into partitions and changes how the window function is

and changes how the window function is calculated and so the Partition by

calculated and so the Partition by doesn't actually reduce the number of

doesn't actually reduce the number of rows returned in our output let's get

rows returned in our output let's get started to look at the actual syntax of

started to look at the actual syntax of how to use Partition by and then we'll

how to use Partition by and then we'll compare it to the group ey statement

compare it to the group ey statement later just to see the differences

later just to see the differences between the two we're going to be using

between the two we're going to be using these two tables on our left over here

these two tables on our left over here so I'm going to pull those up really

so I'm going to pull those up really quick so let's run this and let's look

quick so let's run this and let's look at the two these two tables Side by well

at the two these two tables Side by well one underneath the other really quick so

one underneath the other really quick so what we're going to be using to

what we're going to be using to demonstrate these partitioned by is this

demonstrate these partitioned by is this gender column as well as this salary

gender column as well as this salary column and so we just need to join these

column and so we just need to join these two tables together on the employee ID

two tables together on the employee ID and then we'll go from there now I'm not

and then we'll go from there now I'm not going to bore you with that I'm going to

going to bore you with that I'm going to skip ahead and we'll actually look at

skip ahead and we'll actually look at how to use this partition bu so I've

how to use this partition bu so I've joined these two tables together and

joined these two tables together and this is our output but we don't want

this is our output but we don't want every single column I'm going to start

every single column I'm going to start selecting some of these columns and then

selecting some of these columns and then we'll start using this partition Buy and

we'll start using this partition Buy and see what the output looks like after

see what the output looks like after that all right so let's go right up here

that all right so let's go right up here let's choose the first name let's do the

let's choose the first name let's do the last name we'll do

last name we'll do gender and let's do salary and now we

gender and let's do salary and now we want to identify how many male and

want to identify how many male and female employees we actually have and so

female employees we actually have and so we're going to say count of

we're going to say count of gender and this going to be

gender and this going to be over and now we're going to do our

over and now we're going to do our Partition

Partition by and we're also going to partition

by and we're also going to partition that by the

that by the gender as total gender now I'm going to

gender as total gender now I'm going to come back to why we did each part but I

come back to why we did each part but I want to see the output first and then we

want to see the output first and then we come back to why we wrote it this way so

come back to why we wrote it this way so let's just do this really

let's just do this really quick so it's going to be a little bit

quick so it's going to be a little bit different than what you typically would

different than what you typically would expect in a group by statement the group

expect in a group by statement the group by is going to roll everything up and

by is going to roll everything up and you typically wouldn't have like a first

you typically wouldn't have like a first name last name in a group by statement

name last name in a group by statement because it would be very hard to roll

because it would be very hard to roll all those things up into those

all those things up into those individual columns and to reduce the

individual columns and to reduce the number of columns that are in your

number of columns that are in your output and so in our output we can see

output and so in our output we can see Pam Beasley she's a female she makes

Pam Beasley she's a female she makes $36,000 as a salary and there are three

$36,000 as a salary and there are three total women that work alongside her in

total women that work alongside her in this employee demographics table and so

this employee demographics table and so in our total gender column over here

in our total gender column over here this is where we use the partition bu

this is where we use the partition bu and if we used a group bu statement to

and if we used a group bu statement to get this kind of information all we

get this kind of information all we would be able to do to get this

would be able to do to get this information in a group by statement is

information in a group by statement is say select gender count of gender and

say select gender count of gender and then Group by the gender down below

then Group by the gender down below underneath the join so because we're

underneath the join so because we're using the partition bu we're able to

using the partition bu we're able to isolate just one column that we want to

isolate just one column that we want to perform our aggregate function on and so

perform our aggregate function on and so we're able to add things like the first

we're able to add things like the first name and last name columns even though

name and last name columns even though we aren't trying to include that in any

we aren't trying to include that in any partition or group by statement yet

partition or group by statement yet we're still able to add the aggregate

we're still able to add the aggregate function to each individual row while

function to each individual row while still maintaining those other columns

still maintaining those other columns let's take this entire query and let's

let's take this entire query and let's basically just transform it into a group

basically just transform it into a group by statement and we'll see kind of what

by statement and we'll see kind of what that looks like and what the difference

that looks like and what the difference is so all I'm going to do is get rid of

is so all I'm going to do is get rid of all this I'm going

all this I'm going to copy all of

to copy all of this and I'm going to say

this and I'm going to say Group

Group by and I'm going to do that because we

by and I'm going to do that because we have to use all these columns in our

have to use all these columns in our group by statement so let's execute this

group by statement so let's execute this and as you can tell we are not able to

and as you can tell we are not able to see the output for the aggregate

see the output for the aggregate function that we were hoping for if we

function that we were hoping for if we wanted to get the same output that we

wanted to get the same output that we had before where we're showing three for

had before where we're showing three for females and six for males what we'd have

females and six for males what we'd have to do is get rid of this first and last

to do is get rid of this first and last name and the

name and the salary and do the same thing in the

salary and do the same thing in the group by

group by statement and so let me get rid of these

statement and so let me get rid of these really

really quick and run this and so what the

quick and run this and so what the Partition by is doing is basically

Partition by is doing is basically taking this query right here and

taking this query right here and sticking it on one line in the select

sticking it on one line in the select statement and so I hope now you can see

statement and so I hope now you can see how valuable the partition bu can be if

how valuable the partition bu can be if used correctly thank you guys so much

used correctly thank you guys so much for watching I really appreciate it if

for watching I really appreciate it if you like this video be sure to like And

you like this video be sure to like And subscribe below and I'll see you in the

subscribe below and I'll see you in the next video what's going on everybody

next video what's going on everybody welcome back to another squl tutorial

welcome back to another squl tutorial today we're going to be talking about

today we're going to be talking about CTE

CTE a CTE is a common table expression and

a CTE is a common table expression and it's a named temporary result set which

it's a named temporary result set which is used to manipulate the complex

is used to manipulate the complex subqueries data now this only exists

subqueries data now this only exists within the scope of the statement that

within the scope of the statement that we were about to write once we cancel

we were about to write once we cancel out of this query it's like it never

out of this query it's like it never existed a CTE is also only created in

existed a CTE is also only created in memory rather than a tempdb file like a

memory rather than a tempdb file like a temp table would be but in general a CTE

temp table would be but in general a CTE acts very much like a subquery and so if

acts very much like a subquery and so if you know how to do subqueries you should

you know how to do subqueries you should be able to pick up on CTE fairly easily

be able to pick up on CTE fairly easily so let's get started writing our very

so let's get started writing our very first C CTE and we're going to come down

first C CTE and we're going to come down here and we're going to say with and

here and we're going to say with and we're going to write

we're going to write CTE

CTE employee and we're going to say as and

employee and we're going to say as and this is where everything's going to

this is where everything's going to start now CTE are sometimes called with

start now CTE are sometimes called with queries I've never personally used that

queries I've never personally used that but I've seen it called that online but

but I've seen it called that online but that's because it uses this with

that's because it uses this with statement right at the very beginning so

statement right at the very beginning so now we have with CTE employee as then we

now we have with CTE employee as then we have an open parenthesis and now we have

have an open parenthesis and now we have to construct our select statement and

to construct our select statement and this is kind of where we build out our

this is kind of where we build out our quote unquote subquery and so I'm going

quote unquote subquery and so I'm going to take in a select statement that I

to take in a select statement that I actually used in a previous video where

actually used in a previous video where we using the partition bu and so I'm

we using the partition bu and so I'm going to put that in there and I'm kind

going to put that in there and I'm kind of walk us through what that does and

of walk us through what that does and how we're going to use this so I'm going

how we're going to use this so I'm going to paste this down right here and I'm

to paste this down right here and I'm actually going to go like this just to

actually going to go like this just to make it look a little nicer and then I'm

make it look a little nicer and then I'm going to close the parentheses at the

going to close the parentheses at the end so now we have our CTE in place and

end so now we have our CTE in place and as you can see it is basically just a

as you can see it is basically just a select statement within the with CTE

select statement within the with CTE employee as and what this is going to do

employee as and what this is going to do is going to take the first name last

is going to take the first name last last name gender and salary and then

last name gender and salary and then it's going to take this aggregate

it's going to take this aggregate function with the partition buy

function with the partition buy aggregate function with the partition

aggregate function with the partition buy and it's going to place it to where

buy and it's going to place it to where we can now query off of this data so

we can now query off of this data so it's putting it basically in a temporary

it's putting it basically in a temporary place where we can then go and grab that

place where we can then go and grab that data so all we're going to do at the

data so all we're going to do at the very bottom is we're going to say select

very bottom is we're going to say select everything and we can do that from CTE

everything and we can do that from CTE employee so let's run this entire thing

employee so let's run this entire thing and see what we

and see what we get so as you can see this select

get so as you can see this select everything from CTE employee we are

everything from CTE employee we are selecting everything from this select

selecting everything from this select statement and so this feels a lot like a

statement and so this feels a lot like a temp table we're actually quering off of

temp table we're actually quering off of a temp table but it actually acts a lot

a temp table but it actually acts a lot more like a subquery now we don't have

more like a subquery now we don't have to the select everything we can just do

to the select everything we can just do first name and let's do average

first name and let's do average salary and when we run this we'll just

salary and when we run this we'll just get those two columns and we don't have

get those two columns and we don't have to go through and actually write this

to go through and actually write this out each time it's just in this CTE for

out each time it's just in this CTE for us so it does all the heavy lift within

us so it does all the heavy lift within the CTE and then we can just query off

the CTE and then we can just query off of what we want now something to note is

of what we want now something to note is that the CTE is not stored anywhere and

that the CTE is not stored anywhere and so it's not stored in some temp database

so it's not stored in some temp database somewhere if I try to run just this by

somewhere if I try to run just this by itself it is not going to work so let's

itself it is not going to work so let's try that out really quick and we should

try that out really quick and we should get an error and that's because each

get an error and that's because each time we run this query is actually

time we run this query is actually creating the CTE again and so it's not

creating the CTE again and so it's not being saved anywhere and so each time we

being saved anywhere and so each time we run it we have to run it with the entire

run it we have to run it with the entire CTE another thing to note is you

CTE another thing to note is you actually have to put the select

actually have to put the select statement right after the CTE if I try

statement right after the CTE if I try to go down here and say select

to go down here and say select everything from uh let's do

everything from uh let's do CTE employees it doesn't actually work

CTE employees it doesn't actually work it's not going to come up at all and

it's not going to come up at all and that's because it only is going to work

that's because it only is going to work with the select statement directly after

with the select statement directly after the actual CTE that you've created I

the actual CTE that you've created I hope this was helpful and I hope that

hope this was helpful and I hope that you understand how to use a CTE a little

you understand how to use a CTE a little bit better again you don't have to go

bit better again you don't have to go super complicated with the select

super complicated with the select statement within your CTE it can be very

statement within your CTE it can be very very simple I just wanted to demonstrate

very simple I just wanted to demonstrate that you can use aggregate functions

that you can use aggregate functions within your CTE and then just query off

within your CTE and then just query off of those without having to do the the

of those without having to do the the aggregate function again which I find is

aggregate function again which I find is very very useful again thank you for

very very useful again thank you for watching if you like this video be sure

watching if you like this video be sure to like And subscribe below and I'll see

to like And subscribe below and I'll see you in the next video what's going on

you in the next video what's going on everybody welcome back to another squl

everybody welcome back to another squl tutorial today we are looking at temp

tutorial today we are looking at temp tables and if you can guess it based off

tables and if you can guess it based off of the name they're kind of like

of the name they're kind of like temporary tables and we create them very

temporary tables and we create them very much the same way we're going to do

much the same way we're going to do create table um it's just a little bit

create table um it's just a little bit different and you can hit off of this

different and you can hit off of this temp table multiple times which you

temp table multiple times which you cannot do with something like a CTE or a

cannot do with something like a CTE or a subquery where you can only use it one

subquery where you can only use it one time or with a subquery you need to

time or with a subquery you need to write it multiple times within a query

write it multiple times within a query and so these temp tables are extremely

and so these temp tables are extremely useful I'm going to kind of talk about

useful I'm going to kind of talk about how you can use them as we're going uh

how you can use them as we're going uh throughout this video but let's get

throughout this video but let's get started right away with actually

started right away with actually creating one looking at it inserting

creating one looking at it inserting some data and and and kind of showing

some data and and and kind of showing you how temp tables work and what we can

you how temp tables work and what we can do with them so uh we're going to start

do with them so uh we're going to start off with create table

off with create table much like uh a regular table is created

much like uh a regular table is created the only difference is we're going to do

the only difference is we're going to do this pound signed and then we're going

this pound signed and then we're going to do

to do tempcore

tempcore employee uh so literally the only

employee uh so literally the only difference between a regular table and a

difference between a regular table and a temp table is this right here at the

temp table is this right here at the very beginning this this pound sign so

very beginning this this pound sign so uh let's just start by doing employee ID

uh let's just start by doing employee ID we make that an integer we'll do job

we make that an integer we'll do job title

title and we'll make that a varar

and we'll make that a varar 100 and then we'll do

100 and then we'll do salary and let's make that an

salary and let's make that an integer and so now we have our temp

integer and so now we have our temp table uh let's go ahead and create

table uh let's go ahead and create it so now we have our temp table created

it so now we have our temp table created and so we can look at it really

and so we can look at it really quick so let's select

quick so let's select everything from and we'll do temp

everything from and we'll do temp employee so let's take a look it's

employee so let's take a look it's completely empty um and we can insert

completely empty um and we can insert data very much the same way we'd insert

data very much the same way we'd insert data into a regular table so let's start

data into a regular table so let's start doing that let's do insert

doing that let's do insert into and we'll do temp

into and we'll do temp employee and we'll do

employee and we'll do values and let's just do something

values and let's just do something really quick because I'm going to get to

really quick because I'm going to get to a little bit more interesting stuff in a

a little bit more interesting stuff in a second

oops so we'll make this person HR that's their job title then for

HR that's their job title then for salary we'll give them

salary we'll give them 45,000 and close it off so let's run

45,000 and close it off so let's run this and let's select everything again

this and let's select everything again and see what's in there perfect so we

and see what's in there perfect so we were able to insert data into this temp

were able to insert data into this temp table and again we we don't have to

table and again we we don't have to create this every single time we um um

create this every single time we um um or we don't have to run this every

or we don't have to run this every single time we need to hit off of it

single time we need to hit off of it like we did a CTE if you watch my

like we did a CTE if you watch my previous video and this one we can just

previous video and this one we can just run it and it sits there and so U again

run it and it sits there and so U again it feels very much like a real table and

it feels very much like a real table and I'm going to get to a little bit of the

I'm going to get to a little bit of the nuances of of the and the differences

nuances of of the and the differences between a regular table and a temp table

between a regular table and a temp table in a second but let's really quickly um

in a second but let's really quickly um we want more data in there you don't

we want more data in there you don't have to just um do it value by value we

have to just um do it value by value we can also just do

can also just do um uh where we select all of the data

um uh where we select all of the data from a specific table and insert that

from a specific table and insert that into a temp table and that is really

into a temp table and that is really quickly you know how I do it most of the

quickly you know how I do it most of the time most of the time I'm not inserting

time most of the time I'm not inserting values um I am you know taking a large

values um I am you know taking a large table and taking a subset of that and

table and taking a subset of that and then sticking it into a temp table so

then sticking it into a temp table so let's look at this really

let's look at this really quick and

quick and and run that so now we took all of the

and run that so now we took all of the data from employee salary and then we

data from employee salary and then we just stuck it into this table and really

just stuck it into this table and really quickly this is one of the big uses of a

quickly this is one of the big uses of a temp table we had let let's say for

temp table we had let let's say for example that this employee salary table

example that this employee salary table had a billion rows or or or just an

had a billion rows or or or just an extremely large number and we were

extremely large number and we were trying to uh you know hit a somewhat

trying to uh you know hit a somewhat complex query off of it where we're

complex query off of it where we're using joint coins and we're using U

using joint coins and we're using U maybe some window functions or different

maybe some window functions or different things you know it would take a very

things you know it would take a very long time to hit off of this but what we

long time to hit off of this but what we can do is we could insert that data into

can do is we could insert that data into this temp table and then we can hit off

this temp table and then we can hit off the temp table and it already has that

the temp table and it already has that sub uh that subsection of data that

sub uh that subsection of data that we're wanting to use for all of our

we're wanting to use for all of our later queries so really quickly that's

later queries so really quickly that's kind of um kind of a use case for that

kind of um kind of a use case for that so let's go down here we're going to

so let's go down here we're going to kind of create another one and this

kind of create another one and this one's going to be a little bit more

one's going to be a little bit more advanced a little bit of how I would

advanced a little bit of how I would actually use a temp table above was just

actually use a temp table above was just kind of showing the basic syntax how you

kind of showing the basic syntax how you kind of put data into it you know kind

kind of put data into it you know kind of how it's used now I'm going to show

of how it's used now I'm going to show you kind of how I would actually use it

you kind of how I would actually use it so let's do create table uh let's do

so let's do create table uh let's do temp

temp oops create

oops create table uh let's do

table uh let's do temp uh employee

temp uh employee 2 and then let's do open parentheses and

2 and then let's do open parentheses and we'll do job title and we'll make that a

we'll do job title and we'll make that a varar

varar 50 and then we can do

50 and then we can do employees per job we'll make that an

employees per job we'll make that an integer now we need average age make

integer now we need average age make that an integer and the very last one

that an integer and the very last one will be average salary I'll make that an

will be average salary I'll make that an integer as well and let's run this oops

integer as well and let's run this oops so we have our second table now we want

so we have our second table now we want to insert data into this one so we're

to insert data into this one so we're just going to do insert

just going to do insert into and we'll do temp employee 2 and

into and we'll do temp employee 2 and for this one I'm going to take a query

for this one I'm going to take a query that we used in a previous video and so

that we used in a previous video and so I'm just going to copy and paste that to

I'm just going to copy and paste that to save time uh and then we'll keep on

save time uh and then we'll keep on moving from there all right so I'm just

moving from there all right so I'm just going to paste that in we will run this

going to paste that in we will run this and really all it's doing is from this

and really all it's doing is from this these tables it's taking the job title

these tables it's taking the job title we're getting a count on the job title

we're getting a count on the job title average age average salary and that is

average age average salary and that is it um so let's see if that worked which

it um so let's see if that worked which it looks like it did but you know let's

it looks like it did but you know let's actually take a look at the

actually take a look at the [Music]

[Music] data and so now we have this subsection

data and so now we have this subsection of data from this join

of data from this join above and what this is going to do is is

above and what this is going to do is is whenever we want to run this we don't

whenever we want to run this we don't have to run it on these two tables and

have to run it on these two tables and create the join and then do the

create the join and then do the calculations which takes time what it's

calculations which takes time what it's going to do is it's going to take this

going to do is it's going to take this these exact values and place this into

these exact values and place this into this temporary table and if we want to

this temporary table and if we want to run further calculations on these values

run further calculations on these values we can easily do that in a fraction of

we can easily do that in a fraction of the time instead of having to run this

the time instead of having to run this every single time which will take up so

every single time which will take up so much uh uh processing power and it will

much uh uh processing power and it will reduce your runtime dramatically when

reduce your runtime dramatically when you're placing this data in this temp

you're placing this data in this temp table and hitting off of that instead of

table and hitting off of that instead of all these joints and everything above uh

all these joints and everything above uh a lot of times these temp tables are

a lot of times these temp tables are used in store procedures now if you

used in store procedures now if you haven't learned about store procedures

haven't learned about store procedures or used stor procedures at all you know

or used stor procedures at all you know that's okay I still want to show you

that's okay I still want to show you something that might be useful um

something that might be useful um although this is used a ton in store

although this is used a ton in store procedures so for example let's say we

procedures so for example let's say we have a store procedure set up we run the

have a store procedure set up we run the store procedure and we get an output and

store procedure and we get an output and you know we for whatever reason want to

you know we for whatever reason want to run it again and when we run it again uh

run it again and when we run it again uh we get this error and you know this temp

we get this error and you know this temp table lives somewhere it it doesn't live

table lives somewhere it it doesn't live in an actual in the actual database uh

in an actual in the actual database uh but it lives somewhere and so when we

but it lives somewhere and so when we run it again we get an error because

run it again we get an error because there's already a temp table created one

there's already a temp table created one trick or one little tip that I would

trick or one little tip that I would give is doing something like this saying

give is doing something like this saying drop table oops I don't know why I did

drop table oops I don't know why I did so many spaces drop table if

so many spaces drop table if exists and we'll do temp employee

exists and we'll do temp employee 2 just like that now what this is going

2 just like that now what this is going to do is when you're running that store

to do is when you're running that store procedure over and over and over again

procedure over and over and over again you're getting error or whatever for

you're getting error or whatever for whatever reason you need to run it

whatever reason you need to run it multiple times every time that you run

multiple times every time that you run it it's going to encounter this and so

it it's going to encounter this and so if that already exists it is going to

if that already exists it is going to delete that table and then allow you to

delete that table and then allow you to create it again and this is just a

create it again and this is just a really good thing to do so now if you

really good thing to do so now if you see down below I can run this time and

see down below I can run this time and time and time again and it is going to

time and time again and it is going to work every single time because it is

work every single time because it is checking to see if that exists and if it

checking to see if that exists and if it does it deletes it and then I can create

does it deletes it and then I can create again and so that is just a helpful tip

again and so that is just a helpful tip if you're going to try to use this I

if you're going to try to use this I highly recommend adding that to your

highly recommend adding that to your query just to make sure things run

query just to make sure things run smoothly I know there is a lot more that

smoothly I know there is a lot more that can go into temp tables a lot more of

can go into temp tables a lot more of the technical aspects or the DBA stuff

the technical aspects or the DBA stuff um obviously I just want to teach you

um obviously I just want to teach you how to use it and what you might use it

how to use it and what you might use it for and how to actually write it out but

for and how to actually write it out but you know there are a lot more things

you know there are a lot more things that you can do research on about

that you can do research on about processing speed and storage but unless

processing speed and storage but unless you are something like a DBA you

you are something like a DBA you probably don't need to worry about those

probably don't need to worry about those things and so if you are a DBA I do

things and so if you are a DBA I do recommend looking into those things

recommend looking into those things making sure you understand how that

making sure you understand how that works how this data is stored uh so that

works how this data is stored uh so that when people use them or you are using

when people use them or you are using them you know what's going on in the

them you know what's going on in the background but for getting up and

background but for getting up and running with temp tables I hope that

running with temp tables I hope that this was helpful thank you guys so much

this was helpful thank you guys so much for watching I really appreciate it if

for watching I really appreciate it if you like this video be sure to like And

you like this video be sure to like And subscribe below and I'll see you in the

subscribe below and I'll see you in the next

[Music] video what's going on everybody welcome

video what's going on everybody welcome back to another SQL tutorial today we're

back to another SQL tutorial today we're going to be looking at string functions

going to be looking at string functions some of the things that we're going to

some of the things that we're going to be looking at are things like trim

be looking at are things like trim replace substring and upper and lower uh

replace substring and upper and lower uh we're going to create a new table insert

we're going to create a new table insert a little bit of bad data into it and

a little bit of bad data into it and then we're going to be using that to

then we're going to be using that to work on our string functions today so I

work on our string functions today so I already have this set up right here um

already have this set up right here um I'm going to put this in the GitHub that

I'm going to put this in the GitHub that you can just download this you don't

you can just download this you don't have to you know type this out manually

have to you know type this out manually so go look in the description if you

so go look in the description if you know you just want to get that off the

know you just want to get that off the GitHub and download that and copy and

GitHub and download that and copy and paste it save you a little bit of time

paste it save you a little bit of time but let's go ahead and run this really

but let's go ahead and run this really quick and as you can see in this table

quick and as you can see in this table we have uh our data right here give me

we have uh our data right here give me one second so in this employeee errors

one second so in this employeee errors table basically what we have actually

table basically what we have actually let me pull this back up basically what

let me pull this back up basically what we have is in this first one we have

we have is in this first one we have here we go we have some uh basically

here we go we have some uh basically blank spaces on the right side the

blank spaces on the right side the second one some blank spaces on the left

second one some blank spaces on the left side U we also have Jimbo which is an

side U we also have Jimbo which is an error because his name is Jim um and

error because his name is Jim um and Halbert because his name is actually

Halbert because his name is actually Halbert um and then for Toby for

Halbert um and then for Toby for whatever reason that o is capitalized

whatever reason that o is capitalized and then uh Michael got in here and

and then uh Michael got in here and added this extra part so we're going to

added this extra part so we're going to have to figure out a way to take that

have to figure out a way to take that out when we're doing our query and

out when we're doing our query and that'll come in a little bit later I

that'll come in a little bit later I think in the substring section so let's

think in the substring section so let's get into it right away

get into it right away let's start using uh our left trim and

let's start using uh our left trim and right trim we're going to kind of go

right trim we're going to kind of go through each one um pretty quickly

through each one um pretty quickly hopefully I'm not not trying to make

hopefully I'm not not trying to make this a super long video because we got a

this a super long video because we got a lot of things to get through in this one

lot of things to get through in this one video uh so I'm going to go through the

video uh so I'm going to go through the trim right trim and left trim let's look

trim right trim and left trim let's look at uh the employee ID because that's the

at uh the employee ID because that's the one where we have some blank spaces on

one where we have some blank spaces on the right and the left side the left

the right and the left side the left side you'll be able to obviously you're

side you'll be able to obviously you're going to see that one much easier but uh

going to see that one much easier but uh let's start walking through this so

let's start walking through this so let's do

let's do select employee ID and before we get any

select employee ID and before we get any further let me just get the employee

further let me just get the employee errors on here so we can

errors on here so we can um so that we can see everything as it

um so that we can see everything as it comes up so we're just going to do trim

comes up so we're just going to do trim and then type in the column that we want

and then type in the column that we want to uh take these blank spaces out of

to uh take these blank spaces out of that's where the trim does the trim gets

that's where the trim does the trim gets rid of Blank spaces on either the front

rid of Blank spaces on either the front or the back or or the left on the right

or the back or or the left on the right side so on both sides that's what trim

side so on both sides that's what trim does and we'll say as ID trim so let's

does and we'll say as ID trim so let's run this one really quick and as you can

run this one really quick and as you can see this is our regular employee ID and

see this is our regular employee ID and so you know you can't visually see it as

so you know you can't visually see it as easily on this first one but there are

easily on this first one but there are blank spaces after this 101 and we got

blank spaces after this 101 and we got rid of those and then there were blank

rid of those and then there were blank spaces before the 102 and we got rid of

spaces before the 102 and we got rid of those now I'm just going to copy this uh

those now I'm just going to copy this uh two times because it's basically the

two times because it's basically the exact same thing but uh I'm going to

exact same thing but uh I'm going to show you them all at the same time so

show you them all at the same time so it's the exact same thing except lrim

it's the exact same thing except lrim and right trim uh and let's take a look

and right trim uh and let's take a look at all these at the same

at all these at the same time and let me pull it up so in the me

time and let me pull it up so in the me see if I can get these all in here okay

see if I can get these all in here okay in the trim it got rid of both the left

in the trim it got rid of both the left and the right side so all of these were

and the right side so all of these were fixed in the employee ID for the left

fixed in the employee ID for the left trim we're only getting going to be

trim we're only getting going to be getting rid of this one this one still

getting rid of this one this one still has um blank spaces on it and when we do

has um blank spaces on it and when we do the right trim we're only going to get

the right trim we're only going to get rid of the stuff on the right side so

rid of the stuff on the right side so this one doesn't change because this is

this one doesn't change because this is on the left hand side where the blank

on the left hand side where the blank spaces are so this one was fixed again

spaces are so this one was fixed again not super visual so you can't really see

not super visual so you can't really see it but that one is fixed uh let's move

it but that one is fixed uh let's move on to the next part uh which is using

on to the next part uh which is using replace so for this one we're going to

replace so for this one we're going to be looking at the last name so let's go

be looking at the last name so let's go back up really quick to the employee

back up really quick to the employee errors uh as you can tell the last name

errors uh as you can tell the last name um the biggest one where we kind of want

um the biggest one where we kind of want to take something out of because we

to take something out of because we don't want that um that Dash fired still

don't want that um that Dash fired still in there we're going to replace that and

in there we're going to replace that and so let's look at how to do that um let

so let's look at how to do that um let me

me just copy this real quick and get rid of

just copy this real quick and get rid of this top part um so we're going to do

this top part um so we're going to do the last name so let's just start off

the last name so let's just start off with our last name um and then just as a

with our last name um and then just as a baseline so we can see what it looks

baseline so we can see what it looks like before and then we'll do replace

like before and then we'll do replace and all we're going to specify is the

and all we're going to specify is the column that we want uh to do the

column that we want uh to do the replacing in we're going to specify the

replacing in we're going to specify the value that we want to replace so in this

value that we want to replace so in this it's going to be Dash fire oops got a

it's going to be Dash fire oops got a little aggressive on that one dash

little aggressive on that one dash fired and we're going to indicate what

fired and we're going to indicate what we want to replace it with now I'm just

we want to replace it with now I'm just going to replace it with blank

going to replace it with blank um and we can say as last name

um and we can say as last name fixed so let's see what this looks like

fixed so let's see what this looks like really

really quick and it looks like it worked so in

quick and it looks like it worked so in this last name it originally had

this last name it originally had flenderson DF fired and when we replaced

flenderson DF fired and when we replaced it and we took that Dash fired and

it and we took that Dash fired and replaced it with basically nothing uh it

replaced it with basically nothing uh it then fixed it and so now it looks

then fixed it and so now it looks correct all right let's move on to the

correct all right let's move on to the next one I think this one might be um

next one I think this one might be um the the longest one to write but that is

the the longest one to write but that is the

the substring um and let me take this real

substring um and let me take this real quick trying to save us some time so

quick trying to save us some time so substring is

substring is very is very very unique you can

very is very very unique you can specify um in a either a number or a

specify um in a either a number or a string you can specify the place that

string you can specify the place that you want to start and then you can also

you want to start and then you can also specify how many characters you want to

specify how many characters you want to go out um and and and it pulls that in

go out um and and and it pulls that in so just as a really quick example um and

so just as a really quick example um and then I'm going to show you kind of a use

then I'm going to show you kind of a use case for this one that I think is pretty

case for this one that I think is pretty cool that um you know

cool that um you know maybe let me

maybe let me see so that maybe that you'd find useful

see so that maybe that you'd find useful so I'm going to do first name and then

so I'm going to do first name and then I'm just going to do one comma three so

I'm just going to do one comma three so it's going to take the first name it's

it's going to take the first name it's going to start at the very first um very

going to start at the very first um very first letter or number and it's going to

first letter or number and it's going to go forward three spaces or three spots

go forward three spaces or three spots spots so let's just take a look at what

spots so let's just take a look at what that looks like so for our table it's

that looks like so for our table it's going to take Jim Pam and to or or Tobe

going to take Jim Pam and to or or Tobe for Toby um and so it's only going to

for Toby um and so it's only going to take the the first three because you're

take the the first three because you're starting at number one now what if we

starting at number one now what if we started at three so we do three comma 3

started at three so we do three comma 3 it's going to go to the

it's going to go to the third um digit or or third letter and

third um digit or or third letter and then it's going to go forward three so

then it's going to go forward three so you kind of get a sense of how this

you kind of get a sense of how this works now I'm going to show you

works now I'm going to show you something that I think is very

something that I think is very interesting that I think you guys will

interesting that I think you guys will also find interesting uh let me fix that

also find interesting uh let me fix that CU I just messed it up so if you've ever

CU I just messed it up so if you've ever heard of something called fuzzy matching

heard of something called fuzzy matching now if you don't know what fuzzy

now if you don't know what fuzzy matching is I'll give you an example

matching is I'll give you an example let's say in one table my name is Alex

let's say in one table my name is Alex and in another table my name is

and in another table my name is Alexander if we tried to join those two

Alexander if we tried to join those two together based off of my name they will

together based off of my name they will not join because one is Alex and one is

not join because one is Alex and one is Alexander there's not they're not an

Alexander there's not they're not an exact match but for if I take the

exact match but for if I take the substring and start position one and

substring and start position one and move forward four characters it's going

move forward four characters it's going to take Alex from both and then it will

to take Alex from both and then it will match them together uh and say that they

match them together uh and say that they are the same so that you know it may not

are the same so that you know it may not be perfect that's why it's called a

be perfect that's why it's called a fuzzy match because it can work for a

fuzzy match because it can work for a large majority of the time but it's not

large majority of the time but it's not going to work every single time and so I

going to work every single time and so I want to show you how we can use this

want to show you how we can use this here um really quick I need to join this

here um really quick I need to join this to um the demographics table so I'm

to um the demographics table so I'm going to do that really

going to do that really quick bear with me for just one

quick bear with me for just one second let's try to make this at least

second let's try to make this at least look somewhat good so what I'm going to

look somewhat good so what I'm going to do is I'm going to start off by saying

do is I'm going to start off by saying um let's tie it to the first name uh

um let's tie it to the first name uh let's do whoops let's do air. first name

let's do whoops let's do air. first name is equal to the demographics table first

is equal to the demographics table first name okay so I want to see and I'm just

name okay so I want to see and I'm just going to do first name for

going to do first name for ER and let's do them. first name so

ER and let's do them. first name so let's see what comes up when we do it

let's see what comes up when we do it like this so the only one that is going

like this so the only one that is going to work is Toby and that's because even

to work is Toby and that's because even though it has a capital O it's still

though it has a capital O it's still going to take it um so you know we want

going to take it um so you know we want to get all of them to match and we can

to get all of them to match and we can do that but it's going to be um a little

do that but it's going to be um a little bit of a different way than maybe is

bit of a different way than maybe is perfect but that's why they call it

perfect but that's why they call it fuzzy matching so we're going to use

fuzzy matching so we're going to use substring on this so I'm going to say

substring on this so I'm going to say substring oops let me that right so I'm

substring oops let me that right so I'm going to say substring and we're going

going to say substring and we're going to go one three so starting at the first

to go one three so starting at the first position and going forward with three

position and going forward with three and we're going to do the exact same

and we're going to do the exact same thing on the oops subst string it be

thing on the oops subst string it be great if I could spell that correctly

great if I could spell that correctly we're going to do the exact same thing

we're going to do the exact same thing so one and

so one and three so we are actually going

three so we are actually going to take this give me a second missed

to take this give me a second missed that we're going to take this up here

that we're going to take this up here and we're just going to go like that and

and we're just going to go like that and I why did I copy it with the error okay

I why did I copy it with the error okay so let's run this really

so let's run this really quickly and as you can see it is now

quickly and as you can see it is now going to match all of them and you can

going to match all of them and you can do this on a lot of different things

do this on a lot of different things typically when I'm doing a fuzzy match

typically when I'm doing a fuzzy match like this I'm not just going to do it on

like this I'm not just going to do it on a first name right because if every

a first name right because if every there can be a ton of people named JY

there can be a ton of people named JY you know we want to do it on uh and and

you know we want to do it on uh and and real quick let me actually show you

real quick let me actually show you um what the originals looked like just

um what the originals looked like just to make sure I hit the the point

to make sure I hit the the point across um and that is going to be first

across um and that is going to be first name and come all right so real quick

name and come all right so real quick let's actually look at this so it

let's actually look at this so it originally was Jimbo Pamela and Toby uh

originally was Jimbo Pamela and Toby uh in this one was Jim Pam and Toby And so

in this one was Jim Pam and Toby And so when we just took the first three

when we just took the first three because it was Jimbo it then becomes Jim

because it was Jimbo it then becomes Jim it was Pamela it becomes Pam now it

it was Pamela it becomes Pam now it matches and so that's what that's kind

matches and so that's what that's kind of the example that we're going

of the example that we're going for like I was saying I typically will

for like I was saying I typically will not just filter on a first name because

not just filter on a first name because there's going to be a ton of people

there's going to be a ton of people named Alex or Jim or or or you know

named Alex or Jim or or or you know Henry or whatever you're going to do

Henry or whatever you're going to do this on many different things so would

this on many different things so would be doing it on things like uh if I'm

be doing it on things like uh if I'm trying to do a fuzzy match on a person I

trying to do a fuzzy match on a person I do it on their gender to make sure that

do it on their gender to make sure that their gender is the same um and I

their gender is the same um and I wouldn't probably need to use a

wouldn't probably need to use a substring for that but just to kind

substring for that but just to kind of give you a little bit more

of give you a little bit more information I need to do it on the last

information I need to do it on the last name um so I need to use that substring

name um so I need to use that substring again and I would probably do it on the

again and I would probably do it on the age

age oops the what am I doing come on the age

oops the what am I doing come on the age and the date of birth okay so all of

and the date of birth okay so all of those things if you if you fuzzy match

those things if you if you fuzzy match on the first name and the last name and

on the first name and the last name and then the gender the age and the date of

then the gender the age and the date of birth are all the same then you can

birth are all the same then you can typically get a very high accuracy in

typically get a very high accuracy in matching people across

matching people across tables whether or not you have you know

tables whether or not you have you know this is an example if you don't have

this is an example if you don't have like an employee ID which is what we do

like an employee ID which is what we do have but take for example we were not

have but take for example we were not given that uh this is a way to match

given that uh this is a way to match them using substrings let's move on to

them using substrings let's move on to Upper and lower all upper and lower is

Upper and lower all upper and lower is going to do is basically take all the

going to do is basically take all the characters in The the text and make them

characters in The the text and make them either upper or make them lower so it's

either upper or make them lower so it's very

very self-explanatory uh let me copy this up

self-explanatory uh let me copy this up here and we will get going on this

here and we will get going on this one uh let's just look at the first name

one uh let's just look at the first name um specifically we're going to be

um specifically we're going to be looking at Toby right here so let's do

looking at Toby right here so let's do first name

first name let's do uh lower and all we have to do

let's do uh lower and all we have to do is put in the column that we want to

is put in the column that we want to do so this is our original first name

do so this is our original first name and it then takes every single uh string

and it then takes every single uh string that is in here or every single I guess

that is in here or every single I guess character and and it makes it lowercase

character and and it makes it lowercase that's all it does uh and it is the

that's all it does uh and it is the exact opposite when we do upper so we

exact opposite when we do upper so we can now take take a look at this one and

can now take take a look at this one and now everything's going to be capitalized

now everything's going to be capitalized so there is a lot that you can do with

so there is a lot that you can do with these string functions and this is not

these string functions and this is not all the string functions that there are

all the string functions that there are there are a lot more but I would say

there are a lot more but I would say that these are the more popular more

that these are the more popular more useful ones that I typically use on a

useful ones that I typically use on a regular basis and so I hope that this

regular basis and so I hope that this has been helpful I hope that you learned

has been helpful I hope that you learned something from this if you did be sure

something from this if you did be sure to like And subscribe below I have a lot

to like And subscribe below I have a lot more videos coming out with tutorials on

more videos coming out with tutorials on everything from SQL python Tableau and

everything from SQL python Tableau and Excel

Excel thank you so much for joining me I

thank you so much for joining me I appreciate it and I will see you in the

appreciate it and I will see you in the next

[Music] video what's going on everybody welcome

video what's going on everybody welcome back to another SQL tutorial today we

back to another SQL tutorial today we are talking about stored procedures now

are talking about stored procedures now what is a store procedure a store

what is a store procedure a store procedure is a group of SQL statements

procedure is a group of SQL statements that has been created and then stored in

that has been created and then stored in that database a store procedure can

that database a store procedure can accept input parameters and we will be

accept input parameters and we will be looking at that today but that means

looking at that today but that means that a single store procedure can be

that a single store procedure can be used over the network by several

used over the network by several different users uh and we can all be

different users uh and we can all be using different input data a store

using different input data a store procedure will also reduce Network

procedure will also reduce Network traffic and increase the performance and

traffic and increase the performance and lastly if we modify that store procedure

lastly if we modify that store procedure everyone who uses that store procedure

everyone who uses that store procedure in the future will also get that update

in the future will also get that update let's start writing out the store

let's start writing out the store procedure so we can look at the syntax

procedure so we can look at the syntax we'll start off very simple and then in

we'll start off very simple and then in the next one we'll get a little bit more

the next one we'll get a little bit more complicated so the very first thing that

complicated so the very first thing that you need to write is create and then

you need to write is create and then procedure and after that you're going to

procedure and after that you're going to name it so let's just call this one

name it so let's just call this one test and all you're going to say is as

test and all you're going to say is as and then you're going to write your

and then you're going to write your query and so let's just do select

query and so let's just do select everything from employee

everything from employee demographics and that is it we have

demographics and that is it we have created our very first store procedure

created our very first store procedure of course this is super super simple but

of course this is super super simple but let's execute this really quick and take

let's execute this really quick and take a look at

a look at it so it says that the commands

it so it says that the commands completed successfully let's go over to

completed successfully let's go over to our SQL tutorial we're going to go over

our SQL tutorial we're going to go over to

to programmability store procedures and it

programmability store procedures and it is not showing up there what we need to

is not showing up there what we need to do is we need to refresh our store

do is we need to refresh our store procedures we're just going to go right

procedures we're just going to go right here we're going to click refresh and

here we're going to click refresh and then there is our store procedure now

then there is our store procedure now how do you actually use the store

how do you actually use the store procedure that we just created so let's

procedure that we just created so let's go right down here and let's say x which

go right down here and let's say x which means execute and then all we're going

means execute and then all we're going to say is test test and we're going to

to say is test test and we're going to run

run this and there we go so all we put in

this and there we go so all we put in this store procedure was a select

this store procedure was a select statement and so when we actually

statement and so when we actually Rebrand the store procedure it returned

Rebrand the store procedure it returned our select statement now let's go down

our select statement now let's go down here and we're going to make it a little

here and we're going to make it a little bit more complicated we're going to do

bit more complicated we're going to do the exact same thing in create store

the exact same thing in create store procedure make sure I spelled that right

procedure make sure I spelled that right and let's call this

and let's call this tempore employee so if you remember from

tempore employee so if you remember from a previous video we worked on temp

a previous video we worked on temp tables and we created our temp tables

tables and we created our temp tables then inserted data into that we are

then inserted data into that we are going to add that to this St procedure

going to add that to this St procedure so we can see the difference between a

so we can see the difference between a simple query versus a little bit more

simple query versus a little bit more complicated query so I'm going to say as

complicated query so I'm going to say as and then I'm going to insert that in

and then I'm going to insert that in here now what this is doing is I'm

here now what this is doing is I'm creating a table and then right down

creating a table and then right down here I inserting that table now if I

here I inserting that table now if I create this store procedure and then

create this store procedure and then execute it nothing is actually going to

execute it nothing is actually going to be returned it will insert the data into

be returned it will insert the data into that temp table but since I don't have a

that temp table but since I don't have a select statement in this proced

select statement in this proced procedure nothing will be returned so

procedure nothing will be returned so let's write

let's write select everything and we'll just do

select everything and we'll just do from and this is temp

from and this is temp employee and right here and so now let's

employee and right here and so now let's create our store

create our store procedure so that created successfully

procedure so that created successfully let's refresh over

let's refresh over here and let's execute this so let's

here and let's execute this so let's just go down right

just go down right here and say execute and it's going to

here and say execute and it's going to be temp

be temp employee and now we will execute

employee and now we will execute this and there is our output now really

this and there is our output now really quick let's go into temp employee and we

quick let's go into temp employee and we actually want to change this store

actually want to change this store procedure so we're going to go over to

procedure so we're going to go over to modify so when we modify it a few things

modify so when we modify it a few things are going to show up on your screen the

are going to show up on your screen the first thing that you're going to see is

first thing that you're going to see is it says use SQL tutorial so it's just

it says use SQL tutorial so it's just specifying the database the next two

specifying the database the next two things you may not be as familiar with

things you may not be as familiar with it's set anzy nules and then set quoted

it's set anzy nules and then set quoted identifier if you don't know what these

identifier if you don't know what these are it's not super important the first

are it's not super important the first one just talks about how it to deal with

one just talks about how it to deal with nulles when you're using the wear

nulles when you're using the wear statement and then the quoted identifier

statement and then the quoted identifier just talks about how it uses quotes in

just talks about how it uses quotes in the actual query itself again not super

the actual query itself again not super important but they have those

important but they have those automatically turned on let's go down a

automatically turned on let's go down a little bit further and we're going to

little bit further and we're going to look at the alter procedure so we

look at the alter procedure so we created our store procedure but now we

created our store procedure but now we want to alter it so this is the alter

want to alter it so this is the alter procedure and we are going to add a

procedure and we are going to add a parameter to this so what the parameter

parameter to this so what the parameter is going to allow us to do is when we're

is going to allow us to do is when we're actually executing the store procedure

actually executing the store procedure we can specify an input into that store

we can specify an input into that store procedure so that we get a specific

procedure so that we get a specific result back and I'm going to show you

result back and I'm going to show you what I mean by that in just a second but

what I mean by that in just a second but let's actually add our input and we're

let's actually add our input and we're going to say at we're going to say job

going to say at we're going to say job title and we need to specify the data

title and we need to specify the data type that that is going to be so let's

type that that is going to be so let's just say

just say nvar 100 I know below it says varar 100

nvar 100 I know below it says varar 100 but that's um not extremely important so

but that's um not extremely important so this is going to be our input so we need

this is going to be our input so we need to go down here and say

to go down here and say where job title is equal to at job title

where job title is equal to at job title so when we actually are executing this

so when we actually are executing this and we say the job title is equal to

and we say the job title is equal to let's say accountant this is going to

let's say accountant this is going to become accountant and it's going to give

become accountant and it's going to give us our results based off of it being an

us our results based off of it being an accountant so let's go over here and we

accountant so let's go over here and we are going to click this execute temp

are going to click this execute temp employee which we just modified and when

employee which we just modified and when we run it we're going to get an error

we run it we're going to get an error because it is now expecting us to

because it is now expecting us to include our parameter of job title so

include our parameter of job title so what we need to do is we need to say at

what we need to do is we need to say at job title and let's say it's equal to a

job title and let's say it's equal to a Salesman now let's try running this one

Salesman now let's try running this one and see what we get and so there is our

and see what we get and so there is our output if we go back here I just wanted

output if we go back here I just wanted to show you really quick we do not have

to show you really quick we do not have to put this job title right here you can

to put this job title right here you can put this anywhere in the query and use

put this anywhere in the query and use it however you want that's how

it however you want that's how parameters work and that's why

parameters work and that's why parameters are so useful and you can use

parameters are so useful and you can use multiple parameters for one store

multiple parameters for one store procedure so you don't have to just

procedure so you don't have to just limit yourself to one or none you can

limit yourself to one or none you can put as many as you really like so I hope

put as many as you really like so I hope that this video is helpful and that you

that this video is helpful and that you understand store procedures just a

understand store procedures just a little bit better thank you guys so much

little bit better thank you guys so much for watching I really appreciate it if

for watching I really appreciate it if you like this video be sure to like And

you like this video be sure to like And subscribe below and I'll see you in the

subscribe below and I'll see you in the next

next [Music]

[Music] video

what's going on everybody welcome back to another SQL tutorial today we are

to another SQL tutorial today we are going to be talking about subqueries now

going to be talking about subqueries now subqueries are often called inner

subqueries are often called inner queries or an nestic queries and they're

queries or an nestic queries and they're basically a query within a query a

basically a query within a query a subquery is used to return data that

subquery is used to return data that will be used in the main query or the

will be used in the main query or the outer query as a condition to specify

outer query as a condition to specify the data that we want retrieved you can

the data that we want retrieved you can use subqueries almost anywhere you can

use subqueries almost anywhere you can use it in the select part of a query the

use it in the select part of a query the from the where you can also use it in

from the where you can also use it in insert update and delete statements but

insert update and delete statements but in today's tutorial we're only going to

in today's tutorial we're only going to be looking at the select the from in the

be looking at the select the from in the Weare statements and you should get a

Weare statements and you should get a pretty good idea of how to use it in

pretty good idea of how to use it in those other statements all right now I'm

those other statements all right now I'm going to paste on screen basically what

going to paste on screen basically what we're going to be walking through today

we're going to be walking through today but really quick let's just take a look

but really quick let's just take a look at the table that we're actually be

at the table that we're actually be working in and that is going to be from

working in and that is going to be from the employee salary table and I just

the employee salary table and I just want to show you the data that we're

want to show you the data that we're going to be working with before we

going to be working with before we actually get into it so we have an

actually get into it so we have an employee ID we have a job title and then

employee ID we have a job title and then we have a salary so really quick I'm

we have a salary so really quick I'm going to show you what it looks like to

going to show you what it looks like to have a subquery in the select statement

have a subquery in the select statement so let's go down here really

so let's go down here really quick and what we're going to try to do

quick and what we're going to try to do is kind of do something like a Windows

is kind of do something like a Windows function but without actually having to

function but without actually having to do the windows function um and so we're

do the windows function um and so we're going to do this with a subquery so I'm

going to do this with a subquery so I'm going to select and really quick

going to select and really quick actually let me copy this so we're going

actually let me copy this so we're going to do employee

to do employee ID there we go we're going to do salary

ID there we go we're going to do salary and now we can start building our

and now we can start building our subquery so we need to do an open

subquery so we need to do an open parenthesis and I'm just going to copy

parenthesis and I'm just going to copy this really quick because we're going to

this really quick because we're going to be doing it off of that table so we're

be doing it off of that table so we're going to say select and then I'll paste

going to say select and then I'll paste that and close it as well but what we

that and close it as well but what we want to do is we want to say average and

want to do is we want to say average and salary now what this is going to do is

salary now what this is going to do is it is literally going to run this and

it is literally going to run this and let's run this really quick it is going

let's run this really quick it is going to run this and is going to show that

to run this and is going to show that the average salary for all the employees

the average salary for all the employees is 40 $

is 40 $ 7,99 so we are looking at the average

7,99 so we are looking at the average salary for every employee so when we run

salary for every employee so when we run this it is going to give us the employee

this it is going to give us the employee ID the salary and then in the very last

ID the salary and then in the very last one is going to show the average salary

one is going to show the average salary for every employee now it doesn't have a

for every employee now it doesn't have a column header so or or a column name so

column header so or or a column name so let's give it um let's say as all

let's give it um let's say as all average salary and we'll run that one

average salary and we'll run that one more time just to make it look a little

more time just to make it look a little prettier um you can also do this in

prettier um you can also do this in Partition bu I'm going to Super quickly

Partition bu I'm going to Super quickly just really quickly write this out um it

just really quickly write this out um it should take no time at all and then I'm

should take no time at all and then I'm going to show you why we can't do this

going to show you why we can't do this without the subquery why you aren't able

without the subquery why you aren't able to do this with a group buy so really

to do this with a group buy so really quickly let me copy this I'm going to

quickly let me copy this I'm going to put it right down here and we're going

put it right down here and we're going to say average salary whoops and we can

to say average salary whoops and we can get rid of all this and we can say over

get rid of all this and we can say over and we're not going to partition it by

and we're not going to partition it by anything

anything but let's run both these at the same

but let's run both these at the same time and you'll see that they're the

time and you'll see that they're the exact same outputs and so it's just a

exact same outputs and so it's just a different way of doing it in this

different way of doing it in this example but it really is just to show a

example but it really is just to show a comparison of how you might be able to

comparison of how you might be able to use a subquery in the select statement

use a subquery in the select statement now you might be wondering why group I

now you might be wondering why group I does not work for this uh really quickly

does not work for this uh really quickly I'm going to write this out and let's

I'm going to write this out and let's get rid of that and we'll say Group by

get rid of that and we'll say Group by whoops let me at least try to write it

whoops let me at least try to write it correctly Group by and we'll do employee

correctly Group by and we'll do employee ID and we also have to do salary and

ID and we also have to do salary and then we'll say order

then we'll say order by one two so let's run

by one two so let's run this and as you can see since we have to

this and as you can see since we have to use the group by it groups by both the

use the group by it groups by both the ordered ID and the salary and so we're

ordered ID and the salary and so we're not going to be able to get that all

not going to be able to get that all average salary that we're looking for

average salary that we're looking for that we can get in the partition buy and

that we can get in the partition buy and also the subquery in the select

also the subquery in the select statement now I'm going to show you the

statement now I'm going to show you the subquery in the from statement so let's

subquery in the from statement so let's just get rid of that really quick and

just get rid of that really quick and let's say select everything let's say

let's say select everything let's say from and we're going to do an open

from and we're going to do an open parentheses here and here is where we're

parentheses here and here is where we're going to write our subquery so if you

going to write our subquery so if you have watched previous videos where I've

have watched previous videos where I've done uh tutorials on the CTE or tutorial

done uh tutorials on the CTE or tutorial on the temp tables this is one that is

on the temp tables this is one that is very much like those except I think a

very much like those except I think a little bit less efficient when I'm doing

little bit less efficient when I'm doing something where I'm creating a table and

something where I'm creating a table and then quering off off of it which is what

then quering off off of it which is what we're about to do I much prefer a CTE or

we're about to do I much prefer a CTE or a temp table subqueries tend to be a

a temp table subqueries tend to be a little bit slow compared to a temp table

little bit slow compared to a temp table or a CTE I tend to use temp tables a lot

or a CTE I tend to use temp tables a lot more because you can reuse them over and

more because you can reuse them over and over whereas a subquery you cannot you

over whereas a subquery you cannot you have to write it out each time so really

have to write it out each time so really quickly I'm going to show you how it's

quickly I'm going to show you how it's done although I don't really recommend

done although I don't really recommend using this method really quickly let's

using this method really quickly let's go up here and let's steal this

go up here and let's steal this partition bu really quick this will be

partition bu really quick this will be our

our subquery uh and let's paste this in here

subquery uh and let's paste this in here I'm going make this look a little nicer

I'm going make this look a little nicer just so you can visualize it a little

just so you can visualize it a little bit

bit easier um so really quick what this is

easier um so really quick what this is going to do is it is first going to run

going to do is it is first going to run this and create this table again much

this and create this table again much like a temp table or a CTE so let's

like a temp table or a CTE so let's execute this really quick it's going to

execute this really quick it's going to create this table and then it's going to

create this table and then it's going to allow us to query off of it so I can

allow us to query off of it so I can actually say um and let me give kind of

actually say um and let me give kind of kind of an alias to this a. employee ID

kind of an alias to this a. employee ID and then let's say all average salary so

and then let's say all average salary so now I can take um columns from this

now I can take um columns from this inner query if I want to and just select

inner query if I want to and just select those or I can select everything and

those or I can select everything and return that entire table again I much

return that entire table again I much prefer a temp table or a CTE for this

prefer a temp table or a CTE for this type of situation but as an example I

type of situation but as an example I just wanted to show you how it works now

just wanted to show you how it works now let's go down to the subquery in thewar

let's go down to the subquery in thewar statement but really quick I just steal

statement but really quick I just steal this query so I don't have to rewrite

this query so I don't have to rewrite everything and let's get rid of this

everything and let's get rid of this really quick and add back the job

really quick and add back the job title all right so let's look at this

title all right so let's look at this really quick so we have our table that

really quick so we have our table that we've been using our employee ID job

we've been using our employee ID job title salary so for this example we only

title salary so for this example we only want to return employees if they're over

want to return employees if they're over the age of 30 and as you can see in this

the age of 30 and as you can see in this table there is no age column that is in

table there is no age column that is in the employee demographics table now if

the employee demographics table now if we wanted we could join to that table

we wanted we could join to that table and get that information or we could use

and get that information or we could use a subquery and so for this example we

a subquery and so for this example we are going to be using a subquery so

are going to be using a subquery so let's go right down here and say where

let's go right down here and say where employee ID is in and we'll do an open

employee ID is in and we'll do an open parentheses and now this is where we are

parentheses and now this is where we are going to build out the subquery so just

going to build out the subquery so just for visual purposes I'm going to go

for visual purposes I'm going to go right here I'm going to say select

right here I'm going to say select everything and we'll do from employee

everything and we'll do from employee demographics and close the parenthesis

demographics and close the parenthesis so we're going to try to select

so we're going to try to select something in this subquery that will

something in this subquery that will then identify the employee IDs that are

then identify the employee IDs that are over the age of 30 so really quickly

over the age of 30 so really quickly let's take a look at this table so right

let's take a look at this table so right now we have the entire table selected so

now we have the entire table selected so we have the employee ID first name last

we have the employee ID first name last name age and gender so in this subquery

name age and gender so in this subquery the only thing that should be returned

the only thing that should be returned is the employee ID and in fact in your

is the employee ID and in fact in your subquery you can only have one column

subquery you can only have one column selected so I can't select everything I

selected so I can't select everything I have to specify one column and that's a

have to specify one column and that's a little bit different than how we did it

little bit different than how we did it in in this from statement where we were

in in this from statement where we were basically able to select the entire

basically able to select the entire table and then in the select statement

table and then in the select statement specify what columns we wanted in the

specify what columns we wanted in the where statement we can't do that so we

where statement we can't do that so we want to return the employee ID and we

want to return the employee ID and we also want to say where the age is

also want to say where the age is greater than 30 so let's run this really

greater than 30 so let's run this really quick and see if it works as you can see

quick and see if it works as you can see in the results these are the employees

in the results these are the employees who are over the age of 30 now if you

who are over the age of 30 now if you wanted to display the age as a column in

wanted to display the age as a column in this output you would have to join to

this output you would have to join to that table and then put that column or

that table and then put that column or that field in the select statement but

that field in the select statement but in a lot of situations you won't

in a lot of situations you won't actually want or need to do that and so

actually want or need to do that and so a subquery can be a really good option

a subquery can be a really good option in these scenarios with that being said

in these scenarios with that being said this is the last video in the advanced

this is the last video in the advanced sequel tutorials I hope that this Series

sequel tutorials I hope that this Series has been helpful and that you learned

has been helpful and that you learned something along the way thank you so

something along the way thank you so much for joining me I really appreciate

much for joining me I really appreciate it if you like this video be sure to

it if you like this video be sure to like And subscribe below and I'll see

like And subscribe below and I'll see you in the next

video [Music]

[Music] what is going on everybody welcome back

what is going on everybody welcome back to another video today we are starting

to another video today we are starting our data analyst portfolio project

series now before we jump into our first project I wanted to talk with you for

project I wanted to talk with you for just a second so that we're all on the

just a second so that we're all on the same page first thing is that there are

same page first thing is that there are going to be four projects the first one

going to be four projects the first one is going to be SQL and we doing a lot of

is going to be SQL and we doing a lot of data exploration and we'll be setting up

data exploration and we'll be setting up a lot of our data to visualize it in

a lot of our data to visualize it in Tableau Tableau is going to be our

Tableau Tableau is going to be our second project in our third project

second project in our third project again we're going back to SQL but we're

again we're going back to SQL but we're going to be doing a lot more of the ETL

going to be doing a lot more of the ETL process so a lot more of the data

process so a lot more of the data cleaning I did that one as the third

cleaning I did that one as the third project because I think it's going to be

project because I think it's going to be a little bit more advanced than this

a little bit more advanced than this first project I tried to make it as

first project I tried to make it as beginner friendly as possible so even if

beginner friendly as possible so even if you are a complete beginner as long as

you are a complete beginner as long as you've walked through uh you know the

you've walked through uh you know the tutorial that I have made on my channel

tutorial that I have made on my channel you should be pretty good and then the

you should be pretty good and then the fourth and the final project will be

fourth and the final project will be with python we'll be using a lot of

with python we'll be using a lot of pandas doing a little bit of data

pandas doing a little bit of data cleaning and then doing visualizations

cleaning and then doing visualizations as well as I said just a second ago I'm

as well as I said just a second ago I'm trying to make this as beginner friendly

trying to make this as beginner friendly as I possibly can the whole point of the

as I possibly can the whole point of the series is that if you are trying to

series is that if you are trying to apply for a data analyst job by the end

apply for a data analyst job by the end of the series you should have an entire

of the series you should have an entire portfolio or at least a a really good

portfolio or at least a a really good start at a portfolio to show a potential

start at a portfolio to show a potential employer I give you full permission to

employer I give you full permission to copy every script every query line for

copy every script every query line for line if that is what you want to do and

line if that is what you want to do and create your own portfolio I am totally

create your own portfolio I am totally fine with that but I will encourage you

fine with that but I will encourage you and I'm sure I'll say this throughout

and I'm sure I'll say this throughout the video I encourage you to try to

the video I encourage you to try to think of your own queries try to think

think of your own queries try to think of your own insights and your own things

of your own insights and your own things that you can do to make this portfolio

that you can do to make this portfolio project unique with that being said I'm

project unique with that being said I'm super excited to get started on this

super excited to get started on this with you guys so let's jump over to my

with you guys so let's jump over to my screen and get started on our very first

screen and get started on our very first project all right so now that we are on

project all right so now that we are on my screen we are going to get started on

my screen we are going to get started on this project we're going to download the

this project we're going to download the data set we are going to format it just

data set we are going to format it just a little bit in Excel

a little bit in Excel and then we're going to get into sequel

and then we're going to get into sequel where we will start querying it I will

where we will start querying it I will say that I think this is going to be a

say that I think this is going to be a very long video I'm hoping to keep it

very long video I'm hoping to keep it under an hour and a half I may separate

under an hour and a half I may separate this into two videos depending on how

this into two videos depending on how long it runs um but you know I I will do

long it runs um but you know I I will do my best to keep it short but we have a

my best to keep it short but we have a lot to get through I'm going to

lot to get through I'm going to basically do no Cuts I'm I'm that's my

basically do no Cuts I'm I'm that's my goal is to do no cuts um in this because

goal is to do no cuts um in this because I want to walk you through each step of

I want to walk you through each step of the process so that you understand

the process so that you understand everything that's going on and I I you

everything that's going on and I I you don't get lost at some point um but I

don't get lost at some point um but I think this is probably the best way to

think this is probably the best way to do it we'll see uh the very first thing

do it we'll see uh the very first thing we're going to do is download our data

we're going to do is download our data set so you know as we're looking at this

set so you know as we're looking at this there's an option right here to download

there's an option right here to download the data set I don't recommend that one

the data set I don't recommend that one um you can it just won't give you all

um you can it just won't give you all the information that I personally want

the information that I personally want which is go back to like the very

which is go back to like the very beginning um if you go down right here

beginning um if you go down right here to the very first graph um you can

to the very first graph um you can actually push this back and then

actually push this back and then download it and what this will do is it

download it and what this will do is it will go back to I think January 1st of

will go back to I think January 1st of 2020 so let's open this one

2020 so let's open this one up um and when we get in here we're

up um and when we get in here we're going to reformat it just a little bit

going to reformat it just a little bit it's nothing too complicated I hope um

it's nothing too complicated I hope um I'm just going to double click here

I'm just going to double click here actually let me let me go up here and

actually let me let me go up here and filter just in case we want to filter

filter just in case we want to filter anything so um what we have here is a

anything so um what we have here is a ton of information on Co I mean just a

ton of information on Co I mean just a ton and it goes back to early 2020 I

ton and it goes back to early 2020 I believe it does go back to the first of

believe it does go back to the first of 2020 so really quick a really brief

2020 so really quick a really brief introduction of what kind of data is in

introduction of what kind of data is in here we have total cases new

here we have total cases new cases um total deaths new deaths we use

cases um total deaths new deaths we use those quite a bit in the the queries

those quite a bit in the the queries that are coming

that are coming up um if we go way over here we have

up um if we go way over here we have total vaccin vaccinations people

total vaccin vaccinations people vaccinated um and then over here a

vaccinated um and then over here a little bit farther we have population

little bit farther we have population that's the main stuff we're going to be

that's the main stuff we're going to be working with today as you can see

working with today as you can see there's so many other things in here I

there's so many other things in here I mean you can use this if you want to go

mean you can use this if you want to go back and do more stuff on this I highly

back and do more stuff on this I highly recommend it there's such you know

recommend it there's such you know there's so such unique data in here

there's so such unique data in here about smokers and diabetes and like all

about smokers and diabetes and like all this random stuff that I did not do a

this random stuff that I did not do a deep dive in I mean I could I could

deep dive in I mean I could I could spend you know a month just like looking

spend you know a month just like looking at this data set and and getting really

at this data set and and getting really interesting stuff from it um but I'm not

interesting stuff from it um but I'm not going to do that I wanted to do this

going to do that I wanted to do this faster than uh two months to to

faster than uh two months to to complete what we're going to do um is

complete what we're going to do um is we're going to go back over here we're

we're going to go back over here we're going to take this

going to take this population and we're going to click on

population and we're going to click on this as and we're going to click contrl

this as and we're going to click contrl X and that's going to cut it we're going

X and that's going to cut it we're going to go back to the very beginning and

to go back to the very beginning and we're going to place it right here and

we're going to place it right here and we're going to right click and say

we're going to right click and say insert cut cells now why are we doing

insert cut cells now why are we doing this because I've already done this

this because I've already done this entire project um and if you don't do

entire project um and if you don't do this you're going to do a join with

this you're going to do a join with every single query you do which if you

every single query you do which if you want to do that keep it there and then

want to do that keep it there and then just you know change your query for for

just you know change your query for for that I did it like this because I wanted

that I did it like this because I wanted to show joins later on I wanted to keep

to show joins later on I wanted to keep it kind of simple at the beginning um

it kind of simple at the beginning um and then work my way to a little bit

and then work my way to a little bit more advanced things which you will see

more advanced things which you will see um it gets you know semi Advanced but

um it gets you know semi Advanced but not too much I promise um just stick

not too much I promise um just stick with me let's go back over here we're

with me let's go back over here we're going to go to uh actually double A and

going to go to uh actually double A and then we're going to click control shift

then we're going to click control shift right key that's going to select

right key that's going to select everything over here and we're going to

everything over here and we're going to literally delete it okay this is going

literally delete it okay this is going to be our first table over here so

to be our first table over here so everything you see over here is our

everything you see over here is our first table um and we're going to save

first table um and we're going to save that so let's save as I'm just going to

that so let's save as I'm just going to keep it in my downloads as and let's do

keep it in my downloads as and let's do covid deaths so that has our death

covid deaths so that has our death information the next one is going to

information the next one is going to include our um vaccination information

include our um vaccination information which is what we're going to join on and

which is what we're going to join on and then um we're going to do that later so

then um we're going to do that later so let's let's hit contrl Z that's going to

let's let's hit contrl Z that's going to bring it back now let's select on Z and

bring it back now let's select on Z and go all the way to e and we're going to

go all the way to e and we're going to do the same thing we're going to delete

do the same thing we're going to delete this looks like there's no data but I

this looks like there's no data but I promise there is later on the

promise there is later on the vaccinations um like total vaccinations

vaccinations um like total vaccinations if we go down um you can see that that

if we go down um you can see that that starts on in February the end very end

starts on in February the end very end of February in 2021 that's because

of February in 2021 that's because vaccinations are you know didn't come

vaccinations are you know didn't come out till recently now let's save this

out till recently now let's save this file and we're going to save as instead

file and we're going to save as instead of covid deaths we'll do Co

of covid deaths we'll do Co vaccinations all right now let's save

vaccinations all right now let's save that so now we have our two excels that

that so now we have our two excels that we want we need to get them into SQL

we want we need to get them into SQL we're going to go over to SQL and we're

we're going to go over to SQL and we're going to create a portfolio project

going to create a portfolio project database I've already done this all you

database I've already done this all you have to do though is rightclick click

have to do though is rightclick click new database type in

new database type in portfolio project and then click okay

portfolio project and then click okay and it will create your database for you

and it will create your database for you um if you open up the tables it should

um if you open up the tables it should be empty and that's where we're going to

be empty and that's where we're going to put these two Excel files now uh I had a

put these two Excel files now uh I had a ton of trouble actually importing these

ton of trouble actually importing these excels um I mean I tried everything and

excels um I mean I tried everything and I eventually just went down a rabbit

I eventually just went down a rabbit hole of how to get these in I don't know

hole of how to get these in I don't know if it's me or or what but I could not

if it's me or or what but I could not figure out how to do it if you go to

figure out how to do it if you go to portfolio project you hit tasks and you

portfolio project you hit tasks and you hit import data that may do it for you

hit import data that may do it for you and it may work um it did not work for

and it may work um it did not work for me uh it just it kept giving me errors

me uh it just it kept giving me errors so what I would recommend you do right

so what I would recommend you do right off the bat just to make sure that we're

off the bat just to make sure that we're doing the same thing um and you can do

doing the same thing um and you can do it that way if you want I went over here

it that way if you want I went over here to start um again I'm on a Windows and I

to start um again I'm on a Windows and I went down to Microsoft SQL Server 2019

went down to Microsoft SQL Server 2019 and clicked Import and

and clicked Import and Export looks the same but for whatever

Export looks the same but for whatever reason it it all the research I did it

reason it it all the research I did it has to do with the 32-bit versus the

has to do with the 32-bit versus the 64bit when you do it this way it goes to

64bit when you do it this way it goes to the 64-bit and it is able to import the

the 64-bit and it is able to import the data if you do it the other way it was

data if you do it the other way it was doing it the 32-bit version and gives

doing it the 32-bit version and gives you an error I don't understand it don't

you an error I don't understand it don't ask me that's that's the re that's I

ask me that's that's the re that's I mean I went down a huge rabbit hole but

mean I went down a huge rabbit hole but this one works so let's go over here and

this one works so let's go over here and this is going to be our data source

this is going to be our data source where is the data coming from it's an

where is the data coming from it's an Excel file

Excel file so let's do that let's browse and let's

so let's do that let's browse and let's go over to my

downloads I thought I saved it in downloads uh maybe because it's an Excel

downloads uh maybe because it's an Excel workbook what was I saving

workbook what was I saving before Oh that's a

before Oh that's a CSV okay something important to note is

CSV okay something important to note is we're doing an Excel and not a

we're doing an Excel and not a CSV you're going to get the same error

CSV you're going to get the same error I'm just doing it live and I'm making

I'm just doing it live and I'm making myself look stupid so um we're going to

myself look stupid so um we're going to save it but instead of a CSV we're going

save it but instead of a CSV we're going to save it as an Excel workbook so let's

to save it as an Excel workbook so let's save that um now we have to go back to

save that um now we have to go back to how it was right

how it was right here um the same way and we're going to

here um the same way and we're going to file save as and let's do this is now

file save as and let's do this is now covid

covid deaths and save it as a workbook now we

deaths and save it as a workbook now we have them now let's go back um now we

have them now let's go back um now we have our covid deaths and our covid

have our covid deaths and our covid vaccinations let's do our deaths first

vaccinations let's do our deaths first um let me get back right here so it

um let me get back right here so it looks kind of more

looks kind of more normal um so we have our Excel file we

normal um so we have our Excel file we have our covid deaths let's go next and

have our covid deaths let's go next and now we have to say where we're going to

now we have to say where we're going to place it where's our destination so

place it where's our destination so we're going to click over here and go

we're going to click over here and go down to SQL Server native client

down to SQL Server native client 11.0 I want to say this is something

11.0 I want to say this is something that I messed up and it took me like 45

that I messed up and it took me like 45 minutes to figure out it was the

minutes to figure out it was the stupidest mistake um it's gonna autop

stupidest mistake um it's gonna autop populate a server name

populate a server name and I never checked to confirm that this

and I never checked to confirm that this was my server name and so I couldn't

was my server name and so I couldn't figure out why I wasn't able to insert

figure out why I wasn't able to insert this into my portfolio project uh

this into my portfolio project uh database that's because mine is 01 I

database that's because mine is 01 I created two different servers um

created two different servers um intentionally and for whatever reason I

intentionally and for whatever reason I forgot that and so all I have to do is

forgot that and so all I have to do is add 01 over here so just make sure yours

add 01 over here so just make sure yours is is the same thing click portfolio

is is the same thing click portfolio project click next yes we're want to

project click next yes we're want to copy the data should autop populate if

copy the data should autop populate if it doesn't if it gives you like multiple

it doesn't if it gives you like multiple you can always uh check mark on the one

you can always uh check mark on the one that you think is the right one it

that you think is the right one it should be the first one we'll click next

should be the first one we'll click next we'll just click finish I'm sure it says

we'll just click finish I'm sure it says run immediately we'll click finish and

run immediately we'll click finish and finish now while this is running um

finish now while this is running um there should be around

there should be around 89,000 that's how it was like a week ago

89,000 that's how it was like a week ago when I started it maybe a little more

when I started it maybe a little more now because there's extra

now because there's extra days um with that being said you know

days um with that being said you know there's going to be

there's going to be a good siiz amount of data um we're

a good siiz amount of data um we're about to do a lot of different things

about to do a lot of different things we're going to start at the very basics

we're going to start at the very basics of just like queer quering the table

of just like queer quering the table like super simple um and then we're

like super simple um and then we're going to go into things like joins ctes

going to go into things like joins ctes temp tables creating views um I the

temp tables creating views um I the whole purpose of what we're about to do

whole purpose of what we're about to do is not

is not to it's not to keep it too simple um I

to it's not to keep it too simple um I want to showcase to a potential employer

want to showcase to a potential employer right that you can do more advanced

right that you can do more advanced Advanced things so I'm going to probably

Advanced things so I'm going to probably do I mean I'm I'm looking at because I

do I mean I'm I'm looking at because I have already done this entire project

have already done this entire project individually I mean we've probably got

individually I mean we've probably got like 15 to 20 queries here you don't

like 15 to 20 queries here you don't have to do all of them um I'm going to

have to do all of them um I'm going to walk through all of them and you can

walk through all of them and you can choose which ones you want but you don't

choose which ones you want but you don't have to do all them it is quite a few so

have to do all them it is quite a few so just know that so there's 85,000 right

just know that so there's 85,000 right here that's

here that's fantastic uh it won't show up

fantastic uh it won't show up immediately you need to refresh it uh

immediately you need to refresh it uh and there we go so that's our covid

and there we go so that's our covid vaccinations U let's get rid of this so

vaccinations U let's get rid of this so we just have Co vaccinations um I

we just have Co vaccinations um I thought that was our covid deaths one

thought that was our covid deaths one but maybe I'm wrong um but let's do the

but maybe I'm wrong um but let's do the exact same thing down

exact same thing down here and we will import and say

here and we will import and say next we're going to go down to

next we're going to go down to Excel and browse and now we want to do

Excel and browse and now we want to do the covid deaths apparently last time we

the covid deaths apparently last time we did the vaccinations which

did the vaccinations which um I actually actually you know what I

um I actually actually you know what I bet what it did was it took yeah it took

bet what it did was it took yeah it took this right here as Co vaccinations but

this right here as Co vaccinations but that was the deaths one as it saved so

that was the deaths one as it saved so uh forget that let's go right

uh forget that let's go right here let's do the co vaccinations it

here let's do the co vaccinations it just has the same sheet

just has the same sheet name uh so sorry for the confusion

name uh so sorry for the confusion destination is going to be the exact

destination is going to be the exact same place it's going to be SQL Server

same place it's going to be SQL Server native client let's add that

native client let's add that 01 and let's click refresh portfolio

01 and let's click refresh portfolio project next next um like I said before

project next next um like I said before if it does this just click the first one

if it does this just click the first one it's going to be Co vaccinations it did

it's going to be Co vaccinations it did that for the covid deaths that's because

that for the covid deaths that's because I made the mistake earlier I hope you I

I made the mistake earlier I hope you I hope when you're watching this you

hope when you're watching this you aren't super confused um the whole point

aren't super confused um the whole point make two tables or make two excels one

make two tables or make two excels one should be covid deaths one should be Co

should be covid deaths one should be Co vaccinations upload them and then rename

vaccinations upload them and then rename them in a nutshell U so we have the same

them in a nutshell U so we have the same amount uh let's refresh

amount uh let's refresh this this one is actually the co

this this one is actually the co vaccinations this one is covid

vaccinations this one is covid deaths I'm telling you this stuff is

deaths I'm telling you this stuff is it's confuses me sometimes to be honest

it's confuses me sometimes to be honest um but we're going to query this really

um but we're going to query this really quick to make sure we act are actually

quick to make sure we act are actually doing um what we're supposed to be doing

doing um what we're supposed to be doing so let's do select

so let's do select everything from um and let's do

everything from um and let's do portfolio project and you can do

portfolio project and you can do dbo or you can do dot dot I tend to just

dbo or you can do dot dot I tend to just do that because it's easier um let's

do that because it's easier um let's look at this one make sure it's the

look at this one make sure it's the right table so we have total cases new

right table so we have total cases new cases

cases perfect um and let's order on let's do

perfect um and let's order on let's do three comma 4 just to make

three comma 4 just to make sure or order by of course just to make

sure or order by of course just to make sure that we have all everything that

sure that we have all everything that we're looking for so this looks right

we're looking for so this looks right this looks like our

this looks like our Excel let's copy this let's go down here

Excel let's copy this let's go down here we're going to do covid

we're going to do covid vaccinations and let's run this one make

vaccinations and let's run this one make sure the second one came in correctly as

sure the second one came in correctly as well so perfect so we have our two

well so perfect so we have our two tables this is fantastic news um and now

tables this is fantastic news um and now we can get going um we can keep this one

we can get going um we can keep this one I'm GNA comment it out in case you know

I'm GNA comment it out in case you know we want to come back to it um I'm going

we want to come back to it um I'm going to really quick again right here I have

to really quick again right here I have another laptop I have already done this

another laptop I have already done this whole project so I'm just using it as a

whole project so I'm just using it as a guideline to know kind of what I'm doing

guideline to know kind of what I'm doing next so that I don't waste everyone's

next so that I don't waste everyone's time um so really quickly let's just

time um so really quickly let's just let's select the data that we are going

let's select the data that we are going to be using you don't have to use these

to be using you don't have to use these comments I will say that I'm going to

comments I will say that I'm going to specify I'm going to say hey this

specify I'm going to say hey this comment is something I would keep in

comment is something I would keep in your portfolio project I'm going to add

your portfolio project I'm going to add a bunch of extra stuff that is not

a bunch of extra stuff that is not needed um just for your purpose but when

needed um just for your purpose but when you are creating your portfolio project

you are creating your portfolio project you shouldn't be adding some of the

you shouldn't be adding some of the things that I'm going to be commenting

things that I'm going to be commenting um on so we're going to do um or

um on so we're going to do um or actually let's do really quick let's

actually let's do really quick let's copy this so that it kind of knows what

copy this so that it kind of knows what we're doing so let's select the

we're doing so let's select the location uh the date the to total

location uh the date the to total cases the new

cases the new cases the

cases the [Music]

[Music] total deaths and then

total deaths and then population uh now where we're at I'm

population uh now where we're at I'm going to turn off my camera because it's

going to turn off my camera because it's going to get it's going to start getting

going to get it's going to start getting in the way to be honest I don't want it

in the way to be honest I don't want it to interfere with your ability to see

to interfere with your ability to see what we're doing on screen so it's been

what we're doing on screen so it's been great seeing you guys I'm going to turn

great seeing you guys I'm going to turn this off and we will continue from here

this off and we will continue from here all right that should be turned off so

all right that should be turned off so let's keep running so this is what we're

let's keep running so this is what we're doing let's actually let's keep this

doing let's actually let's keep this going because I I don't like things not

going because I I don't like things not being

being organized um so we have our location oh

organized um so we have our location oh no we want to do one two we want to do

no we want to do one two we want to do it based off the location and the date

it based off the location and the date makes things everything easier I promise

makes things everything easier I promise you so we're going to be the first one's

you so we're going to be the first one's obviously Afghanistan here's our date we

obviously Afghanistan here's our date we have our total cases are new cases total

have our total cases are new cases total deaths and population so really quick

deaths and population so really quick I'm just going to scroll down just a

I'm just going to scroll down just a second um they started having you know

second um they started having you know the the total deaths it's um it started

the the total deaths it's um it started about a

about a month after they got their first case it

month after they got their first case it looks like so and then it just like

looks like so and then it just like ramps up a lot um and we're going to be

ramps up a lot um and we're going to be diving into all these numbers what they

diving into all these numbers what they mean how to you can do some really

mean how to you can do some really simple calculations on them um but

simple calculations on them um but really quickly we're just going to do

really quickly we're just going to do again a super simple calculation um and

again a super simple calculation um and one that we do multiple times for

one that we do multiple times for different things um so let's go right

different things um so let's go right down here and let's say uh we're going

down here and let's say uh we're going to be looking at the total

to be looking at the total cases

cases versus total deaths so how many cases

versus total deaths so how many cases are there in this country and then how

are there in this country and then how many deaths do they have per um uh you

many deaths do they have per um uh you know how many deaths they have for their

know how many deaths they have for their entire cases so let's say they have a

entire cases so let's say they have a thousand people who H who've been

thousand people who H who've been diagnosed they had 10 people who died

diagnosed they had 10 people who died what's the percentage of people who died

what's the percentage of people who died who had um who had it

who had um who had it so uh let's go right down here and we're

so uh let's go right down here and we're gonna I'm just going to copy this really

gonna I'm just going to copy this really quick this just going to make our life

quick this just going to make our life easier I think you should do the same as

easier I think you should do the same as well um so we have location date total

well um so we have location date total cases um and we're going to get rid of

cases um and we're going to get rid of our new cases we don't need that one in

our new cases we don't need that one in this query right here uh nor do you need

this query right here uh nor do you need this population so let's work on our

this population so let's work on our calculation really quick it should be

calculation really quick it should be super super easy let me make sure I'm

super super easy let me make sure I'm still recording perfect oh man we're 25

still recording perfect oh man we're 25 almost 25 minutes in um or more because

almost 25 minutes in um or more because I have the

I have the intro so now we're going to do uh we

intro so now we're going to do uh we want to know the percentage of people

want to know the percentage of people who are dying who actually get infected

who are dying who actually get infected or or or or who um report being infected

or or or or who um report being infected so we're going to do um total underscore

so we're going to do um total underscore deaths we'll go right down here and

deaths we'll go right down here and we're going to divide that by the total

we're going to divide that by the total cases total cases and if we do this

cases total cases and if we do this really

really quick um what it's going to have and

quick um what it's going to have and well let's go down to where there's

well let's go down to where there's actually

actually numbers so we have 34 we have one um

numbers so we have 34 we have one um it's it's showing

it's it's showing 0.029% if you ever try to get a

0.029% if you ever try to get a percentage of something you have to

percentage of something you have to multiply times 8 100 um so let's do that

multiply times 8 100 um so let's do that really quick all we have to add is the

really quick all we have to add is the what's that the asteris sign um times

what's that the asteris sign um times 100 um and while we're here let's just

100 um and while we're here let's just add the um what's it called Alias Let's

add the um what's it called Alias Let's do let's call this death percentage I

do let's call this death percentage I don't know that that works for me and

don't know that that works for me and let's take a look at this it'll be a

let's take a look at this it'll be a little bit more accurate

little bit more accurate accurate so when there were 34 there was

accurate so when there were 34 there was one and that gives gives us a

one and that gives gives us a 2.94% death rate and we can go down even

2.94% death rate and we can go down even further um and this is still all

further um and this is still all Afghanistan let's go down to the very

Afghanistan let's go down to the very bottom let's go down to the very very

bottom let's go down to the very very bottom so as of as of today yesterday

bottom so as of as of today yesterday there were

there were 59745 total cases in Afghanistan and

59745 total cases in Afghanistan and there were 20 2,625 deaths which is 4%

there were 20 2,625 deaths which is 4% so you have a 4% chance basically right

so you have a 4% chance basically right now of dying I mean if if you want to

now of dying I mean if if you want to look at it like that 4% chance of dying

look at it like that 4% chance of dying if you get it and you live in

if you get it and you live in Afghanistan um let's I mean we you don't

Afghanistan um let's I mean we you don't have to but really quick just to look at

have to but really quick just to look at it further let's look at where the

it further let's look at where the location um I think

location um I think it's let's say like real quick because

it's let's say like real quick because I'm not 100% if it's

I'm not 100% if it's States

States um it should I think it's United

um it should I think it's United States but yeah so I mean I live in the

States but yeah so I mean I live in the United States if you don't you can look

United States if you don't you can look at your country but um you know

at your country but um you know we we this is like this is genuine real

we we this is like this is genuine real reported data so it's really interesting

reported data so it's really interesting um right at the beginning I mean the I

um right at the beginning I mean the I don't know if it was the way we were

don't know if it was the way we were reporting or what but we had really high

reporting or what but we had really high percentage rates um as we go down we're

percentage rates um as we go down we're looking at a 5% 6% I mean this was the

looking at a 5% 6% I mean this was the peak of it this got really bad in the US

peak of it this got really bad in the US um maybe get I hope it gets better um

um maybe get I hope it gets better um how many are we at this is I'm going to

how many are we at this is I'm going to go to the end of this year we sitting at

go to the end of this year we sitting at around 2 to

around 2 to 3% um um yeah it goes down to under 2%

3% um um yeah it goes down to under 2% so at the end of at the end of the year

so at the end of at the end of the year we were looking at over 2 million people

we were looking at over 2 million people that's 2

that's 2 million no wait 20 million

million no wait 20 million 9363 wait wait wait 20 million people

9363 wait wait wait 20 million people who have been

who have been infected um that's a lot that's a lot of

infected um that's a lot that's a lot of 20 million people who have had it 35,000

20 million people who have had it 35,000 or 352,000 deaths by the end of the year

or 352,000 deaths by the end of the year that's a lot um let's keep

that's a lot um let's keep going um and at the very end we had over

going um and at the very end we had over 32

m346fa um there's a lot of deaths 576,000 and I verified this number um I

576,000 and I verified this number um I Googled it Google knows all I googled

Googled it Google knows all I googled this number and it's pretty accurate um

this number and it's pretty accurate um and it's really sad that's a lot of lot

and it's really sad that's a lot of lot of lives um and that's

of lives um and that's 1.78% so as of right now if you're were

1.78% so as of right now if you're were to get it today a estimate is around one

to get it today a estimate is around one uh and three fourest to 2% chance that

uh and three fourest to 2% chance that you're that you could die from it um so

you're that you could die from it um so really interesting numbers this is the

really interesting numbers this is the kind of exploratory stuff that that you

kind of exploratory stuff that that you know we're going to be doing we're going

know we're going to be doing we're going to get a lot more advanced as we go on

to get a lot more advanced as we go on but this shows you know the likelihood

but this shows you know the likelihood um and we can I'm going to write that

um and we can I'm going to write that shows the likely I hope I'm spelling

shows the likely I hope I'm spelling this right I'm not spelling this right

this right I'm not spelling this right likelihood I hope that's right if this

likelihood I hope that's right if this not I apologize likelihood

not I apologize likelihood of dying if you

of dying if you contract uh covid in your

contract uh covid in your country um again rough estimates but you

country um again rough estimates but you know just glancing at the data that's

know just glancing at the data that's kind of what we're looking at um now

kind of what we're looking at um now we're going to look at and let's go down

we're going to look at and let's go down here let's look

here let's look at looking at the total cases versus the

at looking at the total cases versus the population again we're going to do a lot

population again we're going to do a lot of this like percentage stuff um it it's

of this like percentage stuff um it it's pretty simple um that will only last for

pretty simple um that will only last for so long I promise you but it'll be

so long I promise you but it'll be really I'm going to keep it on the

really I'm going to keep it on the states just because um I'm going to be

states just because um I'm going to be looking at that one the most because

looking at that one the most because obviously it's pretty relevant to me um

obviously it's pretty relevant to me um so if you're in another country filter

so if you're in another country filter by your country you'll be really

by your country you'll be really interested in the stats I I know I was

interested in the stats I I know I was really really really um shocked by a lot

really really really um shocked by a lot of the things that we're going to find

of the things that we're going to find today so we're going to keep the

today so we're going to keep the location we're going

location we're going to we're going to keep the date keep the

to we're going to keep the date keep the total cases um but let's change this to

total cases um but let's change this to population and then instead of um the

population and then instead of um the total cases being here we're going to

total cases being here we're going to put the total cases there and then

put the total cases there and then change this to population so what is

change this to population so what is this going to do for us this is going to

this going to do for us this is going to show us what percentage of the

show us what percentage of the population has gotten covid so shows

population has gotten covid so shows what

what percentage of population

percentage of population oops got covid um some of these things

oops got covid um some of these things again they're they're good to know um

again they're they're good to know um the one that I upload to

the one that I upload to GitHub will have the notes that I

GitHub will have the notes that I recommend keeping um again not

recommend keeping um again not everything in here is um not everything

everything in here is um not everything in here is

in here is what you know you need to have in there

what you know you need to have in there this is mostly just you know what I

this is mostly just you know what I think you guys need to see while we're

think you guys need to see while we're actually typing this out all right so

actually typing this out all right so let's take a look at this um actually I

let's take a look at this um actually I want to change this I want to put this

want to change this I want to put this right here just as easier for me

right here just as easier for me visually um just for because the total

visually um just for because the total cases right here so our our population

cases right here so our our population in the US is around

in the US is around 331

331 million um

million um so at the beginning when we had one case

so at the beginning when we had one case I mean it's like nothing let's keep

I mean it's like nothing let's keep scrolling um and see where we get to 1%

scrolling um and see where we get to 1% so

so 1% that's

1% that's 3,311

3,311 32 uh people and that happened in what

32 uh people and that happened in what is that August August of last year so 1%

is that August August of last year so 1% of the population let's keep going all

of the population let's keep going all the way down again we're just kind of

the way down again we're just kind of glancing at this we're about 10% um

glancing at this we're about 10% um again we're at the that 32 million so

again we're at the that 32 million so 10% of the population has has gotten it

10% of the population has has gotten it gotten a test and it's been confirmed so

gotten a test and it's been confirmed so really

really interesting um you know we'll come back

interesting um you know we'll come back to that one I'm sure in the future I you

to that one I'm sure in the future I you know we might make we might use this one

know we might make we might use this one as

as like um a visualization again uh I'm

like um a visualization again uh I'm only looking at the states or United

only looking at the states or United States right now but you know think

States right now but you know think about it in terms of how we're going to

about it in terms of how we're going to visualize this in the future cuz a lot

visualize this in the future cuz a lot of what we're doing

of what we're doing we're going to visualize in the future

we're going to visualize in the future um in Tableau I have Tableau even open

um in Tableau I have Tableau even open right here you can see I have a map um

right here you can see I have a map um this is just a super I threw this

this is just a super I threw this together in like two seconds um we have

together in like two seconds um we have the uh we have the location and so you

the uh we have the location and so you know this is like our future this is

know this is like our future this is what you need to be envisioning when

what you need to be envisioning when you're looking at this data so we have

you're looking at this data so we have you know Afghanistan and let's just

you know Afghanistan and let's just scroll through bellaro and Bolivia and

scroll through bellaro and Bolivia and Bulgaria and cambod all the every single

Bulgaria and cambod all the every single country um that that is reporting so

country um that that is reporting so we're just looking at the states but

we're just looking at the states but remember all of these are going to be

remember all of these are going to be used so just something to remember um I

used so just something to remember um I want to know and I'm really curious as

want to know and I'm really curious as to what countries have the highest um

to what countries have the highest um infection rates compared to the

infection rates compared to the population so we're just looking at our

population so we're just looking at our population um up here um how are we

population um up here um how are we going to do this we'll do actually let

going to do this we'll do actually let me say well let me write it out really

me say well let me write it out really quick so let's look looking at

quick so let's look looking at countries with highest infection rate

countries with highest infection rate compared to population so that's what

compared to population so that's what this script is going to do or this query

this script is going to do or this query is going to do I'm going to copy

is going to do I'm going to copy this um so we're going to keep the

this um so we're going to keep the location we are not going to keep the

location we are not going to keep the date this is not going to be date

date this is not going to be date specific this just going to be

specific this just going to be overall and then we're going to look at

overall and then we're going to look at the max of the total cases so we only

the max of the total cases so we only want to look at the highest so when when

want to look at the highest so when when we were looking at the us we had 32

we were looking at the us we had 32 million we don't want to look at every

million we don't want to look at every single Pop um uh of the total cases we

single Pop um uh of the total cases we only look at the very highest one so

only look at the very highest one so we'll look at the Max total

we'll look at the Max total cases um and let's right here we'll just

cases um and let's right here we'll just say give it an alias at least something

say give it an alias at least something to recognize it so highest U I guess we

to recognize it so highest U I guess we can say infection count so we'll say

can say infection count so we'll say highest infection count that's the

highest infection count that's the highest infection count per country um

highest infection count per country um so per location

so per location um and then we want to also take because

um and then we want to also take because it's going to it's not going since we

it's going to it's not going since we don't have Max total cases here if we

don't have Max total cases here if we just kept total cases here it'll give us

just kept total cases here it'll give us the same one that we were looking at in

the same one that we were looking at in this above query what we need to do is

this above query what we need to do is we need to look at the max of this um so

we need to look at the max of this um so we're going to look at

we're going to look at Max and just add a parentheses there um

Max and just add a parentheses there um and we'll look at this isn't the death

and we'll look at this isn't the death percentage anymore I forgot to change it

percentage anymore I forgot to change it in this last one this is

in this last one this is is what is this it's percent of

is what is this it's percent of population

population infected so let's change that for both

infected so let's change that for both of these because I don't want to get

of these because I don't want to get confused when you're looking at the

confused when you're looking at the column headers later um so we'll look at

column headers later um so we'll look at the percent of population infected let's

the percent of population infected let's run this and see what we

run this and see what we get uh list is not contained in either

get uh list is not contained in either the aggregate oh I need to add a group

the aggregate oh I need to add a group ey of course um so let's add Group

ey of course um so let's add Group by um and we need to group by both the

by um and we need to group by both the population and the location so let's try

population and the location so let's try that really quick let's see if this

that really quick let's see if this works

works awesome um well we ordered on location

awesome um well we ordered on location and population but I really want to look

and population but I really want to look at the

at the highest um so let's so let's just see

highest um so let's so let's just see really quick look at some of these

really quick look at some of these numbers got like 1% 4% um 10% okay so

numbers got like 1% 4% um 10% okay so yeah yeah what we want to do is order on

yeah yeah what we want to do is order on um this percent population infected so

um this percent population infected so let's go ahead and do that uh and let's

let's go ahead and do that uh and let's do that

do that descending so the descending gets the

descending so the descending gets the highest number

highest number first um my goodness 177% so what

first um my goodness 177% so what percentage of your population has gotten

percentage of your population has gotten covid it's been reported and and and um

covid it's been reported and and and um we can see that now so the very first

we can see that now so the very first one small population so it doesn't

one small population so it doesn't surprise me but if you look right down

surprise me but if you look right down here here so that's that 32 million that

here here so that's that 32 million that we were talking about that's that Max of

we were talking about that's that Max of total

total cases um which is the the highest number

cases um which is the the highest number of our infection count so we have 33 so

of our infection count so we have 33 so we're at I mean we're we're right up

we're at I mean we're we're right up there on the list let's look for other

there on the list let's look for other large countries I mean it's us you know

large countries I mean it's us you know there's Israel there's

there's Israel there's Belgium Portugal France so you know

Belgium Portugal France so you know we're up almost to about 10% in a lot of

we're up almost to about 10% in a lot of these countries

these countries so some some of us including the United

so some some of us including the United States we are we are in there as well

States we are we are in there as well some of us has have really high

some of us has have really high percentage rates we just did not keep it

percentage rates we just did not keep it under control um and you know a large

under control um and you know a large amount of the population has gotten it

amount of the population has gotten it that's what this one shows um now let's

that's what this one shows um now let's look uh kind of at the sad side of

look uh kind of at the sad side of things we were just looking at how many

things we were just looking at how many people were infected let's look at how

people were infected let's look at how many people actually died um so let's

many people actually died um so let's do let's comment and we'll say this is

do let's comment and we'll say this is going to this is

going to this is showing the

showing the countries with the let's do

countries with the let's do highest high am I spelling that right

highest high am I spelling that right yeah highest death count per

yeah highest death count per population um now how are we going to do

population um now how are we going to do this let's copy this off the bat but I

this let's copy this off the bat but I don't know if we're going to do it the

don't know if we're going to do it the exact same way because we just need

exact same way because we just need location um and not much else honestly

location um and not much else honestly so let's get rid of all this stuff but

so let's get rid of all this stuff but we do need we're looking at the highest

we do need we're looking at the highest death count so like we did up here with

death count so like we did up here with the Max total cases we're going to do

the Max total cases we're going to do Max and then we'll do total

Max and then we'll do total deaths I hope it's like this total

deaths I hope it's like this total deaths um and then we'll do as total

deaths um and then we'll do as total oops total death

oops total death count um and we'll order that by the

count um and we'll order that by the total death count see I don't need

total death count see I don't need this I think yeah I need to group by

this I think yeah I need to group by because there's an aggregate function

because there's an aggregate function and let's try this really

and let's try this really quick okay so if you're getting this

quick okay so if you're getting this there's a there's a simple slash

there's a there's a simple slash confusing explanation to

confusing explanation to this total deaths right now let's go

this total deaths right now let's go into our covid deaths

into our covid deaths columns okay let's show the total deaths

columns okay let's show the total deaths which is right

which is right here it's an nvar chart 255 it's an

here it's an nvar chart 255 it's an issue with the data type um oh wait

issue with the data type um oh wait total deaths no total deaths right here

total deaths no total deaths right here it's an issue with the data type um it

it's an issue with the data type um it just has to do with how the data type is

just has to do with how the data type is read when you use this aggregate

read when you use this aggregate function we need to convert it um or

function we need to convert it um or cast it is what we're actually do we

cast it is what we're actually do we need to cast this as an integer so

need to cast this as an integer so that's red as a numeric um why I cannot

that's red as a numeric um why I cannot 100% give you a perfect explanation for

100% give you a perfect explanation for it but this happens all the time you

it but this happens all the time you just need to look at the data and

just need to look at the data and realize oh it's probably because of this

realize oh it's probably because of this data type let's try something else um

data type let's try something else um and then it'll work so let's cast this

and then it'll work so let's cast this and we're in casting it I find is just

and we're in casting it I find is just easier but just as int boom there you go

easier but just as int boom there you go so now we're taking this nvar chart 255

so now we're taking this nvar chart 255 over here and then we are converting it

over here and then we are converting it to an

to an integer now let's run this um and let's

integer now let's run this um and let's get rid of this just for visual visual

get rid of this just for visual visual purposes now we are much more

purposes now we are much more accurate but we have a slight issue or

accurate but we have a slight issue or we're we're now seeing a slight issue

we're we're now seeing a slight issue with our

with our data in our data in the location section

data in our data in the location section we have a few ones that really shouldn't

we have a few ones that really shouldn't be there ones like world or

be there ones like world or Africa um or South America these are

Africa um or South America these are grouping entire

grouping entire continents so let's go back up to our um

continents so let's go back up to our um let's go back up here and let's do

let's go back up here and let's do actually let's pull it up really quick

actually let's pull it up really quick because this is just part of exploring

because this is just part of exploring the data and figuring it out so if we

the data and figuring it out so if we scroll down um we're going to f we're

scroll down um we're going to f we're going to see one like right where is it

going to see one like right where is it right here this this location is all of

right here this this location is all of Asia whereas in other ones the continent

Asia whereas in other ones the continent is Asia if I can pull one up real quick

is Asia if I can pull one up real quick so like right here the continent is Asia

so like right here the continent is Asia whereas before the location is Asia but

whereas before the location is Asia but if you also notice um the continent is

if you also notice um the continent is null here so what we need to do is say

null here so what we need to do is say um uh where

um uh where continent is not null because when it is

continent is not null because when it is null that means that this location is

null that means that this location is actually an entire continent and we

actually an entire continent and we don't want that um that may be helpful

don't want that um that may be helpful for us um later on but it is not helpful

for us um later on but it is not helpful now so now this right here will get rid

now so now this right here will get rid of that um and just knowing that

of that um and just knowing that figuring that out now we can add that to

figuring that out now we can add that to every every

every every script um and we can do you know you

script um and we can do you know you don't have to do this I'm just doing

don't have to do this I'm just doing this for you know visual purposes I'm

this for you know visual purposes I'm not going to do that for everyone um so

not going to do that for everyone um so let's say where continent is not null

let's say where continent is not null and now let's look at this and now you

and now let's look at this and now you can see that the United States is number

can see that the United States is number one and

one and so number one is not the best thing to

so number one is not the best thing to be number one in but we have a death

be number one in but we have a death count of 576,000 and again I I googled

count of 576,000 and again I I googled this earlier these numbers are pretty

this earlier these numbers are pretty accurate there some of them are like a

accurate there some of them are like a day or two behind give me a second I'm

day or two behind give me a second I'm going to take a

water they're like a couple days behind um this number is actually higher um and

um this number is actually higher um and as you know as we continue to have more

as you know as we continue to have more people die unfortunately that number

people die unfortunately that number just continues to go up um so the data

just continues to go up um so the data that that you download may be a a lot

that that you download may be a a lot higher um as of right now we've been

higher um as of right now we've been breaking everything out by location

breaking everything out by location right really quickly let's just do this

right really quickly let's just do this by something we kind of saw earlier um

by something we kind of saw earlier um and I'm just going to do this for

and I'm just going to do this for breaking it up purposes but I'm going to

breaking it up purposes but I'm going to say I'm going do caps lock let's break

say I'm going do caps lock let's break things down by continent how SP

things down by continent how SP continent

continent Contin jeez is that even how you spell

Contin jeez is that even how you spell it I don't even know let's keep going um

it I don't even know let's keep going um but now we can do consonant right

but now we can do consonant right here and we'll just copy and paste that

here and we'll just copy and paste that let's get that back up here um and now

let's get that back up here um and now we can see where continent is not

we can see where continent is not null let's see if that makes that yeah

null let's see if that makes that yeah okay so now it's breaking it out by

okay so now it's breaking it out by continents um with North America South

continents um with North America South America Asia Europe Africa

America Asia Europe Africa Oceana is this

Oceana is this perfect no no it's not perfect um North

perfect no no it's not perfect um North America looks like it's only including

America looks like it's only including the numbers from the United States and

the numbers from the United States and not Canada um so we have some small

not Canada um so we have some small issues in here um but for the purposes

issues in here um but for the purposes of what we're trying to do which I don't

of what we're trying to do which I don't think anyone's going going to come in

think anyone's going going to come in here and fact check us or check the data

here and fact check us or check the data they may and then you're I don't know

they may and then you're I don't know you might be screwed but for the

you might be screwed but for the purposes of

purposes of hierarchy um and you know drill that

hierarchy um and you know drill that drill down effect in Tableau which is

drill down effect in Tableau which is something we are going to do we want

something we are going to do we want want to start including this continent

want to start including this continent in our in our queries so that we can

in our in our queries so that we can drill down um further into these things

drill down um further into these things um we can also do where just wait I'm

um we can also do where just wait I'm going to do where

going to do where isnull um actually let me see so before

isnull um actually let me see so before we were doing work continent is not null

we were doing work continent is not null but let's do location I'm just I I'm

but let's do location I'm just I I'm doing this on the Fly I haven't done

doing this on the Fly I haven't done this before I just kind of

this before I just kind of am doing

am doing this um this actually is the correct

this um this actually is the correct numbers

numbers and I don't know why I didn't do this

and I don't know why I didn't do this before when I was actually creating this

before when I was actually creating this project but now this is a wonderful

project but now this is a wonderful beautiful thing I believe this is the

beautiful thing I believe this is the correct numbers um I could verify but I

correct numbers um I could verify but I don't want to do that live because I I

don't want to do that live because I I might look stupid but I think this is

might look stupid but I think this is accurate um remember before we were

accurate um remember before we were looking at the location and the location

looking at the location and the location um and it was actually the countries

um and it was actually the countries itself and then there were ones where we

itself and then there were ones where we did where is notnull to get rid of all

did where is notnull to get rid of all the ones that were like world and all

the ones that were like world and all those other things well now I'm just

those other things well now I'm just filtering on those instead of deleting

filtering on those instead of deleting them before we were looking at

them before we were looking at everything but these now we're only

everything but these now we're only looking at these and these numbers look

looking at these and these numbers look a lot more accurate so with that being

a lot more accurate so with that being said um I'm going to use this going

said um I'm going to use this going forward in my script so I'm going to

forward in my script so I'm going to kind of change things up to where from

kind of change things up to where from what I originally had um let me see

what I originally had um let me see though because if that is the case it

though because if that is the case it may screw up our drill down effect um

may screw up our drill down effect um which is highly unfortunate I may I I

which is highly unfortunate I may I I honestly might just revert back to it

honestly might just revert back to it for the pure fact that we want the

for the pure fact that we want the visualizations to look correct um just

visualizations to look correct um just know that this is the right way and if

know that this is the right way and if you want to go back and do that I highly

you want to go back and do that I highly encourage that I didn't figure that out

encourage that I didn't figure that out my first time around um but I'm willing

my first time around um but I'm willing to admit when I'm wrong let me see what

to admit when I'm wrong let me see what let me do a time check all we're run

let me do a time check all we're run like 50 minutes or so I think we're

like 50 minutes or so I think we're gonna we're just going to keep going all

gonna we're just going to keep going all the way through I I I don't think we're

the way through I I I don't think we're going to stop um I don't think we're

going to stop um I don't think we're going to stop in this project so we want

going to stop in this project so we want to do some of the the above queries were

to do some of the the above queries were kind of what we were going for nothing

kind of what we were going for nothing crazy difficult right nothing crazy hard

crazy difficult right nothing crazy hard um and now we want to we want to start

um and now we want to we want to start breaking this out by um continent as

breaking this out by um continent as well I'm I'm going to go back

well I'm I'm going to go back and is this correct let me look no so is

and is this correct let me look no so is not

not no

no um so we want to start doing some of the

um so we want to start doing some of the above queries but adding that content in

above queries but adding that content in there you can even go back and add that

there you can even go back and add that as well um if you want to that's totally

as well um if you want to that's totally fine I'm going to do some more queries

fine I'm going to do some more queries down

down here um or at least one more one or two

here um or at least one more one or two more and then we're going to start

more and then we're going to start getting I think into some a little bit

getting I think into some a little bit more advanced things we're going to

more advanced things we're going to start getting into some temp tables uh

start getting into some temp tables uh stuff like that because we're going to

stuff like that because we're going to eventually set these up in um views so

eventually set these up in um views so that we have these views to um use for

that we have these views to um use for Tableau

Tableau later um and again it shows you know how

later um and again it shows you know how to create a view so that's important so

to create a view so that's important so we we've we've done this first one this

we we've we've done this first one this next one is going to let me go down one

next one is going to let me go down one more this is

more this is showing the continents with the highest

showing the continents with the highest death count so almost the exact same as

death count so almost the exact same as we did before but now we're looking at

we did before but now we're looking at the continents um we can even go up and

the continents um we can even go up and look at uh just wait we literally just

look at uh just wait we literally just did that

did that um so that's what this one is

um so that's what this one is actually looking at my notes wrong

actually looking at my notes wrong idiot okay perfect um now we you know we

idiot okay perfect um now we you know we want to start looking at this from a

want to start looking at this from a Viewpoint of I'm going to visualize this

Viewpoint of I'm going to visualize this so how do we do that what we want to

so how do we do that what we want to look at let's look at some Global

look at let's look at some Global numbers um you can do as many many of

numbers um you can do as many many of these as you want anything up here just

these as you want anything up here just add continent to it um anything what

add continent to it um anything what like groupy just replace it with

like groupy just replace it with continent and you and you got it um so I

continent and you and you got it um so I don't want to go through and do every

don't want to go through and do every single one of those but that is kind of

single one of those but that is kind of the gist of what you might want to do

the gist of what you might want to do especially if you want that drill down

especially if you want that drill down effect and if you don't know what that

effect and if you don't know what that is um you know it's like clicking on

is um you know it's like clicking on North America and then when you bring up

North America and then when you bring up North America then it shows all the

North America then it shows all the countries in North America so Canada uh

countries in North America so Canada uh and the United States and so it's a

and the United States and so it's a drill down so you like on Africa and

drill down so you like on Africa and then there's all the African countries

then there's all the African countries that's what drilling down does and

that's what drilling down does and that's what you can do when you have um

that's what you can do when you have um those layers so you have the continent

those layers so you have the continent then you have the location um so you

then you have the location um so you know I'm not going to we we'll look at

know I'm not going to we we'll look at that when we actually get to Tableau but

that when we actually get to Tableau but I don't want to actually spend all the

I don't want to actually spend all the time writing that

time writing that out um but what we now want to do is we

out um but what we now want to do is we want to calculate everything for the

want to calculate everything for the across the entire

across the entire world so

world so let's do this let's

let's do this let's say um breaking let's do Global let's

say um breaking let's do Global let's just say global global numbers easier

just say global global numbers easier easier than

easier than nothing

nothing um all

um all right uh I let me really quick find the

right uh I let me really quick find the I think it's probably the first one the

I think it's probably the first one the death percentage let me let me see if

death percentage let me let me see if this is the one that we want

this is the one that we want

[Music] okay let me

see all right so let's take this one I'm sorry that took me a while to find again

sorry that took me a while to find again I'm not cutting any of this stuff out

I'm not cutting any of this stuff out you just got to stick with me you if

you just got to stick with me you if you're sticking with me this long I know

you're sticking with me this long I know you care I know you're not you're not

you care I know you're not you're not cutting away because I'm trying to

cutting away because I'm trying to figure things out on my side so um let

figure things out on my side so um let me get rid of this so this is the exact

me get rid of this so this is the exact same SC what well let's say where just

same SC what well let's say where just so we can get the right

so we can get the right numbers um so we are now going to look

numbers um so we are now going to look at the global numbers uh so we're not

at the global numbers uh so we're not going

going to we're not going to uh include any

to we're not going to uh include any location any continent or anything like

location any continent or anything like that but we do want to make sure that

that but we do want to make sure that we're only looking at all of the um

we're only looking at all of the um countries and we're not looking at the

countries and we're not looking at the world numbers plus all the countries

world numbers plus all the countries because then the numbers would get

because then the numbers would get astronomical so instead of now now we

astronomical so instead of now now we can't do so let's try running this

can't do so let's try running this really

really quick so now

quick so now we really can't do this um because now

we really can't do this um because now it's breaking everything out by

it's breaking everything out by um by you know that uh which is the date

um by you know that uh which is the date it's breaking everything out by the date

it's breaking everything out by the date because um these total case the numbers

because um these total case the numbers are different right so really quick

are different right so really quick let's Group by date

let's Group by date and now let's see what it looks

and now let's see what it looks like uh it's going to give us an error

like uh it's going to give us an error obviously that's because we're looking

obviously that's because we're looking at

at um that's because when we're looking at

um that's because when we're looking at this we're looking at multiple things

this we're looking at multiple things and we can't Group by just the dates

and we can't Group by just the dates obviously if we wanted to group by

obviously if we wanted to group by something which we need to

something which we need to do we then need to um start using

do we then need to um start using aggregate functions on everything

aggregate functions on everything else um so really

else um so really quickly let's do some aggregate

quickly let's do some aggregate functions I'm looking at my notes for

functions I'm looking at my notes for just a second um to see what I

just a second um to see what I did basically what we want to do and I

did basically what we want to do and I think what'll make things

think what'll make things easier is I mean I could try to do the

easier is I mean I could try to do the sum of Max total cases I don't think

sum of Max total cases I don't think that's possible um let me comment this

that's possible um let me comment this out really

quick yeah um it's because there's an aggregate function within an aggregate

aggregate function within an aggregate fun function and we can't really do that

fun function and we can't really do that um if we go back to the data and you we

um if we go back to the data and you we kind of looked at this earlier there's

kind of looked at this earlier there's one called new

one called new cases um let's use this because instead

cases um let's use this because instead of doing Max we can just sum it or or or

of doing Max we can just sum it or or or do a sum on it and that's going to give

do a sum on it and that's going to give us the sum of all the new cases which

us the sum of all the new cases which adds up to the total cases so if we do

adds up to the total cases so if we do this let's see this will give us on each

this let's see this will give us on each day the total across the world because

day the total across the world because we're not filtering by any continent or

we're not filtering by any continent or or we're filtering out um like the world

or we're filtering out um like the world and in the actual continents we're not

and in the actual continents we're not filtering by location or continent or

filtering by location or continent or anything it's just by date so we're

anything it's just by date so we're looking at the sum of the new

looking at the sum of the new cases so now let's

cases so now let's do uh let's do the

do uh let's do the [Music]

[Music] sum

sum of uh new underscore

of uh new underscore deaths and we can run that

one um operating data type and our chart is invalid for the some operator so

is invalid for the some operator so going back um and this is something I

going back um and this is something I encountered a lot when I was doing this

encountered a lot when I was doing this is these new cases is a float which is

is these new cases is a float which is why it's working in the sum but the new

why it's working in the sum but the new deaths is an narar so what we need to do

deaths is an narar so what we need to do again is cast that as an integer it's

again is cast that as an integer it's just the easiest thing to

just the easiest thing to do um and now that one should

do um and now that one should work so um let's get rid of the well

work so um let's get rid of the well let's get rid of down to here so we're

let's get rid of down to here so we're we're about to do another one and that's

we're about to do another one and that's going to be our death percentage

going to be our death percentage globally across um across the I guess

globally across um across the I guess the

the world so we need to do the sum of I

world so we need to do the sum of I think it's we need to do new

deaths all right divided by the sum

by the sum of new

of new [Music]

[Music] cases all right times

cases all right times 100 let's see what this takes

100 let's see what this takes us um okay of course we're getting the

us um okay of course we're getting the same thing let me

same thing let me um let me put this right

um let me put this right here and see if this

here and see if this works um

works um invalid data oh that's because this was

invalid data oh that's because this was new cases

new cases the new deaths one is right

the new deaths one is right here and let's run

here and let's run this and now we

this and now we are looking good um and as you can see

are looking good um and as you can see the death percentage is right here we

the death percentage is right here we have

have 91 um and let me give these I don't we

91 um and let me give these I don't we can't let me go back real quick and just

can't let me go back real quick and just say as total

say as total cases as as

cases as as total

total deaths um and let's run that

again okay and so across the world these are our numbers so we have total cases

are our numbers so we have total cases on that very first day that cases were

on that very first day that cases were starting to be

starting to be reported there were 98 total cases there

reported there were 98 total cases there was one total deaths that gives us a

was one total deaths that gives us a death percentage of 1% across the

death percentage of 1% across the country

country or across the world and as we scroll

or across the world and as we scroll down it gets lower and lower and that's

down it gets lower and lower and that's cuz we have a lot of people who have

cuz we have a lot of people who have gotten infected are the total cases um

gotten infected are the total cases um and again that's per day right so if we

and again that's per day right so if we remove this all together that date Al

remove this all together that date Al together which we can do right

now this will uh this will give us the total

will uh this will give us the total cases which is oh gosh let me read this

cases which is oh gosh let me read this through one two 150

through one two 150 million um versus

million um versus 3,180 26 so overall across the world we

3,180 26 so overall across the world we are looking at a um a death percentage

are looking at a um a death percentage of a little over

2% so interesting numbers you can keep both of those queries separate if you'd

both of those queries separate if you'd like um you know they might come in

like um you know they might come in handy

handy later but let's do

later but let's do this

this so we

so we have

have um give me one second check on my notes

um give me one second check on my notes again because I just want to make sure

again because I just want to make sure I'm not doing something

right all right so again we have a whole another table that we haven't used yet

another table that we haven't used yet uh it's this covid

uh it's this covid vaccinations um and just to you know

vaccinations um and just to you know refresh your memory let's do um let's

refresh your memory let's do um let's look at the table from portfolio

look at the table from portfolio project. Co vaccinations let's jog our

project. Co vaccinations let's jog our memory on what we got

memory on what we got here so we have

here so we have um we have these tests we have um

um we have these tests we have um vaccinations over here which was what

vaccinations over here which was what we're actually going to be

we're actually going to be using

using um excuse me

um excuse me me uh that's what we are going to be

me uh that's what we are going to be using so let's join these two tables

using so let's join these two tables together uh and let's let's actually

together uh and let's let's actually just

just do from actually let's just do this

do from actually let's just do this whole

whole thing from let's do covid deaths and

thing from let's do covid deaths and here's how we're going to join it so

here's how we're going to join it so we're going to say join and we're going

we're going to say join and we're going to say oops wait that is wrong join and

to say oops wait that is wrong join and we're going to say on so what are we

we're going to say on so what are we going to join them on um we're going to

going to join them on um we're going to join them on two things we're going to

join them on two things we're going to join them on location because that's

join them on location because that's much more specific than the continent

much more specific than the continent we're going to join them on location and

we're going to join them on location and we're going to join them on date let's

we're going to join them on date let's call this one DEA let's call this one

call this one DEA let's call this one vaccination so a little Alias for these

vaccination so a little Alias for these so that we don't have to type out this

so that we don't have to type out this entire table name each time so let's do

entire table name each time so let's do dea. location

dea. location is equal to

is equal to vac.

vac. location and da do and we'll say date is

location and da do and we'll say date is equal to

equal to vac. date and let's just see what we get

vac. date and let's just see what we get really quick so we'll have all of these

really quick so we'll have all of these things and let's look at Granada

things and let's look at Granada 0717 let's go all the way over

0717 let's go all the way over here and it should

here and it should have Gren

have Gren 0717 so just making sure that they were

0717 so just making sure that they were joined

joined correctly um for this query what we're

correctly um for this query what we're going to do is look at the total

going to do is look at the total population and let's do that right here

population and let's do that right here so looking at total population versus

so looking at total population versus vaccination so how many PE what is the

vaccination so how many PE what is the total amount of people in the world that

total amount of people in the world that have been vaccinated that is that is

have been vaccinated that is that is what we're going to do in this query

what we're going to do in this query right here so

right here so let's do

let's do dea.

continent location uh da. date again these are

location uh da. date again these are going to be the same in either one but

going to be the same in either one but we have to specify um let me just for

we have to specify um let me just for example if we do population population

example if we do population population oh actually that's a terrible example um

oh actually that's a terrible example um because population's only in one let me

because population's only in one let me go back real quick let me say I only

go back real quick let me say I only write date that's going to give me an

write date that's going to give me an error because there's date in both of

error because there's date in both of them in fact we joined it on them so we

them in fact we joined it on them so we know there's date in both of them so

know there's date in both of them so it's going to give us an error we just

it's going to give us an error we just have to specify what table we want to

have to specify what table we want to pull it from so we going to do

pull it from so we going to do DEA um and da. population just to keep

DEA um and da. population just to keep it consistent um and now we're going to

it consistent um and now we're going to add the next one da do and let's do new

add the next one da do and let's do new vaccinations um and really quick let's

vaccinations um and really quick let's just look at

this um and let me get my orders cu I want it to be organized I I

orders cu I want it to be organized I I actually one let's do one two three I

actually one let's do one two three I don't like it when it's not organized it

don't like it when it's not organized it bothers

bothers me so we're looking at oh no I also need

me so we're looking at oh no I also need to add or consonant is not

to add or consonant is not [Music]

[Music] null there we

null there we go uh

go uh da perfect now let's run this this

da perfect now let's run this this should look much better there we go all

should look much better there we go all right we are in fact if we want to look

right we are in fact if we want to look at Afghanistan like we have normally

at Afghanistan like we have normally been doing um in previous ones we do two

been doing um in previous ones we do two slash3 so there's our population here's

slash3 so there's our population here's our new

our new vaccinations

vaccinations now let's

now let's see we're going to go back go down and

see we're going to go back go down and let's see they have vaccinations

let's see they have vaccinations starting on

starting on 218 um if we go even further down let's

218 um if we go even further down let's just go

just go to who's this Canada oh yeah Canada

to who's this Canada oh yeah Canada would be a good one to look at they

would be a good one to look at they started doing vaccinations

started doing vaccinations on right here so 12:15 I mean they

on right here so 12:15 I mean they started very

started very early and their numbers only increased

early and their numbers only increased and now they're you know doing this is

and now they're you know doing this is per day right so this is 288,000 in one

per day right so this is 288,000 in one day um so that's you know really high

day um so that's you know really high numbers but this is the number of new

numbers but this is the number of new vaccinations um there is a column called

vaccinations um there is a column called total vaccinations in this table but

total vaccinations in this table but we're going to do something pretty just

we're going to do something pretty just to display again this whole portfolio

to display again this whole portfolio project is to show potential employers

project is to show potential employers that you know how to do certain things

that you know how to do certain things so I want to set up opportunities to do

so I want to set up opportunities to do that we're not going to use the total

that we're not going to use the total vaccinations we're going to use this new

vaccinations we're going to use this new vaccinations which is new vaccinations

vaccinations which is new vaccinations per

per day um so we want to we want to know or

day um so we want to we want to know or do kind of like a rolling count um out

do kind of like a rolling count um out here so as this number let me go back to

here so as this number let me go back to the beginning as this number increases

the beginning as this number increases 718 2300 4179 we want it to add up over

718 2300 4179 we want it to add up over here it's a pretty cool thing I mean you

here it's a pretty cool thing I mean you know it's once you see it you'll be like

know it's once you see it you'll be like oh that's pretty easy but you know we're

oh that's pretty easy but you know we're going to be using partition bu we're

going to be using partition bu we're going to be using

going to be using um uh this a Windows function so it's

um uh this a Windows function so it's really good to to Showcase I think so

really good to to Showcase I think so we're going to do

um and let's do um we need to do the sum because

do um we need to do the sum because we're going to be adding these together

we're going to be adding these together so we need to do the sum of new

so we need to do the sum of new vaccinations

vaccinations oops do the sum of new

oops do the sum of new vaccinations let's do

vaccinations let's do over and we're going to say partition oh

over and we're going to say partition oh gosh Partition by and

gosh Partition by and we need to Partition by the location

we need to Partition by the location first and foremost because we're

first and foremost because we're breaking it up by if we do it by

breaking it up by if we do it by continent the numbers are going to be

continent the numbers are going to be completely off we need to do it by

completely off we need to do it by location location and and also partly

location location and and also partly the date but you'll see that in just a

the date but you'll see that in just a second but we need to partition it by

second but we need to partition it by breaking it up by um

breaking it up by um location and why is that because every

location and why is that because every time it gets to a new location we want

time it gets to a new location we want the count to start over we we don't want

the count to start over we we don't want this aggregate function to just keep

this aggregate function to just keep running and running running it'll ruin

running and running running it'll ruin all of our numbers we only want the this

all of our numbers we only want the this part a partition on the the location so

part a partition on the the location so that it runs only through Canada and

that it runs only through Canada and then when it gets to the next country it

then when it gets to the next country it doesn't keep going um and if we only did

doesn't keep going um and if we only did that by the way let's look at what this

that by the way let's look at what this looks like uh okay real quick I need to

looks like uh okay real quick I need to cast

cast this um as an integer like we've been

this um as an integer like we've been doing in the past you can also do um

doing in the past you can also do um real quick I want to show you another

real quick I want to show you another one convert and I think

one convert and I think it's comma

it's comma [Music]

[Music] integer um or is it integer comma let me

integer um or is it integer comma let me try integer comma I think it's that way

try integer comma I think it's that way actually um and you can do it this way

actually um and you can do it this way as well that is up to you um you know

as well that is up to you um you know either one is totally fine if you want

either one is totally fine if you want to use both that's even better because

to use both that's even better because then it kind of shows you can do both um

then it kind of shows you can do both um but they basically do the exact same

but they basically do the exact same thing so let's go

thing so let's go down and let's see what what's happening

down and let's see what what's happening here so it goes down to Albania and

here so it goes down to Albania and since we're partitioning on Albania

since we're partitioning on Albania Albania their total amount of

Albania their total amount of vaccinations is 347,000 I know that

vaccinations is 347,000 I know that going into it because it has it on every

going into it because it has it on every single stinking row but down here they

single stinking row but down here they started to add they started to add up

started to add they started to add up right but we didn't do that we only

right but we didn't do that we only partitioned on location so it added it

partitioned on location so it added it did the sum of all the new vaccinations

did the sum of all the new vaccinations by that location so what we need to do

by that location so what we need to do is go over here and say order by and we

is go over here and say order by and we need to order it by both the location

need to order it by both the location oops da.

oops da. location and the date that is very

location and the date that is very important uh the date is what's going to

important uh the date is what's going to separate it out um and you'll see in

separate it out um and you'll see in just a second what I mean so now let's

just a second what I mean so now let's run this and let's go back down to

run this and let's go back down to Albania I think it was so here's Albania

Albania I think it was so here's Albania let's go to our first one so here's what

let's go to our first one so here's what we have we have 60 and it gives us 60

we have we have 60 and it gives us 60 then we add 78 so we add 60 + 78 = 138

then we add 78 so we add 60 + 78 = 138 then 78 + 1 78 sorry 60 + 78 + 42 = 180

then 78 + 1 78 sorry 60 + 78 + 42 = 180 then 60 + 78+ 142 + 61 241 so you get

then 60 + 78+ 142 + 61 241 so you get the point it adds up every single uh

the point it adds up every single uh consecutive one and when there's nulls

consecutive one and when there's nulls or there's zeros it's going to uh not

or there's zeros it's going to uh not anything it's just going to keep it uh

anything it's just going to keep it uh going and then you can see as it's it's

going and then you can see as it's it's a rolling count so we're going to name

a rolling count so we're going to name this let's do

this let's do as

as um let's do as um rolling people

um let's do as um rolling people vaccinated let's call

that um I think that's good now what we want to do is actually

good now what we want to do is actually look at the total population versus the

look at the total population versus the vaccinations um and really what we want

vaccinations um and really what we want to do is use this rolling people

to do is use this rolling people vaccinated we want to use the max number

vaccinated we want to use the max number because at the very bottom is our Max

because at the very bottom is our Max number this is how many people in

number this is how many people in Albania um we want to use that number

Albania um we want to use that number and divide it by the population to know

and divide it by the population to know how many people in that country are

how many people in that country are vaccinated so what we want to do is

vaccinated so what we want to do is we'll do this we'll do rolling people

we'll do this we'll do rolling people vaccinated divided by

vaccinated divided by population times 100 and as you can see

population times 100 and as you can see we're getting an error you can't use a

we're getting an error you can't use a column that you just created to then use

column that you just created to then use the next one so what we need to do is we

the next one so what we need to do is we need to create either a CTE or a temp

need to create either a CTE or a temp table um this is at this is the time of

table um this is at this is the time of of the show of this tutorial whatever

of the show of this tutorial whatever you want to call it where I'm going to

you want to call it where I'm going to give you some options you can do one you

give you some options you can do one you can do

can do both you know there's no preference to

both you know there's no preference to me

me um but we're going to take this and

um but we're going to take this and we're going to at least for this first

we're going to at least for this first one we're going to use a

one we're going to use a CT so we're going to say excuse me we're

CT so we're going to say excuse me we're going to say with and let's call

going to say with and let's call it um pop vers vac I don't

it um pop vers vac I don't know population versus

know population versus vaccination and then all we need to do

vaccination and then all we need to do is specify the um basically the columns

is specify the um basically the columns that we're going to input um so let's

that we're going to input um so let's put as and let's insert that down here

put as and let's insert that down here because what we need to do is we want to

because what we need to do is we want to say

say um we do

um we do continent oh gosh I'm so bad at spelling

continent oh gosh I'm so bad at spelling continent uh

continent uh location date

location date population um and then we'll have this

population um and then we'll have this rolling people vaccinated that should be

it um and let's see if there's we just need to close this parentheses so this

need to close this parentheses so this is our CTE it should be

is our CTE it should be working um actually that's not true I

working um actually that's not true I need an open parenthesis here that's why

need an open parenthesis here that's why it's giving me that

it's giving me that error um let's see it's I'm still

error um let's see it's I'm still getting an error so let me see if I'm

getting an error so let me see if I'm doing something

doing something wrong do

parentheses there and there I say with pop back there continent location

with pop back there continent location date

date population

population ah I believe that is the

ah I believe that is the issue so then we need we just need to

issue so then we need we just need to add that last

add that last column new

column new vaccinations um if the number of columns

vaccinations um if the number of columns in the CTE is different than the number

in the CTE is different than the number of columns here it's going to give you

of columns here it's going to give you an error so you got to make sure um and

an error so you got to make sure um and then let's just say for real for right

then let's just say for real for right now select everything from and we'll do

now select everything from and we'll do and we can even say pop versus vag it'll

and we can even say pop versus vag it'll come up right away so really quickly

come up right away so really quickly let's run this and see what

let's run this and see what happens uh the order by Clause can't be

happens uh the order by Clause can't be in there I knew that but

in there I knew that but whoops let's comment that out

whoops let's comment that out let's get that all the way up here let's

let's get that all the way up here let's run this so now that query that we were

run this so now that query that we were looking at before is now in here but now

looking at before is now in here but now we can actually use it to perform

we can actually use it to perform further calculations um so we'll just do

further calculations um so we'll just do everything

everything comma and then we'll do rolling people

comma and then we'll do rolling people vaccinated uh divided

vaccinated uh divided by and that needs to be

by and that needs to be population time 100 I'm pretty sure this

population time 100 I'm pretty sure this is incorrect give me me a

is incorrect give me me a second um invalid object oh it's because

second um invalid object oh it's because I have to run it with the

I have to run it with the CTE my bad

CTE my bad um so let's look at this percentage

um so let's look at this percentage really quick

really quick um it's not wrong and it's actually

um it's not wrong and it's actually going to give us a rolling number and

going to give us a rolling number and this may actually be what we

this may actually be what we want

um so basically what it's doing is it's taking this column

taking this column and doing it versus this column and so

and doing it versus this column and so this number should only increase because

this number should only increase because as this number increases this number

as this number increases this number will increase because the population

will increase because the population stays stagnant um again I'm kind of

stays stagnant um again I'm kind of looking at this as we go so right now

looking at this as we go so right now 12% of the population in um

12% of the population in um Albania is vaccinated so that you know

Albania is vaccinated so that you know that is that's all we know I don't think

that is that's all we know I don't think we need to go any further than that I

we need to go any further than that I think um if you want to

think um if you want to you can look at the max

you can look at the max one um but you'll have to get rid of

one um but you'll have to get rid of date and just keep the location um

date and just keep the location um population Etc because the date is going

population Etc because the date is going to throw everything

to throw everything off so if that's something you want to

off so if that's something you want to do absolutely do

do absolutely do that um you can use a temp table here uh

that um you can use a temp table here uh we can look at how to do

we can look at how to do that really quickly I think um so that

that really quickly I think um so that you guys know how to do that again I

you guys know how to do that again I recommend throwing in one or two of

recommend throwing in one or two of these um like even up

these um like even up here you can do different um different

here you can do different um different counts and then do one for each um so

counts and then do one for each um so let's do temp

let's do temp table all right so it's going to be a

table all right so it's going to be a lot of the same stuff we're going to

lot of the same stuff we're going to keep

keep this

this and this is going to be what we insert

and this is going to be what we insert so let's say insert into and we need to

so let's say insert into and we need to write where we're inserting it into but

write where we're inserting it into but let's say uh again I'm only doing this

let's say uh again I'm only doing this for it's going to be basically the same

for it's going to be basically the same it's going to have the same effect but

it's going to have the same effect but um with a temp table so uh we're going

um with a temp table so uh we're going to do temp table and let's look

to do temp table and let's look at um let's say let's call percent

at um let's say let's call percent population

population vaccinated and we need to specify our

vaccinated and we need to specify our columns so let's go down here excuse me

columns so let's go down here excuse me let's go down here and let's do the

let's go down here and let's do the basically the exact same thing so

basically the exact same thing so continent

continent I think I spelled that

I think I spelled that right no I didn't spell that right I

right no I didn't spell that right I almost did I got really confident we'll

almost did I got really confident we'll do we and and just so you know for these

do we and and just so you know for these we have to specify the data type as well

we have to specify the data type as well um because we're basically creating like

um because we're basically creating like a genuine table is just a temporary one

a genuine table is just a temporary one so let's do invar Char 255 we'll do um

so let's do invar Char 255 we'll do um location we'll do the same thing and

location we'll do the same thing and barar

barar oops

oops 255 we need to do date and we'll do that

255 we need to do date and we'll do that as date time we'll do

as date time we'll do population and we can do I mean there's

population and we can do I mean there's lots of different ones we can do but

lots of different ones we can do but we'll do numeric for this

we'll do numeric for this example there's new uncore

example there's new uncore vaccinations and let's do that one as

vaccinations and let's do that one as numeric again you can use different

numeric again you can use different things um and then we'll do rolling

things um and then we'll do rolling people

people vaccinated Um this can can be numeric as

vaccinated Um this can can be numeric as well

well um and then we need to insert that into

um and then we need to insert that into here okay so we're inserting the data

here okay so we're inserting the data and then down here we can actually

and then down here we can actually select it and let's let's take

select it and let's let's take this and do right here except we're

this and do right here except we're going to be doing this

going to be doing this by this right here but it hasn't been

by this right here but it hasn't been created yet but it will be created in

created yet but it will be created in just a

just a second okay so you let me see if

second okay so you let me see if yeah so these were the rows that were

yeah so these were the rows that were affected um and and then we got our

affected um and and then we got our actual output from this right here now

actual output from this right here now let's say you wanted to change something

let's say you wanted to change something in here you're like oh you know I I

in here you're like oh you know I I don't want to do it we this let me

don't want to do it we this let me comment that out and then let me do this

comment that out and then let me do this and um create that table again oh no we

and um create that table again oh no we got an error um how can we get around

got an error um how can we get around this very simple I've done this in a I

this very simple I've done this in a I should do this in a different one you

should do this in a different one you can do drop table if exist

can do drop table if exist and then do this right

and then do this right here um and when we run this it

here um and when we run this it should give us our output I highly

should give us our output I highly recommend just adding this especially if

recommend just adding this especially if you plan on making any alterations so

you plan on making any alterations so that when you um run it multiple times

that when you um run it multiple times you don't have to you know go and then

you don't have to you know go and then delete the view or or delete the temp

delete the view or or delete the temp table or drop temp table or you know

table or drop temp table or you know it's just built in it's at the top it's

it's just built in it's at the top it's easy to maintain and it looks good it's

easy to maintain and it looks good it's it's something that that a lot of people

it's something that that a lot of people do and so if you have that at the top of

do and so if you have that at the top of your query and somebody you know

your query and somebody you know somebody who wants to hire you looks at

somebody who wants to hire you looks at this like oh okay that makes sense I'm

this like oh okay that makes sense I'm glad they included that they know what

glad they included that they know what they're doing this guy's smart I should

they're doing this guy's smart I should hire them um now what we're going to do

hire them um now what we're going to do is uh I feel like I've showed you as

is uh I feel like I've showed you as much as I can show you um with the

much as I can show you um with the limited data that we've looked at again

limited data that we've looked at again I could have done this for six hours

I could have done this for six hours straight if I had used all the data at

straight if I had used all the data at least I mean there's just so much data

least I mean there's just so much data but let's create a view you know I'm

but let's create a view you know I'm only going to show you how to create one

only going to show you how to create one view but I want you to go back and

view but I want you to go back and create multiple views you know if this

create multiple views you know if this is one that you want to look at these

is one that you want to look at these Global numbers um let's look at this one

Global numbers um let's look at this one really quick if you want to look at this

really quick if you want to look at this number right here toss it in a view I

number right here toss it in a view I mean that one doesn't make sense to toss

mean that one doesn't make sense to toss in a view but this

in a view but this one toss these numbers in a view um and

one toss these numbers in a view um and we're we're going to um look at it in

we're we're going to um look at it in Tableau later but for right now let's

Tableau later but for right now let's just create our

just create our view um so like let's just

view um so like let's just say

say creating view to store data for

creating view to store data for later

later visualizations all right so let's say

visualizations all right so let's say create view um and I want I'm just going

create view um and I want I'm just going to keep the same thing um like that um

to keep the same thing um like that um and for views it's so easy I mean I'm

and for views it's so easy I mean I'm literally just going to and I can even

literally just going to and I can even take um the order by I believe we'll see

take um the order by I believe we'll see if I'm

if I'm correct um actually let's get rid of

correct um actually let's get rid of both of these

both of these things so it says create view percent

things so it says create view percent uh percent populate oops percent

uh percent populate oops percent population

population vaccinated um and let's see am I doing

vaccinated um and let's see am I doing anything wrong

anything wrong [Music]

[Music] here let me

here let me see the order by clause

see the order by clause I was completely wrong I was wondering

I was completely wrong I was wondering why I was getting that now let's try

why I was getting that now let's try running it okay so it ran successfully

running it okay so it ran successfully um let's look at our views it's not

um let's look at our views it's not going to be in there let's refresh it

going to be in there let's refresh it hey look we got our very first view we

hey look we got our very first view we can open that up like a table if we want

can open that up like a table if we want to um isn't it's I mean it's gorgeous um

to um isn't it's I mean it's gorgeous um if you want to get rid of that select or

if you want to get rid of that select or sorry control shift R that's a

sorry control shift R that's a refresh um and now it it basically

refresh um and now it it basically recognized is it but let's go back here

recognized is it but let's go back here for a

for a second um and you know we can now query

second um and you know we can now query off of that it's a view now so you know

off of that it's a view now so you know it's it's something that you can it's

it's it's something that you can it's permanent you know you have to go in and

permanent you know you have to go in and actually delete it's not like a temp

actually delete it's not like a temp table this is now permanent and this

table this is now permanent and this could be something that we now use for a

could be something that we now use for a visualization later so do some of these

visualization later so do some of these look at some of the queries that we've

look at some of the queries that we've looked at and create a few of these

looked at and create a few of these views um and we will use them later

views um and we will use them later um normally in like a normal setting uh

um normally in like a normal setting uh if I was actually working I would put

if I was actually working I would put some of these in actual like I would

some of these in actual like I would call them like a work view or a work

call them like a work view or a work table or something set aside so that I

table or something set aside so that I can use them consistently um but I would

can use them consistently um but I would also set them aside so that I could

also set them aside so that I could connect Tableau to that view now we're

connect Tableau to that view now we're going to be using something called

going to be using something called Tableau public that'll be in the very

Tableau public that'll be in the very next tutorial unfortunately um

next tutorial unfortunately um let me see if I can show you I can't

let me see if I can show you I can't show you Tableau public does not connect

show you Tableau public does not connect to SQL databases um and that's because

to SQL databases um and that's because it's free and I totally get it you have

it's free and I totally get it you have to pay for the upgraded version but I am

to pay for the upgraded version but I am not a a billionaire okay I cannot afford

not a a billionaire okay I cannot afford uh the real version of Tableau I'm also

uh the real version of Tableau I'm also not like a student or or like something

not like a student or or like something where I can get it cheap so I'm not

where I can get it cheap so I'm not paying for that so we're going to use

paying for that so we're going to use Tableau public and and I recommend this

Tableau public and and I recommend this anyways because anybody can access it

anyways because anybody can access it it's it's free for anybody so we're

it's it's free for anybody so we're going to be using Tableau in the next

going to be using Tableau in the next one to actually visualize a lot of these

one to actually visualize a lot of these things I want to get at least five

things I want to get at least five visualizations we're going to create a

visualizations we're going to create a dashboard it's going to be a beautiful

dashboard it's going to be a beautiful beautiful thing all right so the very

beautiful thing all right so the very last thing that we are going to do is we

last thing that we are going to do is we are going to actually save this and then

are going to actually save this and then put it into GitHub and I just want to

put it into GitHub and I just want to show you how to do that that's where

show you how to do that that's where we're going to be storing our code at

we're going to be storing our code at least for now um so let's go up here

least for now um so let's go up here let's click file let's click save as

let's click file let's click save as I've already have multiple versions of

I've already have multiple versions of this let's just put

this let's just put B2 we're going to save that so we have

B2 we're going to save that so we have this saved now I'm going to go over here

this saved now I'm going to go over here I'm going to go to my GitHub now if you

I'm going to go to my GitHub now if you don't have an account I highly recommend

don't have an account I highly recommend getting an account so you can start

getting an account so you can start putting your portfolio projects in here

putting your portfolio projects in here of course we're not going to put our

of course we're not going to put our Tableau one in here but our SQL ones and

Tableau one in here but our SQL ones and our python ones you can put in here

our python ones you can put in here again I'll talk a lot more about how we

again I'll talk a lot more about how we actually want to display this in GitHub

actually want to display this in GitHub or other places but what we're going to

or other places but what we're going to do for this is we're going to create a

do for this is we're going to create a new

new repository let's call this one

repository let's call this one portfolio

portfolio projects make it public we'll create the

projects make it public we'll create the repository we'll do all that extra stuff

repository we'll do all that extra stuff later so what we now want to do is

later so what we now want to do is upload an existing file we'll click

upload an existing file we'll click right there go to choose files and we'll

right there go to choose files and we'll click this latest one that we saved uh

click this latest one that we saved uh and we'll open it and we can always

and we'll open it and we can always change the name of it later on and you

change the name of it later on and you can add notes if you'd like but we'll

can add notes if you'd like but we'll commit that change so we'll actually

commit that change so we'll actually upload this uh this

upload this uh this file um but let's look at it really

file um but let's look at it really quick and I'm going to go back and I'm

quick and I'm going to go back and I'm going to use the real one where has the

going to use the real one where has the formatting and and the notes that I have

formatting and and the notes that I have that I wanted to add in there but as you

that I wanted to add in there but as you can see you know you can see all of the

can see you know you can see all of the queries that we wrote and this is

queries that we wrote and this is fantastic so if somebody comes in here

fantastic so if somebody comes in here you know we'll have more notes and kind

you know we'll have more notes and kind of better comments on what they do um

of better comments on what they do um and what the takeaway is this from for a

and what the takeaway is this from for a hiring manager to you know when they

hiring manager to you know when they actually look at this so this is a

actually look at this so this is a really really good place to start again

really really good place to start again uh this may not be your optimal place to

uh this may not be your optimal place to put this I'll give you a few different

put this I'll give you a few different options in a later video about how we

options in a later video about how we can actually uh potentially improve upon

can actually uh potentially improve upon this I'm really looking forward to

this I'm really looking forward to getting more portfolio projects done so

getting more portfolio projects done so we can actually start building a compl

we can actually start building a compl complete

complete portfolio uh if you've stuck around all

portfolio uh if you've stuck around all this way I just want to say

this way I just want to say congratulations I mean I know this was a

congratulations I mean I know this was a long video I know that it took a long

long video I know that it took a long time but you stuck with me uh you you

time but you stuck with me uh you you put in the hard work and that is

put in the hard work and that is fantastic and I really hope that it pays

fantastic and I really hope that it pays off and I hope that this has been

off and I hope that this has been helpful thank you for watching we'll

helpful thank you for watching we'll have a lot more uh videos in the future

have a lot more uh videos in the future on these portfolio projects and I'm I'm

on these portfolio projects and I'm I'm just really really looking forward to

just really really looking forward to doing them to be honest so thank you for

doing them to be honest so thank you for sticking with with me uh thank you for

sticking with with me uh thank you for watching I really appreciate it if you

watching I really appreciate it if you like this video be sure to like And

like this video be sure to like And subscribe below and I will see you in

subscribe below and I will see you in the next

the next [Music]

[Music] video what's going on everybody welcome

video what's going on everybody welcome back to another video today we will be

back to another video today we will be heading back in a sequel for our third

heading back in a sequel for our third portfolio

now I am extremely excited for this project in particular for a few reasons

project in particular for a few reasons one we're getting back into SQL and I

one we're getting back into SQL and I really like SQL and two we are finally

really like SQL and two we are finally focusing on data cleaning and I have

focusing on data cleaning and I have talked so much about why data cleaning

talked so much about why data cleaning is important and that you really need to

is important and that you really need to learn how to clean data and that that's

learn how to clean data and that that's a big part of what a data analyst does

a big part of what a data analyst does but I haven't actually showed you how to

but I haven't actually showed you how to do it yet and so that is what this whole

do it yet and so that is what this whole project is going to be and then at the

project is going to be and then at the end you'll get to add it to your

end you'll get to add it to your portfolio so it's really a win-win now

portfolio so it's really a win-win now before we start I just want to say that

before we start I just want to say that I think it's going to be a little bit

I think it's going to be a little bit more advanced than our very first video

more advanced than our very first video in Sequel where we walk through data

in Sequel where we walk through data exploration if you see something that

exploration if you see something that you have never seen before I will do my

you have never seen before I will do my best to explain it while we're walking

best to explain it while we're walking through it but if you get confused or it

through it but if you get confused or it seems a little complicated please pause

seems a little complicated please pause it Google it do a little bit of research

it Google it do a little bit of research and then come back and I think that will

and then come back and I think that will be very helpful with that being said

be very helpful with that being said let's jump over to my screen and we'll

let's jump over to my screen and we'll get started on the project so we're

get started on the project so we're going to start over here on GitHub and

going to start over here on GitHub and this is where I've actually put the data

this is where I've actually put the data set that we are going to be using so I

set that we are going to be using so I will put this link in the description uh

will put this link in the description uh we're going to go right over here to the

we're going to go right over here to the Nashville housing data for data cleaning

Nashville housing data for data cleaning all you have to do is Click download and

all you have to do is Click download and it's going to download it and you can

it's going to download it and you can open it up if you want to we're not

open it up if you want to we're not going to do anything to this data at all

going to do anything to this data at all but really quick I'm just going to show

but really quick I'm just going to show you what it does look like um and we'll

you what it does look like um and we'll of course look at this in SQL in just a

of course look at this in SQL in just a little bit but we have a unique ID

little bit but we have a unique ID parcel ID uh we have this

parcel ID uh we have this address a sales date uh the price of the

address a sales date uh the price of the home so this is housing data if you

home so this is housing data if you didn't pick up on that already uh who

didn't pick up on that already uh who actually owns the home the owner address

actually owns the home the owner address and then some information about land

and then some information about land value um bedrooms bathrooms things like

value um bedrooms bathrooms things like that again not super important um

that again not super important um because we're going to be doing all of

because we're going to be doing all of this in uh SQL so let's actually get

this in uh SQL so let's actually get this data into SQL we're going to import

this data into SQL we're going to import it the exact same way that we did uh in

it the exact same way that we did uh in the very first video so we're going to

the very first video so we're going to come right over here going to go all the

come right over here going to go all the way down to Microsoft SQL Server 2019

way down to Microsoft SQL Server 2019 Import and Export we'll click next our

Import and Export we'll click next our data source is like last time a

data source is like last time a Microsoft Excel and let's take a

Microsoft Excel and let's take a look and we'll take that first one this

look and we'll take that first one this is the most recent one I've downloaded

is the most recent one I've downloaded but I just wanted to make sure so I

but I just wanted to make sure so I downloaded a few times um for the

downloaded a few times um for the destination we're going to click SQL

destination we're going to click SQL Server native client

Server native client 11.0 and this is my client or my server

11.0 and this is my client or my server right here

right here and I'm going to go down here and I want

and I'm going to go down here and I want to put it in this portfolio project so

to put it in this portfolio project so you know just configure this to what

you know just configure this to what your server is um again if you haven't

your server is um again if you haven't done this before you've never set up SQL

done this before you've never set up SQL server or a server um to go on SQL

server or a server um to go on SQL Server I will leave a link hopefully

Server I will leave a link hopefully right here also in the description uh

right here also in the description uh like I did for the first project so um

like I did for the first project so um you know be sure to go through that

you know be sure to go through that video so that you know how to download

video so that you know how to download this and have everything we're going to

this and have everything we're going to copy the data we're going to take sheet

copy the data we're going to take sheet one um we could renamed sheet one to

one um we could renamed sheet one to something else but uh we didn't and then

something else but uh we didn't and then we're going to finish this and finish

we're going to finish this and finish and it should run successfully

and it should run successfully hopefully it's looking good perfect so

hopefully it's looking good perfect so we have

we have 56477 so let's head over to

56477 so let's head over to SQL all right let's go to our

SQL all right let's go to our database portfolio project uh and here

database portfolio project uh and here is our sheet one now I'm going to rename

is our sheet one now I'm going to rename this um let's rename name it what is it

this um let's rename name it what is it Nashville let's just do Nashville

Nashville let's just do Nashville housing that's what I'm going to rename

housing that's what I'm going to rename it as um at least so when I post these

it as um at least so when I post these queries um to the GitHub and you see

queries um to the GitHub and you see them this is what they will be so if you

them this is what they will be so if you want to have them the exact same or be

want to have them the exact same or be able to copy and paste them um you know

able to copy and paste them um you know you should you should do that as well so

you should you should do that as well so let's take a look really quick let's

let's take a look really quick let's select the top

select the top 1,000 but there's about 56,000 rows

1,000 but there's about 56,000 rows there's a lot of data in here um and a

there's a lot of data in here um and a lot of things so

lot of things so uh I'm about to open up a a save thing

uh I'm about to open up a a save thing and we'll walk through the exact things

and we'll walk through the exact things that we're going to be working on in

that we're going to be working on in just a little bit but um yeah this is

just a little bit but um yeah this is what the data looks like in here there's

what the data looks like in here there's lots of columns uh lots of data so

lots of columns uh lots of data so really excited about this um let me pull

really excited about this um let me pull this open really fast it's going to be

this open really fast it's going to be this project walkth through here are the

this project walkth through here are the things and I'm going to show you this

things and I'm going to show you this really quickly here are the things that

really quickly here are the things that we're going to be walking through so

we're going to be walking through so we're going to standardize the date

we're going to standardize the date format we're going to populate the

format we're going to populate the property address data um that's

property address data um that's referring to this right here if you

referring to this right here if you notice there's the address and there's

notice there's the address and there's also the city that it's in so we want to

also the city that it's in so we want to be able to separate that out um and that

be able to separate that out um and that is actually right over here we're going

is actually right over here we're going to be doing the same same thing to the

to be doing the same same thing to the owner address except that has an address

owner address except that has an address a city and the state um which makes it a

a city and the state um which makes it a little bit more complicated and so um

little bit more complicated and so um that one should be really really cool to

that one should be really really cool to to show you um oh whoops I I messed up

to show you um oh whoops I I messed up that's what this one is breaking out

that's what this one is breaking out into individual columns that's where

into individual columns that's where going to do for that this popular in the

going to do for that this popular in the property address um you know if you

property address um you know if you notice and we'll go into this a little

notice and we'll go into this a little bit there's actually some values in the

bit there's actually some values in the property address that are blank but I'm

property address that are blank but I'm going to show you how you can actually

going to show you how you can actually populate that um which you know is a

populate that um which you know is a it's just a cool trick that I've used a

it's just a cool trick that I've used a few times and it it it does work I think

few times and it it it does work I think you'll find that one interesting um in

you'll find that one interesting um in the sold as vacant field we're going to

the sold as vacant field we're going to be doing some um some case statements if

be doing some um some case statements if then um then we're going to be removing

then um then we're going to be removing duplicates and then delet deleting

duplicates and then delet deleting unused columns so we have a lot to get

unused columns so we have a lot to get through this could be potentially the

through this could be potentially the longest video and I'm okay with that um

longest video and I'm okay with that um because I'm I love SQL down here and and

because I'm I love SQL down here and and I will say that when I when I in the

I will say that when I when I in the very first video I said it was going to

very first video I said it was going to be an ETL video um and I fully intended

be an ETL video um and I fully intended on doing that but I ran into not issues

on doing that but I ran into not issues on my side but issues in the fact that

on my side but issues in the fact that the ma vast majority of people who are

the ma vast majority of people who are going to be watching this are not going

going to be watching this are not going to be able to do what I did to configure

to be able to do what I did to configure my server um but I left it in here

my server um but I left it in here anyways when I think ETL is an automated

anyways when I think ETL is an automated process in order to uh extract the data

process in order to uh extract the data from somewhere we're going to transform

from somewhere we're going to transform it and then put it somewhere this was

it and then put it somewhere this was going to be the extraction method um and

going to be the extraction method um and I was going to put it in a store

I was going to put it in a store procedure so that you could um you know

procedure so that you could um you know run the run the store procedure run the

run the run the store procedure run the job import the data it was going to be

job import the data it was going to be really cool but I know that if I was

really cool but I know that if I was having trouble with it me trying to

having trouble with it me trying to explain it to you and you being able to

explain it to you and you being able to figure it out on your side was going to

figure it out on your side was going to be very tough I left the this anyways

be very tough I left the this anyways because I was able to get to work on my

because I was able to get to work on my computer um but it is tough and it took

computer um but it is tough and it took a lot of research um and I did this for

a lot of research um and I did this for a previous server like a year or two ago

a previous server like a year or two ago and I remember it being crazy hard but I

and I remember it being crazy hard but I was able to figure it out on my computer

was able to figure it out on my computer so if you want to try it out um try it

so if you want to try it out um try it out and and look into the stuff so I'm

out and and look into the stuff so I'm going to leave this here this is just

going to leave this here this is just for if you want to try it it's a little

for if you want to try it it's a little more advanced um and so you don't have

more advanced um and so you don't have to just important and this will be a

to just important and this will be a data cleaning project

data cleaning project instead of an ETL project but data

instead of an ETL project but data cleaning is what 90% it was going to be

cleaning is what 90% it was going to be anyways um anyways let's go back up to

anyways um anyways let's go back up to the very top really quickly I have a

the very top really quickly I have a whole another laptop right here as I did

whole another laptop right here as I did in the first video I didn't show it to

in the first video I didn't show it to you last time but um I have all of my

you last time but um I have all of my queries written out over here I'm going

queries written out over here I'm going to try to do this as quickly as possible

to try to do this as quickly as possible we have a lot to get through now before

we have a lot to get through now before we start writing our queries I am going

we start writing our queries I am going to turn off my camera so I do not get in

to turn off my camera so I do not get in the way all right you should still be

the way all right you should still be hearing my voice let's let get started

hearing my voice let's let get started let's just start with select everything

let's just start with select everything and we'll do from uh and it is portfolio

and we'll do from uh and it is portfolio project.

project. db. Nashville housing so let's just get

db. Nashville housing so let's just get this pulled up on

this pulled up on screen awesome so this is exactly what

screen awesome so this is exactly what we were looking at

we were looking at before and the very first thing that

before and the very first thing that we're going to be looking at is this

we're going to be looking at is this sale date now uh I wrote standardized

sale date now uh I wrote standardized sale date but I'm really just going to

sale date but I'm really just going to change the sale date um so let's copy

change the sale date um so let's copy this really

this really quick and let's look at just s

date and it has this time on the end and it serves absolutely no purpose and I it

it serves absolutely no purpose and I it just annoys me I want to take that off

just annoys me I want to take that off and so right now it's a say it's a date

and so right now it's a say it's a date time format but we're going to convert

time format but we're going to convert and we're going to do date and we're

and we're going to do date and we're going to take sale

going to take sale date sale date and we're going to go

date sale date and we're going to go like that and let's run this really

like that and let's run this really quick and this is what we want it to

quick and this is what we want it to look like all right so let's say update

look like all right so let's say update and we have portfolio project specified

and we have portfolio project specified up here so we can just say Nashville

up here so we can just say Nashville housing and we are going to set sale

housing and we are going to set sale date equal to and we're just going to

date equal to and we're just going to copy this now I will say before we do

copy this now I will say before we do this um I had some issues in my when I

this um I had some issues in my when I was initially doing it whether or not it

was initially doing it whether or not it made the update and I was I'm not sure

made the update and I was I'm not sure why why not it was doing it um so yeah

why why not it was doing it um so yeah it's not doing it right now I you try it

it's not doing it right now I you try it out on yours it may or may not be

out on yours it may or may not be working I'm not exactly sure why that is

working I'm not exactly sure why that is because I would say like 80% of the time

because I would say like 80% of the time it's doing it 10 20% it's not I don't

it's doing it 10 20% it's not I don't know why um no logical explanation of

know why um no logical explanation of that but uh when I most the time when I

that but uh when I most the time when I did it they would then be the same

did it they would then be the same column something we can do I just

column something we can do I just thought of we can do alter alter can't

thought of we can do alter alter can't even say that word alter

even say that word alter table and we can say um I think it's new

table and we can say um I think it's new or it's add add um give me one

or it's add add um give me one second yeah so add and we'll just do

second yeah so add and we'll just do sale date

sale date converted um and let's make that a date

converted um and let's make that a date format and bum just like this and then

format and bum just like this and then we can

we can say like this and say

say like this and say sale date

sale date converted um let's try this and see what

converted um let's try this and see what happens so I'm going to add this

happens so I'm going to add this column and then I'm going to update this

column and then I'm going to update this and it says it's affected let's see what

and it says it's affected let's see what happened uh so let's write sale

happened uh so let's write sale date convert

date convert sale date

sale date converted let's see what happened let's

converted let's see what happened let's see if it actually

see if it actually worked and it worked okay so we we now

worked and it worked okay so we we now have a column um and maybe at the end

have a column um and maybe at the end we'll remove that sale date column U so

we'll remove that sale date column U so that we just have that sale date

that we just have that sale date converted but we know what that is you

converted but we know what that is you don't have to name it that you can name

don't have to name it that you can name it sale date to or something like that

it sale date to or something like that um cool well let's go down to the

um cool well let's go down to the property address and let's get a just a

property address and let's get a just a really quick look at it uh let's copy

really quick look at it uh let's copy this up here I hate rewriting this stuff

this up here I hate rewriting this stuff so I'm always copying and pasting um but

so I'm always copying and pasting um but we're going to be working with the prop

address there we go so let's take a look at this really

at this really quick

um so let's look at sorry I was looking at my notes we need to look at where the

at my notes we need to look at where the property address is null so what you'll

property address is null so what you'll see really quick when we run this is

see really quick when we run this is that there are null values um why there

that there are null values um why there are null values yeah I really don't know

are null values yeah I really don't know um I I really am not sure but let's look

um I I really am not sure but let's look at

at everything where this

everything where this is um where it's n so we have this

is um where it's n so we have this property address we have a sale date a

property address we have a sale date a price legal reference um there's this

price legal reference um there's this parcel ID and there's this unique ID um

parcel ID and there's this unique ID um so we have a lot of information and when

so we have a lot of information and when you have something like this something

you have something like this something like a u an address an address is you

like a u an address an address is you know the address isn't going to change

know the address isn't going to change the address is the address the owner the

the address is the address the owner the owner's address might change but the

owner's address might change but the property itself the address 99.9% of the

property itself the address 99.9% of the time is not going to change so you can

time is not going to change so you can say with almost certainty that you know

say with almost certainty that you know this property address could be populated

this property address could be populated if we had a reference point um to base

if we had a reference point um to base that off of so really quickly um let's

that off of so really quickly um let's look at just

look at just everything and let's look at and we'll

everything and let's look at and we'll just order by

just order by let's

let's do property not property address uh

do property not property address uh let's do parcel ID and let's take a look

let's do parcel ID and let's take a look at this so we have to do a little bit of

at this so we have to do a little bit of some research on this um but I'm going

some research on this um but I'm going to show you something really quick let's

to show you something really quick let's see if I can find

see if I can find example um in not too

example um in not too long okay so here's an example here's

long okay so here's an example here's the same ID so 015 bum and that's the

the same ID so 015 bum and that's the exact same address and we'll find this a

exact same address and we'll find this a lot of times and I look through the data

lot of times and I look through the data and it's it is pretty much accurate um

and it's it is pretty much accurate um when it does have it it it is the exact

when it does have it it it is the exact same address so this parcel ID is going

same address so this parcel ID is going to be the same as the property address

to be the same as the property address um so something that we can do is

um so something that we can do is basically say if this parcel ID has an

basically say if this parcel ID has an address and this parcel ID does not have

address and this parcel ID does not have an address let's populate it with this

an address let's populate it with this address that's already populated because

address that's already populated because we know these are going to be the same

we know these are going to be the same that is basically what we are about to

that is basically what we are about to do um and it's not super complicated um

do um and it's not super complicated um but let's get started writing

but let's get started writing it let's copy that down there um one

it let's copy that down there um one thing we are going to have to do with

thing we are going to have to do with this is do a self-join so we have to

this is do a self-join so we have to join the table to itself to look

join the table to itself to look at if this is equal to this then this

at if this is equal to this then this needs to be equal to this that kind of

needs to be equal to this that kind of thing um so real quick let's just write

thing um so real quick let's just write that join part out and we'll go from

that join part out and we'll go from there I don't know why I sounded

there I don't know why I sounded Canadian right there we'll go from

Canadian right there we'll go from there uh so we'll join on this and we'll

there uh so we'll join on this and we'll say

say on a do oh wait let's let's label them

on a do oh wait let's let's label them I'm gonna do this in a really lazy way

I'm gonna do this in a really lazy way I'm just going to do a and b a. parcel

I'm just going to do a and b a. parcel ID is equal to b. parcel ID

ID is equal to b. parcel ID and um let's see really

and um let's see really quick so we need to find a way to

quick so we need to find a way to distinguish these the sale date could be

distinguish these the sale date could be the same um one thing this unique ID is

the same um one thing this unique ID is is unique so we need these to be

is unique so we need these to be different so let's use this and let's

different so let's use this and let's say um let's say and a. unique ID is not

say um let's say and a. unique ID is not equal to b. unique ID so all we have

equal to b. unique ID so all we have done here is we've joined these the same

done here is we've joined these the same exact table to it self and we said where

exact table to it self and we said where the partiel ID is the same but it's not

the partiel ID is the same but it's not the same row right because this is a

the same row right because this is a unique ID unique will never that means

unique ID unique will never that means these will never repeat themselves so

these will never repeat themselves so we'll never get the same one so if

we'll never get the same one so if this is equal to this but these are

this is equal to this but these are different we want to then populate um

different we want to then populate um populate the other one so let's do a.

populate the other one so let's do a. parcel ID and we'll say a do property

parcel ID and we'll say a do property address B do parcel ID comma bproperty

address B do parcel ID comma bproperty address um and let's take a look at this

address um and let's take a look at this really

really quick and let's

quick and let's do let me see if this works where

do let me see if this works where aproperty address is null and let's see

aproperty address is null and let's see if see what comes up

if see what comes up here okay so this is perfect this is

here okay so this is perfect this is exactly what I wanted to see so we have

exactly what I wanted to see so we have this parcel ID we have this parcel ID

this parcel ID we have this parcel ID and here is our address and it's blank

and here is our address and it's blank in all 35 of these so we have an address

in all 35 of these so we have an address for all of these but we're not

for all of these but we're not populating it so what we want to do is

populating it so what we want to do is we want to say use this thing called

we want to say use this thing called isnull so isnull is basically saying

isnull so isnull is basically saying it's the first thing is what do we want

it's the first thing is what do we want to check to see if it's null so we want

to check to see if it's null so we want to check aproperty address this whole

to check aproperty address this whole thing now if it is null what do we want

thing now if it is null what do we want to populate um we want to put in there

to populate um we want to put in there this B do bproperty um address because

this B do bproperty um address because we want to take that property address

we want to take that property address and stick it in there so um let's run

and stick it in there so um let's run this really quick so this row is what is

this really quick so this row is what is eventually going to be stuck into this

eventually going to be stuck into this row so this is perfect um it's literally

row so this is perfect um it's literally saying when it's null take take this and

saying when it's null take take this and put it there and so that's what this um

put it there and so that's what this um this part of is doing so let's go in

this part of is doing so let's go in here and write our update

here and write our update uh so we want to update and let's take

uh so we want to update and let's take this whole thing from here

up and we this will be the set oops um so we're going to set

oops um so we're going to set um

property okay we need to specify um and just so you know when you're doing joins

just so you know when you're doing joins in an update statement you're not going

in an update statement you're not going to say Nashville housing okay that's

to say Nashville housing okay that's going to give you an error you need to

going to give you an error you need to use it by by its Alias so let's put a so

use it by by its Alias so let's put a so now we're going to say property address

now we're going to say property address is going to be equal to and now we're

is going to be equal to and now we're just going to copy this is

just going to copy this is null and put it right

null and put it right here and we only want to update let's

here and we only want to update let's see if it it does take this so I think

see if it it does take this so I think this should be correct let's let's test

this should be correct let's let's test it out really quick and we're going to

it out really quick and we're going to run this above query and see if it made

run this above query and see if it made that

update all right so there you go um as you can see there are now none that have

you can see there are now none that have null in there otherwise it'd be giving

null in there otherwise it'd be giving us an output right now so that one is

us an output right now so that one is fixed we can go back and check it if you

fixed we can go back and check it if you want to please go back and and double

want to please go back and and double check that um but that is what we did

check that um but that is what we did and it worked perfectly so that's what

and it worked perfectly so that's what that is null does it checks to see if

that is null does it checks to see if this is null if it is null it it it can

this is null if it is null it it it can populate with a value you can also do

populate with a value you can also do like a string and what we I mean you can

like a string and what we I mean you can write you know no address if you wanted

write you know no address if you wanted to do something like that we don't want

to do something like that we don't want to do that we're going to keep it how it

to do that we're going to keep it how it is let's keep moving on we do not have

is let's keep moving on we do not have unlimited time here trying to keep this

unlimited time here trying to keep this I'm going to try to keep this on one

I'm going to try to keep this on one under two hours stretching the rules

under two hours stretching the rules because for my love of SQL and that is

because for my love of SQL and that is the only reason um and this I think is

the only reason um and this I think is going to take a little longer so let's

going to take a little longer so let's take a look and let's copy this real

quick and let's take a look at uh what are we doing the property address the

are we doing the property address the property address um and we can get rid

property address um and we can get rid of this as

of this as well so if you notice we have two things

well so if you notice we have two things here we have both the address and then

here we have both the address and then there's this comma after all of them and

there's this comma after all of them and there is the

there is the city now you know you don't know that or

city now you know you don't know that or you maybe you haven't looked into this

you maybe you haven't looked into this but I have and there are no other commas

but I have and there are no other commas anywhere except for in between these

anywhere except for in between these things as a separator as a delimiter

things as a separator as a delimiter um a delimiter is lit if you don't know

um a delimiter is lit if you don't know what if you've never heard that term

what if you've never heard that term delimiter a delimiter um is something

delimiter a delimiter um is something that separates different columns or

that separates different columns or different values so for us the delimer

different values so for us the delimer is a comma and for this first one

is a comma and for this first one because we're going to be separating

because we're going to be separating this one out and then we're going to be

this one out and then we're going to be doing the owner

doing the owner address um for this one we're going to

address um for this one we're going to be using something called a substring

be using something called a substring and we're also going to be using

and we're also going to be using something called a character index or a

something called a character index or a charart index so let's start writing

charart index so let's start writing that out and let's do

that out and let's do select and let's say substring now the

select and let's say substring now the substring that we want to take we of

substring that we want to take we of course want to be looking at oops let me

course want to be looking at oops let me um put this down here so it helps us out

um put this down here so it helps us out a little

a little bit and I'll get do like that so

bit and I'll get do like that so substring and of course we're looking at

substring and of course we're looking at property

property address and we want to look at position

address and we want to look at position one so we're going to start at position

one so we're going to start at position one one now this next part

one one now this next part is something that you may have never

is something that you may have never seen before um and if that if you

seen before um and if that if you haven't that's totally okay um we're

haven't that's totally okay um we're going to be the character index is going

going to be the character index is going to be searching

to be searching for the um it's going to basically be

for the um it's going to basically be searching for a specific value okay

searching for a specific value okay that's all it's doing and you and you

that's all it's doing and you and you can look into this a little bit more if

can look into this a little bit more if you want um so it's going to be Char

you want um so it's going to be Char index that's how it's spelled and then

index that's how it's spelled and then like an open parentheses and we want to

like an open parentheses and we want to specify what we're looking for so it can

specify what we're looking for so it can be anything you can even do you know if

be anything you can even do you know if you wanted to things like um Tom or you

you wanted to things like um Tom or you can do Val well you do it um like this

can do Val well you do it um like this you can look for Tom or if you're

you can look for Tom or if you're looking for a specific word like John

looking for a specific word like John you can search that that's what this is

you can search that that's what this is for um but we're going to do a comma

for um but we're going to do a comma where are we looking that's what this

where are we looking that's what this next one is so we're looking in property

next one is so we're looking in property address uh and then we're going to close

address uh and then we're going to close the

the parenthesis and and we'd also close it

parenthesis and and we'd also close it again to complete off that substring and

again to complete off that substring and we'll say as

we'll say as address um and let's just take a look

address um and let's just take a look really quick at

really quick at this so right now it's taking the it is

this so right now it's taking the it is basically going it's looking at property

basically going it's looking at property address it's going to the very first

address it's going to the very first value or starting at the first value and

value or starting at the first value and then it's going until the comma Now the

then it's going until the comma Now the unfortunate thing is is we actually

unfortunate thing is is we actually getting this comma in this output and we

getting this comma in this output and we don't want that uh you don't want a

don't want that uh you don't want a comma at the end of every address we can

comma at the end of every address we can change that um so we can say because

change that um so we can say because this is specifying a position if we just

this is specifying a position if we just look at this chart index which we can do

look at this chart index which we can do really

quick it is going to give us a a number it is saying at position 19 that is

it is saying at position 19 that is where the comma is right so it's not

where the comma is right so it's not like it's taking it's not a value or

like it's taking it's not a value or it's not a um it's not a string it's a

it's not a um it's not a string it's a it's a number so we can say minus one

it's a number so we can say minus one one and if we do

one and if we do that and now we run

that and now we run it now that comma is gone because we're

it now that comma is gone because we're looking back we're going to the comma

looking back we're going to the comma and then going back one from uh one

and then going back one from uh one behind the comma so that's how you get

behind the comma so that's how you get rid of that comma right there um the

rid of that comma right there um the next one's a little bit more tricky

next one's a little bit more tricky because we're not starting well it's not

because we're not starting well it's not super tricky but we're not starting at

super tricky but we're not starting at that first position anymore so let's put

that first position anymore so let's put a comma then we have our substring now

a comma then we have our substring now where we want to start is at this as at

where we want to start is at this as at where the comma is so instead of

where the comma is so instead of position one we want it to be where that

position one we want it to be where that character

character index um I don't want it to look like

index um I don't want it to look like this this whole time is it like this

this this whole time is it like this what am I doing it doesn't

what am I doing it doesn't matter let's just get rid of this and

matter let's just get rid of this and see if that fixes

see if that fixes it what am I doing here oh it's just

it what am I doing here oh it's just because this is wrong um and we'll just

because this is wrong um and we'll just do comma parentheses that might fix it

do comma parentheses that might fix it ah doesn't matter okay I'm wasting time

ah doesn't matter okay I'm wasting time I'm going to keep going we want to start

I'm going to keep going we want to start in this in this position okay um but we

in this in this position okay um but we actually don't want to start at minus

actually don't want to start at minus one we need to start at plus one because

one we need to start at plus one because we want to go to the actual comma itself

we want to go to the actual comma itself then once we get to the comma we want to

then once we get to the comma we want to add one so if we didn't if we just left

add one so if we didn't if we just left it the same again it would include the

it the same again it would include the comma at the beginning um then we need

comma at the beginning um then we need to specify where it needs to go to where

to specify where it needs to go to where does it need to finish now every single

does it need to finish now every single thing is going to be different every

thing is going to be different every single address has a different length

single address has a different length but we can use that to our advantage in

but we can use that to our advantage in this one and we can literally say the

this one and we can literally say the length

length of property address you guessed it right

of property address you guessed it right and then we can close this off let's see

and then we can close this off let's see if that

if that works okay what's messing up so we have

works okay what's messing up so we have property

property substring property

substring property address comma character

address comma character index and then we have specifying it in

index and then we have specifying it in the

the comma um we have the property address

comma um we have the property address plus one okay we can't have that right

plus one okay we can't have that right there I don't know why I had that F

there I don't know why I had that F finally figured it out at the end um so

finally figured it out at the end um so let's see what we're doing here let's

let's see what we're doing here let's see if it worked it works perfect um and

see if it worked it works perfect um and again this was one that I'm guessing a

again this was one that I'm guessing a lot of people haven't used before so I

lot of people haven't used before so I was trying to explain it a little bit

was trying to explain it a little bit more than other ones um but if we take

more than other ones um but if we take that out we take out that plus one

that out we take out that plus one you're going to see the comma at the

you're going to see the comma at the beginning right here so that's what that

beginning right here so that's what that is um so Plus one and that's what we're

is um so Plus one and that's what we're going to keep now we can't separate two

going to keep now we can't separate two values into from one column without

values into from one column without creating two other columns so just like

creating two other columns so just like we added this um table up here we're

we added this um table up here we're just going to I mean we're we're I'm

just going to I mean we're we're I'm just going to copy this down here really

just going to copy this down here really quick we're going to create two new

columns and add that value in so we're gonna we're gonna add that we're going

gonna we're gonna add that we're going to call this um let's call it because

to call this um let's call it because it's property address let's do

it's property address let's do property property split um and this is

property property split um and this is the

the address and then we'll say this one this

address and then we'll say this one this next one is going to be property and

next one is going to be property and this is City split

this is City split city city and this isn't going to be a

city city and this isn't going to be a date of course uh this going to be let's

date of course uh this going to be let's do narar and let's make it 255 just in

do narar and let's make it 255 just in case it's a large um just in case it is

case it's a large um just in case it is a large string a large text so then we

a large string a large text so then we can say um update that update

can say um update that update that um and now we need to in insert um

that um and now we need to in insert um what we did for it so this first one is

what we did for it so this first one is the address so we're going to say that

the address so we're going to say that equals the address and we're going to

equals the address and we're going to take this whole thing this whole

take this whole thing this whole substring oops and copy that and that's

substring oops and copy that and that's going to equal this um and then at the

going to equal this um and then at the end we'll we'll look at it really quick

end we'll we'll look at it really quick so first let's add this table I'm going

so first let's add this table I'm going to do this one at a time really quick so

to do this one at a time really quick so you can see it so it adds the

you can see it so it adds the table now it adds the results and again

table now it adds the results and again adds the table of city and sets that

adds the table of city and sets that City to that

City to that substring and now let's take um let's

substring and now let's take um let's take this and just do

take this and just do select everything from this and you

select everything from this and you should see at the very end because when

should see at the very end because when you add it it goes to the end we should

you add it it goes to the end we should have two new values and here we are so

have two new values and here we are so property split address and property

property split address and property split city um it's much more usable than

split city um it's much more usable than this I mean this would be a nightmare

this I mean this would be a nightmare not a nightmare it just be annoying to

not a nightmare it just be annoying to use this column I mean now that it's

use this column I mean now that it's separated on the address and the city

separated on the address and the city it's so much more usable of data it

it's so much more usable of data it really really is the next thing we're

really really is the next thing we're going to be looking at is this owner

going to be looking at is this owner address

address now it was hard enough or it was tough

now it was hard enough or it was tough enough to do this um but I want to show

enough to do this um but I want to show you maybe even a simpler way to do it

you maybe even a simpler way to do it even though this is more complicated so

even though this is more complicated so let's go down

let's go down here and let's get rid of

here and let's get rid of this so let's say um let's get this and

this so let's say um let's get this and let's just say property oops no we're

let's just say property oops no we're doing owner owner address here we go

doing owner owner address here we go let's just take a look at this let's see

let's just take a look at this let's see what we got so again we're using or what

what we got so again we're using or what we have in here is the address the city

we have in here is the address the city and the state so what we need to do is

and the state so what we need to do is split all of those out um and again I

split all of those out um and again I don't want to use substrings again that

don't want to use substrings again that was a pain I want to use um something a

was a pain I want to use um something a little different something again that

little different something again that you may have never seen it's called

you may have never seen it's called parse name um and parse name is super

parse name um and parse name is super useful um especially for like delimited

useful um especially for like delimited stuff stuff that's delimited by a

stuff stuff that's delimited by a specific value um so let me just show

specific value um so let me just show you what it is and then we'll go from

you what it is and then we'll go from there so we can say

there so we can say parse parse name um and we're going to

parse parse name um and we're going to be doing this on the owner

be doing this on the owner address okay let's let me see let me see

address okay let's let me see let me see yeah I mean it's because I don't have

yeah I mean it's because I don't have this of course I do that all the time so

this of course I do that all the time so annoying so on the owner address um and

annoying so on the owner address um and then let's do

then let's do one and let's just see what happens

uh nothing changed of course because parse name only is useful with periods

parse name only is useful with periods or that's what it looks for that's what

or that's what it looks for that's what par name looks for and these are commas

par name looks for and these are commas so something we can just do is we can

so something we can just do is we can replace those commas with uh a a instead

replace those commas with uh a a instead of a comma we replace it with a period

of a comma we replace it with a period so super easy we're just going to do

so super easy we're just going to do owner address

owner address comma um and we'll look for the comma in

comma um and we'll look for the comma in there then we need to specify what we

there then we need to specify what we need to change it to we'll change it to

need to change it to we'll change it to a period and let's close

a period and let's close that and now let's run

that and now let's run it and it's taking Tennessee so

it and it's taking Tennessee so something odd about at least to me odd

something odd about at least to me odd about parse name is that it kind of does

about parse name is that it kind of does things backwards than what you would

things backwards than what you would expect it to do uh let's really quick

expect it to do uh let's really quick let's add the other things um you'll

let's add the other things um you'll you'll get a kick out well you won't get

you'll get a kick out well you won't get a kick out of this as much as I do

a kick out of this as much as I do here's one two

here's one two three let's execute this and it

three let's execute this and it separates everything for us but it's

separates everything for us but it's backwards so it's 1 2 3 you would

backwards so it's 1 2 3 you would imagine it' be one two 3 but no it's one

imagine it' be one two 3 but no it's one two three so all we need to do is go

two three so all we need to do is go three 2

three 2 one and run

one and run this and there we go so now we have it

this and there we go so now we have it broken out this is now our address this

broken out this is now our address this is our city and this is our state so

is our city and this is our state so super what I would consider super easy a

super what I would consider super easy a lot easier than the substring but I

lot easier than the substring but I didn't want to show you the easy one

didn't want to show you the easy one first and then give you the hard one um

first and then give you the hard one um so now we just need to add those columns

so now we just need to add those columns and then we need to add the values so

and then we need to add the values so let's do

let's do this uh let's make some room and I need

this uh let's make some room and I need to get rid of one of these I think o did

to get rid of one of these I think o did I do that right what did I

do I have my alter table update alter table update what is this doing here

table update what is this doing here what is this I don't even know what this

what is this I don't even know what this is we'll just go like that so now we

is we'll just go like that so now we have three perfect um so from National

have three perfect um so from National Housing we're going to say we're going

Housing we're going to say we're going to say this is the

to say this is the owner oops owner split

owner oops owner split address um actually let me just copy the

address um actually let me just copy the owner make it easier so we have owner

owner make it easier so we have owner split address owner split

split address owner split City

City and let's do owner owner split and then

and let's do owner owner split and then State oops and copy there owner split

State oops and copy there owner split City there we go owner split address

City there we go owner split address owner split address so I'm putting all

owner split address so I'm putting all the sets equal to what we're about to

the sets equal to what we're about to add to so now this first one this three

add to so now this first one this three is the address we'll paste it there the

is the address we'll paste it there the second one is the city so we'll put that

second one is the city so we'll put that oh I see what happened here that's what

oh I see what happened here that's what happened got to get rid of

happened got to get rid of that um I set the owner split City equal

that um I set the owner split City equal to that middle one and then of course

to that middle one and then of course the third one is the

the third one is the state so let's go do

state so let's go do that and that should be done so let's do

that and that should be done so let's do it two at a

it two at a time oops owner split address what's

time oops owner split address what's wrong with that oh I probably just got

wrong with that oh I probably just got to run this first let's try that

to run this first let's try that tried to get good too quick um you can

tried to get good too quick um you can do this a much more efficient way I'm

do this a much more efficient way I'm just doing this for visual purposes I

just doing this for visual purposes I would update all the tables first or add

would update all the tables first or add all the um columns first I mean and then

all the um columns first I mean and then do all the updating at the end that's

do all the updating at the end that's normally how I do it but um again for

normally how I do it but um again for visual purposes that this is what we're

visual purposes that this is what we're doing so let's go get this actually

doing so let's go get this actually let's get this bring this down

let's get this bring this down here um don't keep this in in your final

here um don't keep this in in your final queries it's a lot of extra selecting

queries it's a lot of extra selecting everything you don't need to do that um

everything you don't need to do that um so here we go so owner split address

so here we go so owner split address owner split City owner split State again

owner split City owner split State again so much more usable than when it's all

so much more usable than when it's all in one column I mean it is 10 100 times

in one column I mean it is 10 100 times more useful data now um you know that

more useful data now um you know that one to me you that gets used a lot let's

one to me you that gets used a lot let's keep it going I feel like we're making

keep it going I feel like we're making fantastic time I don't even know I'm not

fantastic time I don't even know I'm not even keeping track of time time is not

even keeping track of time time is not even relative anymore be three hours and

even relative anymore be three hours and I wouldn't care let's keep going um

I wouldn't care let's keep going um let's take a look at this column right

let's take a look at this column right here sold as vacant um right now has no

here sold as vacant um right now has no but let's look at let's do select

but let's look at let's do select distinct oh gosh I hate when I do this I

distinct oh gosh I hate when I do this I do this all the time am I the only one I

do this all the time am I the only one I don't think I'm the only one and we'll

don't think I'm the only one and we'll do sp uh what is it sold as okay sold as

do sp uh what is it sold as okay sold as vacant let's do a distinct count on are

vacant let's do a distinct count on are distinct on

distinct on these so right now we have yes no n y

these so right now we have yes no n y I'm guessing which is no and yes and

I'm guessing which is no and yes and then no so let's look just for just

then no so let's look just for just because I'm curious um let's look at a

because I'm curious um let's look at a count

count of I don't want to do the let me just do

of I don't want to do the let me just do sold as vacant let me do a count of this

sold as vacant let me do a count of this and we'll Group

and we'll Group by uh sold is vacant okay let's run this

by uh sold is vacant okay let's run this and see what we get oh gosh let me order

and see what we get oh gosh let me order by okay here we go now we're now we're

by okay here we go now we're now we're moving that's not what I wanted at all

moving that's not what I wanted at all order by two here's what I wanted okay

order by two here's what I wanted okay so at no we have 51,000 yes 4,000 almost

so at no we have 51,000 yes 4,000 almost 5,000 no and then just a few so let's

5,000 no and then just a few so let's change them to to yes and no because

change them to to yes and no because these are obviously the vastly more

these are obviously the vastly more populated ones um and we're just going

populated ones um and we're just going to do this through a case statement so

to do this through a case statement so we're going to say oh yeah let me get

we're going to say oh yeah let me get this ready before we start oh yeah I'm

this ready before we start oh yeah I'm ahead of the game now let's do select

ahead of the game now let's do select and we'll do sold as vacant and then

and we'll do sold as vacant and then we'll start our case

we'll start our case statement um yeah let's do right here so

statement um yeah let's do right here so we'll do case when sold as vacant is

we'll do case when sold as vacant is equal to yes all we want to do is say

equal to yes all we want to do is say then we want to make it

then we want to make it no oh

no oh won't make a yes what am I doing geez

won't make a yes what am I doing geez I'm losing it when and I'm just oops

I'm losing it when and I'm just oops oops oops oops ignore that pretend that

oops oops oops ignore that pretend that didn't

didn't happen

happen when sold as vacant is equal to

when sold as vacant is equal to n

n then

then no and then else we want to say if it's

no and then else we want to say if it's already if it's not one of those values

already if it's not one of those values it means it's already a yes or no so

it means it's already a yes or no so we're just going to say just keep it as

we're just going to say just keep it as sold as vacant and then we'll end it so

sold as vacant and then we'll end it so let's take a

let's take a look okay so let's scroll through here

look okay so let's scroll through here and see if we get any that we can see oh

and see if we get any that we can see oh I just went byy some didn't

I just went byy some didn't I oh I just went buy some I know I

I oh I just went buy some I know I did um let's see okay here we go so

did um let's see okay here we go so here's an N it's now a no so this this

here's an N it's now a no so this this sold as vacant as this column the newly

sold as vacant as this column the newly uh the case statement right here is

uh the case statement right here is changing it so the N is no so this

changing it so the N is no so this should work all and this will be a

should work all and this will be a unique update statement um and I hope it

unique update statement um and I hope it works unlike the first update statement

works unlike the first update statement that we we did that was a that was a

that we we did that was a that was a travesty um let's do update Nashville

travesty um let's do update Nashville housing um and we'll

housing um and we'll say

say set sorry I'm talking faster than I'm

set sorry I'm talking faster than I'm going set sold as vacant equal to and we

going set sold as vacant equal to and we can just literally put in this case

can just literally put in this case statement um it's not but let's try

statement um it's not but let's try it okay now let's go look at this again

it okay now let's go look at this again and see if it made the update there we

and see if it made the update there we go the update statement worked oh

go the update statement worked oh fantastic it's a beautiful

fantastic it's a beautiful thing okay great I'm glad that one

thing okay great I'm glad that one worked I was worried for a second that

worked I was worried for a second that uh my update had broken in um in SQL

uh my update had broken in um in SQL Server now now we're going to do

Server now now we're going to do something um these next two things is

something um these next two things is we're going to remove the duplicates and

we're going to remove the duplicates and then we're going to get rid of unused

then we're going to get rid of unused columns um this removing duplicate I got

columns um this removing duplicate I got to be honest I don't do it a ton in SQL

to be honest I don't do it a ton in SQL but I have done it um especially for

but I have done it um especially for like

like queries you know when I'm looking at

queries you know when I'm looking at full tables I I will write some sort of

full tables I I will write some sort of temp table and like put the remove

temp table and like put the remove duplicates in there I normally don't

duplicates in there I normally don't delete actual data we are we're going to

delete actual data we are we're going to do that um but it's not a standard

do that um but it's not a standard practice to delete data that's in um

practice to delete data that's in um that's in your database so just for

that's in your database so just for future purposes don't blame me if you

future purposes don't blame me if you delete all the all the duplicates back

delete all the all the duplicates back accident in your uh table at work so you

accident in your uh table at work so you can do this a few different ways but the

can do this a few different ways but the way I'm going to show you is we're going

way I'm going to show you is we're going to write a

to write a CTE and we're going to do some windows

CTE and we're going to do some windows functions to find where there are

functions to find where there are duplicate values okay so excuse me so

duplicate values okay so excuse me so let's start writing out our CTE and or

let's start writing out our CTE and or you know even we can write out the query

you know even we can write out the query first then put it into a CTE that might

first then put it into a CTE that might be a little bit better so let's do

be a little bit better so let's do select everything and oh my gosh I was

select everything and oh my gosh I was about to do it somebody's out there just

about to do it somebody's out there just like waiting for me to make that mistake

like waiting for me to make that mistake again so we want to partition our

again so we want to partition our data um when you're doing removing

data um when you're doing removing duplicates we're going to have duplicate

duplicates we're going to have duplicate rows and we need to be able to have a

rows and we need to be able to have a way to identify those rows right so you

way to identify those rows right so you can use things like rank order rank

can use things like rank order rank um row number there are a few different

um row number there are a few different options we're going to be using row

options we're going to be using row number um and you know if you want to

number um and you know if you want to look into how Rank and rank uh like

look into how Rank and rank uh like dense Rank and all those ones work

dense Rank and all those ones work please do that so you know why we're

please do that so you know why we're doing it um but we're using row number

doing it um but we're using row number because it's the I think the simplest um

because it's the I think the simplest um and it's going to do what we need

and it's going to do what we need exactly so I'm going to get this over

exactly so I'm going to get this over here we'll say select everything because

here we'll say select everything because we're selecting everything then we're

we're selecting everything then we're going to add this row number on here so

going to add this row number on here so row number and we're going to do these

row number and we're going to do these parenthesis right here we're going to

parenthesis right here we're going to say over and an open parentheses now we

say over and an open parentheses now we need to write our partition because

need to write our partition because we're going to partition this data so

we're going to partition this data so we're going to say um

we're going to say um Partition by cool um now really quickly

Partition by cool um now really quickly while we're here we need to actually

while we're here we need to actually know what we're partitioning on that's

know what we're partitioning on that's helpful so let me write this so while

helpful so let me write this so while we're writing it we can see what we're

we're writing it we can see what we're doing we need to partition it on things

doing we need to partition it on things that should be unique

that should be unique um

um two basically to each row um if in I

two basically to each row um if in I guess for the sake of what we're doing

guess for the sake of what we're doing we're we're going to pretend this unique

we're we're going to pretend this unique ID isn't here um although you know you

ID isn't here um although you know you could say I'm cheating it doesn't matter

could say I'm cheating it doesn't matter but I'm going to say you know if things

but I'm going to say you know if things like the parcel ID are the same if the

like the parcel ID are the same if the sale date is the same um the property

sale date is the same um the property address is the same the sales price is

address is the same the sales price is the same This legal reference which I'm

the same This legal reference which I'm guessing is some type of legal document

guessing is some type of legal document saying it's like somebody's uh property

saying it's like somebody's uh property if all of those are the exact same then

if all of those are the exact same then to me that is the same data it's it's

to me that is the same data it's it's unusable just for example I mean this

unusable just for example I mean this may I don't I mean this data is just

may I don't I mean this data is just some random data set I found online

some random data set I found online right so that's what we're going to be

right so that's what we're going to be going with that's what we're going to be

going with that's what we're going to be running with and pretend that lie that I

running with and pretend that lie that I just told you is completely true so what

just told you is completely true so what we want to Partition by let's start with

we want to Partition by let's start with the

the parcel um can I is this not right here

parcel um can I is this not right here why is it saying this why is it not

why is it saying this why is it not giving

giving me okay doesn't even matter I'm just

me okay doesn't even matter I'm just going to say parcel

going to say parcel ID um we can

ID um we can say

say property we'll do a property address

property we'll do a property address stick with me we're getting somewhere

stick with me we're getting somewhere we'll do sale

we'll do sale price um what do we say sale date I mean

price um what do we say sale date I mean there shouldn't be two of this they

there shouldn't be two of this they didn't sell twice on the same day come

didn't sell twice on the same day come on and then legal

reference and oh I know why it's not working or my

and oh I know why it's not working or my autocomplete isn't working which I love

autocomplete isn't working which I love um it's because we're creating our own

um it's because we're creating our own partition so it's its own column of

partition so it's its own column of course I don't know why I'm it's late as

course I don't know why I'm it's late as you can see down here it's 11:15 it's

you can see down here it's 11:15 it's getting late for me but hey I I this is

getting late for me but hey I I this is an adrenaline rush for me um now we need

an adrenaline rush for me um now we need to order it now we want to order it on

to order it now we want to order it on something that should

something that should be um not necessar I guess unique so

be um not necessar I guess unique so we're going to order it on this unique

we're going to order it on this unique ID we'll see if that actually does what

ID we'll see if that actually does what we want it to do um oops what am I doing

we want it to do um oops what am I doing order bu come on and we'll say uh

order bu come on and we'll say uh unique oops unique ID perfect and we

unique oops unique ID perfect and we should be able to close that off and

should be able to close that off and we're going to call this R num I mean

we're going to call this R num I mean that's just that just makes sense so now

that's just that just makes sense so now we have this and let's run this really

we have this and let's run this really quick and see what happens so um and

quick and see what happens so um and maybe we should order this as well but

maybe we should order this as well but we'll maybe we'll do that

we'll maybe we'll do that later yeah let's order this on parcel

later yeah let's order this on parcel ID um order by parcel ID let's just see

ID um order by parcel ID let's just see what happens because this I think that

what happens because this I think that should be pretty

accurate um let's scroll down and see if we get

we get any this is all

any this is all ones maybe should be doing it on unique

ones maybe should be doing it on unique ID I don't know let's see if we get any

ID I don't know let's see if we get any hits okay there's a two in

hits okay there's a two in there let's let's look at this really

there let's let's look at this really quick because I want to see

quick because I want to see it maybe I did something wrong I don't

it maybe I did something wrong I don't know it is absolutely

possible somebody play some Jeopardy music for me real

music for me real quick yeah I don't know I don't know why

quick yeah I don't know I don't know why it's um okay so let's see let's let's

it's um okay so let's see let's let's look at these

look at these two um and let's see if I

two um and let's see if I did something wrong

did something wrong oops don't need to pull that

oops don't need to pull that up I was doing some research when I when

up I was doing some research when I when that convert by wasn't working um okay

that convert by wasn't working um okay so this one and this one it's giving

so this one and this one it's giving different row

different row numbers so let's look at the actual data

numbers so let's look at the actual data ignore the unique ID but the data itself

ignore the unique ID but the data itself so the the sale date is the same the

so the the sale date is the same the sale price is the same the legal

sale price is the same the legal reference is the same the owner is the

same this is the same I mean literally every single thing

same I mean literally every single thing in here is the same so this is a good

in here is the same so this is a good example so we're going to in this query

example so we're going to in this query that we're about to write that that will

that we're about to write that that will be that second one will be deleted

be that second one will be deleted because we don't need it now there

because we don't need it now there there's only one so it looks like this

there's only one so it looks like this is working as intended um I can also

is working as intended um I can also do um

do um let's do where rowcor num is greater

let's do where rowcor num is greater than one let's see if that I don't think

than one let's see if that I don't think it will work

it will work actually yeah that's because uh it is

actually yeah that's because uh it is that is in a Windows function of course

that is in a Windows function of course we can't do that what am I thinking

we can't do that what am I thinking that's why we need to put it into a

that's why we need to put it into a CTE oh of course it all comes back so

CTE oh of course it all comes back so let's call this all comes back to the CT

let's call this all comes back to the CT those things are amazing um let's call

those things are amazing um let's call this um row num

this um row num num

num CTE and we'll say as and then open

CTE and we'll say as and then open parentheses and I don't think we can

parentheses and I don't think we can have an order by in here let's do it

have an order by in here let's do it like this and let's just do select

like this and let's just do select everything from row number

everything from row number CTE so again if you haven't watched my

CTE so again if you haven't watched my like CTE CTE video or you've never used

like CTE CTE video or you've never used a CTE before um this is now basically

a CTE before um this is now basically almost like a temp table so we're going

almost like a temp table so we're going to be able to this query down here is

to be able to this query down here is querying off of this table that we quote

querying off of this table that we quote unquote

unquote created so um it looks like it's working

created so um it looks like it's working so all we're going to do is select um

so all we're going to do is select um everything from that and we want to say

everything from that and we want to say where row num because that's now a row

where row num because that's now a row is greater than one and let's order that

is greater than one and let's order that by I don't know property address let's

by I don't know property address let's see if that

see if that works and let's see what happens

works and let's see what happens okay so all of these are duplicates we

okay so all of these are duplicates we have 104 of them it looks like so

have 104 of them it looks like so there's not many but it there's twos any

there's not many but it there's twos any threes no no threes so there's multiple

threes no no threes so there's multiple of these rows or columns that are

of these rows or columns that are basically duplicates um and we want to

basically duplicates um and we want to delete them so all we're going to say is

delete them so all we're going to say is we're going to select instead of saying

we're going to select instead of saying select everything from row we're just

select everything from row we're just going to say delete

going to say delete and uh yeah I got to get rid of that

and uh yeah I got to get rid of that order bu that doesn't work and let's do

this there's 104 let's see if it worked um so now let's do let's go back and

um so now let's do let's go back and we'll say select everything and let's

we'll say select everything and let's see if there's any more duplicates in

see if there's any more duplicates in there there are none that is fantastic

there there are none that is fantastic every I'm like biting my nails now to

every I'm like biting my nails now to see if each one of these Works um

see if each one of these Works um because I that first one didn't work um

because I that first one didn't work um so yeah so it worked we got rid of the

so yeah so it worked we got rid of the duplicates that is fantastic um and now

duplicates that is fantastic um and now it's smooth sailing from here because

it's smooth sailing from here because we're just going to delete some um

we're just going to delete some um unused columns that we don't care about

unused columns that we don't care about this doesn't happen often um this I

this doesn't happen often um this I would say actually happens more in like

would say actually happens more in like views when I'm creating views I have a

views when I'm creating views I have a view and I'm like oh I didn't mean to

view and I'm like oh I didn't mean to add that column let me just remove it

add that column let me just remove it because it's a I don't need it you don't

because it's a I don't need it you don't do this to um like the raw data that you

do this to um like the raw data that you import usually this is I mean again best

import usually this is I mean again best practices please don't do this to your

practices please don't do this to your raw data that comes into your database

raw data that comes into your database um talk to somebody before you do this

um talk to somebody before you do this that's just my my legal advice for the

that's just my my legal advice for the day I'm not legally bound or legally

day I'm not legally bound or legally held responsible for any mistakes you

held responsible for any mistakes you make so let's keep going um we're

make so let's keep going um we're literally just going to delete some

literally just going to delete some columns it could be any columns that we

columns it could be any columns that we want um but for example we got have

want um but for example we got have these property split address and owner

these property split address and owner split address um in city and state and

split address um in city and state and city and these are perfect and much more

city and these are perfect and much more useful than these owner um these owner

useful than these owner um these owner address because this is really unusable

address because this is really unusable to be honest so we're going to delete

to be honest so we're going to delete those um and maybe we'll also get rid of

those um and maybe we'll also get rid of like I don't know maybe the land that

like I don't know maybe the land that land use might be useful this tax tax

land use might be useful this tax tax District who cares about that um so it's

District who cares about that um so it's going to be super easy we're just going

going to be super easy we're just going to write alter table alter table did I

to write alter table alter table did I say that right

say that right geez um and we're going to say alter

geez um and we're going to say alter this

this table and we're going to

table and we're going to drop a column and you can do as many as

drop a column and you can do as many as many as we want so we're going to say

many as we want so we're going to say owner um

owner um address we're going to do tax

address we're going to do tax district and let's also do the property

address all right and let's try this and let's see if it

let's see if it works I'm

nervous all right so as you can see that the property address is gone the owner

the property address is gone the owner address is gone the tax what was it tax

address is gone the tax what was it tax district is gone and now we are left

district is gone and now we are left with this um now remember the whole

with this um now remember the whole point of everything we were doing was to

point of everything we were doing was to clean up the data right we wanted to

clean up the data right we wanted to clean the data and actually now well now

clean the data and actually now well now that we're here we have this sale date

that we're here we have this sale date as well U and we have the sale date

as well U and we have the sale date converted over here let's get rid I

converted over here let's get rid I forgot let's get rid of this oh that was

forgot let's get rid of this oh that was my dog Max excuse them let's get rid of

my dog Max excuse them let's get rid of oops let's get rid of that sale price

oops let's get rid of that sale price that that or the um sale date that made

that that or the um sale date that made me look like an idiot this is Sweet

me look like an idiot this is Sweet Revenge sale

Revenge sale date Sweet Sweet

date Sweet Sweet Revenge all right and it is gone so it's

Revenge all right and it is gone so it's as easy as that now remember like I was

as easy as that now remember like I was saying before the whole point of this

saying before the whole point of this project is to clean the data and make it

project is to clean the data and make it more usable um and it may not have felt

more usable um and it may not have felt like that as we were going through cuz I

like that as we were going through cuz I wasn't you know really looking at the

wasn't you know really looking at the clean cleaning data uh uh we were

clean cleaning data uh uh we were cleaning it but you know what was the

cleaning it but you know what was the purpose of it I may not have highlighted

purpose of it I may not have highlighted that too much all these other columns

that too much all these other columns that we created um are just it's much

that we created um are just it's much more usable much more friendly um this

more usable much more friendly um this is standardized now and you know we we

is standardized now and you know we we did that through quite a few various

did that through quite a few various methods um so let's go back up the top

methods um so let's go back up the top we're going to recap what we did really

we're going to recap what we did really quick so using this convert we tried to

quick so using this convert we tried to standardize the date format or change

standardize the date format or change the date format may or may not have

the date format may or may not have worked for you didn't work for me we

worked for you didn't work for me we populated this property address um which

populated this property address um which we did that

we did that before we broke this out because if we

before we broke this out because if we reversed it if we broke these addresses

reversed it if we broke these addresses out into individual columns and then we

out into individual columns and then we populated the this thing um we would

populated the this thing um we would have because then we went and

have because then we went and deleted uh we went and deleted this

deleted uh we went and deleted this column oops sorry we went and deleted uh

column oops sorry we went and deleted uh this property address so we wouldn't

this property address so we wouldn't have actually gotten any of that data so

have actually gotten any of that data so there was a reason it was in that order

there was a reason it was in that order uh don't mess that up that's happened um

uh don't mess that up that's happened um so we broke it out we did that to to

so we broke it out we did that to to using um substring chart index as well

using um substring chart index as well as parse name and

as parse name and replace then we went through and we

replace then we went through and we changed yes to no or Y and n's to yeses

changed yes to no or Y and n's to yeses and NOS using case

and NOS using case statements um then we use we removed

statements um then we use we removed duplicates using a row number a c te and

duplicates using a row number a c te and windows function of Partition by and

windows function of Partition by and then at the end we deleted a few useless

then at the end we deleted a few useless columns that we no longer want to see

columns that we no longer want to see because um they are horrible and

because um they are horrible and terrible and um you know we don't want

terrible and um you know we don't want to see them anymore that is the entire

to see them anymore that is the entire project that was everything and you did

project that was everything and you did it and I'm honestly super proud of you

it and I'm honestly super proud of you for sticking around this long it this

for sticking around this long it this this was not necessarily an easy project

this was not necessarily an easy project we used quite a few new things that I

we used quite a few new things that I may have not talked about or showed you

may have not talked about or showed you before this to me is just the beginning

before this to me is just the beginning right this is just a a glimpse into all

right this is just a a glimpse into all the things that you need to do you need

the things that you need to do you need to look for um in order to clean data so

to look for um in order to clean data so you know I really do think this is a

you know I really do think this is a good portfolio project because it will

good portfolio project because it will show that you understand and know how to

show that you understand and know how to clean the data although this is not an

clean the data although this is not an end to-end project right that could that

end to-end project right that could that would take a long time and a lot more

would take a long time and a lot more exploratory analysis looking into the

exploratory analysis looking into the data to to figure out what we need to

data to to figure out what we need to change but for all intents and purposes

change but for all intents and purposes I mean this is a a pretty good project

I mean this is a a pretty good project for cleaning data and I hope that you

for cleaning data and I hope that you learned something I also hope that you

learned something I also hope that you worked on this hard um if you want to

worked on this hard um if you want to make any improvements please do that

make any improvements please do that this is not perfect by any means there's

this is not perfect by any means there's other things that you could change um

other things that you could change um you could you know I don't even know I'm

you could you know I don't even know I'm not even going to try to guess you could

not even going to try to guess you could do other things to this data though um

do other things to this data though um and and create your own queries create

and and create your own queries create your own um data cleaning uh part of

your own um data cleaning uh part of this and so um you do that if you were

this and so um you do that if you were able to get this um the ETL part of it

able to get this um the ETL part of it done do that I think it'd be really

done do that I think it'd be really really cool um again I was able to get

really cool um again I was able to get it to work but I don't think 90% of

it to work but I don't think 90% of people out there would be able to get it

people out there would be able to get it to work um it's just every computer is

to work um it's just every computer is different every server is configured

different every server is configured differently um and so it would just be a

differently um and so it would just be a huge pain so I decided to cut that out

huge pain so I decided to cut that out and I'm sorry um but hopefully this will

and I'm sorry um but hopefully this will suffice um with that being said this is

suffice um with that being said this is it you made it all the way to the end

it you made it all the way to the end again I'm super proud you guys are doing

again I'm super proud you guys are doing fantastic you guys are the ones putting

fantastic you guys are the ones putting in the hard work to build the portfolio

in the hard work to build the portfolio for your future job I mean it's not easy

for your future job I mean it's not easy but you're putting in the work and so

but you're putting in the work and so and so kudos to you um in our next video

and so kudos to you um in our next video we're going to be going into python for

we're going to be going into python for the very first time really excited about

the very first time really excited about that one because um I think the only

that one because um I think the only python video that I have up right now is

python video that I have up right now is on one where I was scraping data from

on one where I was scraping data from Twitter so um you know this will be a

Twitter so um you know this will be a nice change a pace or a little bit

nice change a pace or a little bit different content than I normally put

different content than I normally put out and so I'm really excited about it

out and so I'm really excited about it and I hope you are as well with that

and I hope you are as well with that being said I am done with the video I'm

being said I am done with the video I'm going to be stopping it soon thank you

going to be stopping it soon thank you for joining me if you like this video be

for joining me if you like this video be sure to subscribe be sure to like this

sure to subscribe be sure to like this video leave a comment below um telling

video leave a comment below um telling me how it changed your life uh and I

me how it changed your life uh and I will see you in the next video

will see you in the next video [Music]

goodbye [Music]

[Music] what's going on everybody today we are

what's going on everybody today we are starting our Excel tutorial

starting our Excel tutorial [Music]

[Music] series now there are so many things that

series now there are so many things that you can do in Excel so I don't know how

you can do in Excel so I don't know how long this series is going to be it could

long this series is going to be it could be 15 or even 20 videos but what I do

be 15 or even 20 videos but what I do know is that I'm going to be covering

know is that I'm going to be covering just about every single thing that I've

just about every single thing that I've used since I became a data analyst and I

used since I became a data analyst and I want to show you how to do it uh so

want to show you how to do it uh so won't just be the more concrete things

won't just be the more concrete things um you know like pivot tables charts V

um you know like pivot tables charts V lookups things like that it'll also be

lookups things like that it'll also be some of the more nuanced things like how

some of the more nuanced things like how to deal with missing data or how to deal

to deal with missing data or how to deal with dirty data and how to clean that up

with dirty data and how to clean that up within Excel and so those are things

within Excel and so those are things that you may not be able to do you know

that you may not be able to do you know if somebody wasn't showing you how to do

if somebody wasn't showing you how to do it and so that's what I'm going to try

it and so that's what I'm going to try to help you because I know that that is

to help you because I know that that is something that you will need to do or

something that you will need to do or learn how to do in Excel now before we

learn how to do in Excel now before we get into it I want to give a huge shout

get into it I want to give a huge shout out to the sponsor of this Excel series

out to the sponsor of this Excel series and that is udem me I took so many Excel

and that is udem me I took so many Excel courses on you to me when I was first

courses on you to me when I was first starting out as a data analyst and there

starting out as a data analyst and there was this one course that I kept going

was this one course that I kept going back to over and over again because as I

back to over and over again because as I got into it in my job I realized that

got into it in my job I realized that there were so many things that were in

there were so many things that were in that course that I really needed to know

that course that I really needed to know but I didn't realize I needed to know it

but I didn't realize I needed to know it and so I'm going to put the links to

and so I'm going to put the links to those courses in the description in case

those courses in the description in case you want to take those again huge shout

you want to take those again huge shout out to you to me without further Ado

out to you to me without further Ado let's jump on my screen and get started

let's jump on my screen and get started with our very first Excel tutorial all

with our very first Excel tutorial all right so I'm going to go ahead and get

right so I'm going to go ahead and get rid of myself we are going to be looking

rid of myself we are going to be looking at something absolutely pivotal in your

at something absolutely pivotal in your data analytics career and that is Pivot

data analytics career and that is Pivot tables uh and I think that's really

tables uh and I think that's really appropriate it is probably one of the

appropriate it is probably one of the most commonly used things I think that

most commonly used things I think that data analysts use to convey information

data analysts use to convey information in Excel it's super easy to group things

in Excel it's super easy to group things together to display information in a

together to display information in a very easily understandable way

very easily understandable way especially for people who are not data

especially for people who are not data analysts right I use this a lot for

analysts right I use this a lot for other managers or for higher-ups um who

other managers or for higher-ups um who don't want to get into SQL or or you

don't want to get into SQL or or you know aren't super text savy in like

know aren't super text savy in like python or Tableau they just want it in

python or Tableau they just want it in an except sell and so I use it all the

an except sell and so I use it all the time for that reason and so we're going

time for that reason and so we're going to be using this data set right here

to be using this data set right here bike store sales in Europe I will

bike store sales in Europe I will include this link in the description um

include this link in the description um we're not going to look at the columns

we're not going to look at the columns just yet we're going to download it um

just yet we're going to download it um I've already downloaded it a few times

I've already downloaded it a few times but we are going to go

but we are going to go to um our downloads we're going to open

to um our downloads we're going to open it up and we're going to open up this

it up and we're going to open up this sales right

sales right here and give it a

here and give it a second all right perfect and so here's

second all right perfect and so here's what it looks like uh at least on my

what it looks like uh at least on my screen I'm going to uh spread it out

screen I'm going to uh spread it out just a little

just a little bit um and really quickly let's take a

bit um and really quickly let's take a very quick glance at this so we have a

very quick glance at this so we have a date a day a month a year so some um

date a day a month a year so some um some date

some date information um then we have some

information um then we have some customer age information so how old was

customer age information so how old was the customer again this is bike sales so

the customer again this is bike sales so what did um you know what did they buy

what did um you know what did they buy and they have some demographic

and they have some demographic information so this is their age group

information so this is their age group we have uh the gender country State the

we have uh the gender country State the product category the subcategory the

product category the subcategory the actual product that was purchased and

actual product that was purchased and then we have things like um you know how

then we have things like um you know how much these things cost the quantity that

much these things cost the quantity that was that was ordered so we have order

was that was ordered so we have order Quant quantity unit cost unit price then

Quant quantity unit cost unit price then we have the profit cost and revenue all

we have the profit cost and revenue all things that we almost everything in here

things that we almost everything in here we can in some way put into a pivot

we can in some way put into a pivot table now I'm not going to go through

table now I'm not going to go through every single variation of that but we

every single variation of that but we are going to be um looking at a lot of

are going to be um looking at a lot of this um Revenue over here because I

this um Revenue over here because I think it's it's pretty easy to show the

think it's it's pretty easy to show the value of a pivot table with especially

value of a pivot table with especially with um you know currency or money so

with um you know currency or money so what we're going to do to get started is

what we're going to do to get started is we're going to go up to insert and we're

we're going to go up to insert and we're going to click on insert and then we are

going to click on insert and then we are going to click on pivot table now really

going to click on pivot table now really quick there is a recommended pivot

quick there is a recommended pivot tables and if you click on that what

tables and if you click on that what will come up is some recommendations

will come up is some recommendations that Excel gives based on the data that

that Excel gives based on the data that you have um and it can kind of give you

you have um and it can kind of give you some ideas of of what you can do with

some ideas of of what you can do with pivot tables it's going to generate it

pivot tables it's going to generate it for you we're not going to do that we're

for you we're not going to do that we're going to build our

going to build our own uh but let's click on pivot table

own uh but let's click on pivot table and it's going to Auto Select basically

and it's going to Auto Select basically everything and that's fantastic um but

everything and that's fantastic um but what if it doesn't come like that I I

what if it doesn't come like that I I just erase that if it doesn't come like

just erase that if it doesn't come like that you can click right here you can

that you can click right here you can cck excuse me you can click control

cck excuse me you can click control shift and then the right arrow and then

shift and then the right arrow and then the down arrow and is going to select

the down arrow and is going to select all of our data um and you have right

all of our data um and you have right here a new worksheet or an existing

here a new worksheet or an existing worksheet we're going to create a new

worksheet we're going to create a new worksheet just tends to get too clogged

worksheet just tends to get too clogged up if we put it on the same worksheet

up if we put it on the same worksheet that already has a lot of data in it so

that already has a lot of data in it so right over here are pivot table fields

right over here are pivot table fields and these are all of our columns that we

and these are all of our columns that we just looked at and we're going to be

just looked at and we're going to be able to select those and kind of drag

able to select those and kind of drag and drop now if you just took the

and drop now if you just took the Tableau um tutorial series that I just

Tableau um tutorial series that I just finished doing last week then this is

finished doing last week then this is going to be pretty pretty familiar um

going to be pretty pretty familiar um you're going to start seeing a little

you're going to start seeing a little bit of um hopefully some patterns about

bit of um hopefully some patterns about how the data is kind of displayed and so

how the data is kind of displayed and so we have our filters down here we have

we have our filters down here we have columns rows

columns rows values all these things uh we will be

values all these things uh we will be using I'll show you how to use today as

using I'll show you how to use today as well as some additional things um one

well as some additional things um one thing that we want to start with uh for

thing that we want to start with uh for this demonstration is we're going to be

this demonstration is we're going to be looking at kind of the um these bottom

looking at kind of the um these bottom ones right here profit cost and Revenue

ones right here profit cost and Revenue and we're going to be doing that per

and we're going to be doing that per country uh per country and state and

country uh per country and state and we'll kind of do some drill Downs um and

we'll kind of do some drill Downs um and I'll show you how those work so for just

I'll show you how those work so for just to start out we're going to take the

to start out we're going to take the country right here and you'll see it

country right here and you'll see it populate right over here in fact um let

populate right over here in fact um let me zoom in maybe once uh yeah that

me zoom in maybe once uh yeah that should be fine I don't know if I want I

should be fine I don't know if I want I might zoom in it again in just a little

might zoom in it again in just a little bit um so we have our country and and

bit um so we have our country and and it's just like this very very simple

it's just like this very very simple oops um now I'm going to include the

oops um now I'm going to include the state now I'm going to drag this um all

state now I'm going to drag this um all the way and I'm going to put it under

the way and I'm going to put it under you can put it above or you can put it

you can put it above or you can put it below I'm going to put it

below I'm going to put it below uh it definitely makes the most

below uh it definitely makes the most sense there now when you do that it it

sense there now when you do that it it um kind of populates it in an expanded

um kind of populates it in an expanded way but you can collapse this very

way but you can collapse this very easily we're going to go right here

easily we're going to go right here we're going to right click we're going

we're going to right click we're going to go go down to expand and collapse and

to go go down to expand and collapse and we're going to collapse the entire field

we're going to collapse the entire field and so now here are all of our um all of

and so now here are all of our um all of our countries as they were before now

our countries as they were before now each of them has this plus sign to the

each of them has this plus sign to the left and if you click on it now we can

left and if you click on it now we can go and we see this state that we that we

go and we see this state that we that we added to these rows and what this is

added to these rows and what this is going to do is it kind of is like a

going to do is it kind of is like a rollup or it's like a grouping um and so

rollup or it's like a grouping um and so if you you know have taken the SQL um

if you you know have taken the SQL um tutorial series and you've done things

tutorial series and you've done things with Group by this is very similar to

with Group by this is very similar to that um and if you've done the Tableau

that um and if you've done the Tableau tutorial series it's kind of like a

tutorial series it's kind of like a drill down it's very very similar so you

drill down it's very very similar so you can drill into the information so we um

can drill into the information so we um can put some values in here uh and what

can put some values in here uh and what we're what that's going to do is that's

we're what that's going to do is that's going to kind of create some some

going to kind of create some some context to what this what we're grouping

context to what this what we're grouping by so just for um visual purposes let's

by so just for um visual purposes let's add this Revenue so this is the revenue

add this Revenue so this is the revenue that is bike uh bike sales revenue right

that is bike uh bike sales revenue right that's what we're looking at so this is

that's what we're looking at so this is the sum of the revenue for these bike

the sum of the revenue for these bike sales per country now if we drop down

sales per country now if we drop down right here we can see that in Australia

right here we can see that in Australia uh New South Wales had uh 92 was that

uh New South Wales had uh 92 was that 9,234 N5 Queensland had 5 million you

9,234 N5 Queensland had 5 million you know etc etc so now we can break it down

know etc etc so now we can break it down we can't it's we don't just have to look

we can't it's we don't just have to look at Australia we can now drill down even

at Australia we can now drill down even further to the actual state is what

further to the actual state is what they're calling it um the actual state

they're calling it um the actual state within Australia and so it's super super

within Australia and so it's super super useful and you can do that for every

useful and you can do that for every single one and so we can look at Canada

single one and so we can look at Canada we can look at France and we can really

we can look at France and we can really drill down into uh the revenue for each

drill down into uh the revenue for each of these countries as well as the states

of these countries as well as the states within them now over here this is not

within them now over here this is not the most uh pretty um it just says sum

the most uh pretty um it just says sum of Revenue and then it has some numbers

of Revenue and then it has some numbers not not the most pretty thing I've ever

not not the most pretty thing I've ever seen um really quick we can go like we

seen um really quick we can go like we can um kind of highlight over these and

can um kind of highlight over these and we can go back to home you can do it in

we can go back to home you can do it in a couple different ways we can go to

a couple different ways we can go to home and will type currency now it has

home and will type currency now it has these two. Z at the end you can get rid

these two. Z at the end you can get rid of those really easily by going like

of those really easily by going like that um already this looks quite a bit

that um already this looks quite a bit better just visually um especially if

better just visually um especially if you're looking at it in uh you know

you're looking at it in uh you know dollars you can change the currency um

dollars you can change the currency um to different currencies if you want to

to different currencies if you want to do that now we don't just have to do uh

do that now we don't just have to do uh the sum of Revenue we can do a lot of

the sum of Revenue we can do a lot of different things so let's go to the

different things so let's go to the value field settings so we can customize

value field settings so we can customize this name so we can do um Revenue oops

this name so we can do um Revenue oops good if I get spell Revenue per

good if I get spell Revenue per country that's fine that you know it's

country that's fine that you know it's just a placeholder trying to show you

just a placeholder trying to show you but we don't have to just do that um you

but we don't have to just do that um you know we could do the count the average

know we could do the count the average the max the Min we can do just about

the max the Min we can do just about anything we want um but let's keep it

anything we want um but let's keep it the sum right now um and if we want to

the sum right now um and if we want to we can show this value as different

we can show this value as different things so we percentage the percentage

things so we percentage the percentage of column total percentage of row total

of column total percentage of row total let's do really quick just for

let's do really quick just for demonstration purposes the percentage of

demonstration purposes the percentage of grand total so when we do that we can

grand total so when we do that we can see that the United States the per

see that the United States the per Revenue per country United States has

Revenue per country United States has 32% just between these um you know these

32% just between these um you know these countries and Australia has the next one

countries and Australia has the next one so you know it might be kind of hard to

so you know it might be kind of hard to glance at this really quickly to know

glance at this really quickly to know who has the highest um but what we can

who has the highest um but what we can do is we can go right here and we can go

do is we can go right here and we can go to sort and we can do largest to

to sort and we can do largest to smallest and there we have the United

smallest and there we have the United States on top now when you do it right

States on top now when you do it right here it's not sorted largest uh to

here it's not sorted largest uh to smallest you'd have to go in again click

smallest you'd have to go in again click sort and do largest to smallest and so

sort and do largest to smallest and so now we can see that California has the

now we can see that California has the has the um you know biggest percentage

has the um you know biggest percentage they're pulling in 20% of that 32% of

they're pulling in 20% of that 32% of Revenue so I'm just going to click C

Revenue so I'm just going to click C control z a few times and get us back to

control z a few times and get us back to where we just were um and what I want to

where we just were um and what I want to do is I want to show you a few different

do is I want to show you a few different things uh pretty quickly so we want to

things uh pretty quickly so we want to pull in this profit and this cost uh and

pull in this profit and this cost uh and so I'm going to pull in this cost next

so I'm going to pull in this cost next and then I'm going to pull in this

and then I'm going to pull in this profit again uh I'm going to

profit again uh I'm going to change the currency on

change the currency on this and I'm not going to change the

this and I'm not going to change the names um right now but you know you

names um right now but you know you absolutely can do that now the revenue

absolutely can do that now the revenue is the how much is actually being sold

is the how much is actually being sold so you know for the United States it was

so you know for the United States it was 27 million now the cost is how much did

27 million now the cost is how much did it cost to manufacture or or store um or

it cost to manufacture or or store um or distribute all of these products so that

distribute all of these products so that was 60 million and the profit is

was 60 million and the profit is actually how much money is being made at

actually how much money is being made at the end of the day after um you know all

the end of the day after um you know all their costs after all their employee

their costs after all their employee costs after everything they're still

costs after everything they're still making the United States is still making

making the United States is still making $1

$1 million now you might look at this and

million now you might look at this and you might say well you know I can kind

you might say well you know I can kind of glance at it and say know that this

of glance at it and say know that this profit is correct based off these two

profit is correct based off these two numbers um but we can do a calculated

numbers um but we can do a calculated field um if you remember what calculated

field um if you remember what calculated fields are that's something from Tableau

fields are that's something from Tableau very uh basically the exact same thing

very uh basically the exact same thing and so we can create an additional

and so we can create an additional column right here that is a calculated

column right here that is a calculated field that can add and subtract these

field that can add and subtract these things to make sure that our numbers are

things to make sure that our numbers are adding up correctly

adding up correctly so let's do that really quickly U let's

so let's do that really quickly U let's go to pivot table analyze we're going to

go to pivot table analyze we're going to go over to Fields items and sets and go

go over to Fields items and sets and go to calculated field now we can name this

to calculated field now we can name this anything um and I'm just going to for

anything um and I'm just going to for demo purposes I'm going to say um oops

demo purposes I'm going to say um oops calculated field demo uh I'm sure yours

calculated field demo uh I'm sure yours will be different now um if you want to

will be different now um if you want to you can go in here and this is the

you can go in here and this is the formula it's almost like um you know we

formula it's almost like um you know we haven't looked at formulas up this is

haven't looked at formulas up this is our first tutorial but you know when we

our first tutorial but you know when we look at formulas it's basically the same

look at formulas it's basically the same thing as writing it if inside of a cell

thing as writing it if inside of a cell but here it gives us kind of this um

but here it gives us kind of this um open text to do how we uh do what we

open text to do how we uh do what we want with it now what we're going to do

want with it now what we're going to do is we're going to do Revenue I'm going

is we're going to do Revenue I'm going to insert that I'm going to get rid of

to insert that I'm going to get rid of this I'm going to do revenue and so

this I'm going to do revenue and so that's the the the very large number and

that's the the the very large number and then we're going to

then we're going to subtract and we're going to sub subract

subtract and we're going to sub subract our cost we going to insert that and

our cost we going to insert that and let's do this and click okay so this is

let's do this and click okay so this is our calculated field demo column that we

our calculated field demo column that we just created and as you can see it

just created and as you can see it matches our uh sum of profit column

matches our uh sum of profit column exactly and that's exactly what we want

exactly and that's exactly what we want to see we want to kind of check to make

to see we want to kind of check to make sure that this revenue and cost uh

sure that this revenue and cost uh fields are generating the correct profit

fields are generating the correct profit and sometimes those are off and so it's

and sometimes those are off and so it's really good to kind of check those and

really good to kind of check those and have that additional column um You

have that additional column um You probably wouldn't have this if you were

probably wouldn't have this if you were um you know going to submit this to

um you know going to submit this to somebody uh just so you know now that

somebody uh just so you know now that this is an actual column you can't go

this is an actual column you can't go here and do something like cut or and

here and do something like cut or and paste it over here you know that's not

paste it over here you know that's not it won't let you do that what it is is

it won't let you do that what it is is is now an actual um column and so we can

is now an actual um column and so we can go and remove that and we can add it

go and remove that and we can add it back at any moment so if we want to go

back at any moment so if we want to go back and add that um oops add that down

back and add that um oops add that down here we can do that because we've

here we can do that because we've created that column it's now permanently

created that column it's now permanently there unless we go and delete all of

there unless we go and delete all of that data uh and so we can just click

that data uh and so we can just click this check mark and it will get rid of

this check mark and it will get rid of it for us all right now the last thing

it for us all right now the last thing that we have not used down here is the

that we have not used down here is the filters now the filters is exactly what

filters now the filters is exactly what it sounds like it's going to allow you

it sounds like it's going to allow you to filter on certain things um but

to filter on certain things um but probably not things that you already

probably not things that you already have included in your pivot table so if

have included in your pivot table so if you add something like the country down

you add something like the country down here um it's going to kind of expand

here um it's going to kind of expand everything and then if you then go and

everything and then if you then go and filter on it it kind of breaks it down

filter on it it kind of breaks it down that's really not what the filter is

that's really not what the filter is kind of used for or meant for um for

kind of used for or meant for um for example right up here we have uh

example right up here we have uh customer gender okay so let's take the

customer gender okay so let's take the customer gender and we'll put it in this

customer gender and we'll put it in this filters now we can see all of the

filters now we can see all of the revenue all of the cost all the profit

revenue all of the cost all the profit and we can do that based off of the

and we can do that based off of the gender so we can filter by a gender not

gender so we can filter by a gender not really having to change anything about

really having to change anything about our pivot table and so at a super Quick

our pivot table and so at a super Quick Glance we can see that uh the males are

Glance we can see that uh the males are the profit from the males is

16.48% so at a super uh basic level at a really quick glance we can see that the

really quick glance we can see that the men or the males are you know spending a

men or the males are you know spending a little bit more than the females by

little bit more than the females by about about

about about $700,000 now let's go ahead and create

$700,000 now let's go ahead and create one more pivot table uh we are going to

one more pivot table uh we are going to create a pivot table right over here

create a pivot table right over here let's go back to the

let's go back to the sales right here again control shift

sales right here again control shift right down it's going to select all of

right down it's going to select all of our data and we're click okay so one

our data and we're click okay so one thing that we're going to look at is

thing that we're going to look at is we're going to use some of this date

we're going to use some of this date information right here so let's select

information right here so let's select our country just like we did before um

our country just like we did before um and what we want to do is see you know

and what we want to do is see you know what year were we performing our best

what year were we performing our best when were we doing our absolute best uh

when were we doing our absolute best uh with oops

with oops let me go

let me go back uh with our sales so I'm going to

back uh with our sales so I'm going to select the year and put that in our

select the year and put that in our columns and so now we have 2011 through

columns and so now we have 2011 through 2016 and we want to look at our Revenue

2016 and we want to look at our Revenue let's put our Revenue right down here

let's put our Revenue right down here and now we have all of our Revenue now

and now we have all of our Revenue now let's again make this into a

let's again make this into a currency just like that and super

currency just like that and super quickly now we can get a really quick

quickly now we can get a really quick glance at at how Australia was doing

glance at at how Australia was doing each year and we can see that there was

each year and we can see that there was a huge uptick in 2013 and a huge uptick

a huge uptick in 2013 and a huge uptick in 2015 it didn't happen for every

in 2015 it didn't happen for every single country uh it did go up uh for

single country uh it did go up uh for most countries very slightly for some

most countries very slightly for some but we can see on a large scale from um

but we can see on a large scale from um year to year what that's like and So

year to year what that's like and So within just a few minutes we're able to

within just a few minutes we're able to create some really useful pivot tables

create some really useful pivot tables that anybody could look at and

that anybody could look at and understand and that's really the biggest

understand and that's really the biggest use of these PIV pivot tables is that

use of these PIV pivot tables is that you can kind of group these things

you can kind of group these things together show some uh information and

together show some uh information and data at at kind of a broad larger scale

data at at kind of a broad larger scale and make it to where anybody who's

and make it to where anybody who's looking at it can understand it that is

looking at it can understand it that is why pivot tables are so useful and so I

why pivot tables are so useful and so I hope that this video was helpful I hope

hope that this video was helpful I hope that I was able to walk through it and

that I was able to walk through it and help you better understand how pivot

help you better understand how pivot tables work and how you can use them

tables work and how you can use them when you are working within Excel thank

when you are working within Excel thank you guys so much for watching I really

you guys so much for watching I really appreciate it if you like this video be

appreciate it if you like this video be sure to like And subscribe below and

sure to like And subscribe below and I'll see you in the next video

I'll see you in the next video [Music]

[Music] what's going on everybody today we're

what's going on everybody today we're going to be looking at formulas in

going to be looking at formulas in [Music]

[Music] Excel now I know what you're thinking

Excel now I know what you're thinking there's absolutely no way that you're

there's absolutely no way that you're going to be able to show us every single

going to be able to show us every single formula in Excel and you're absolutely

formula in Excel and you're absolutely right but I am going to show you some of

right but I am going to show you some of my favorites and the ones that I found

my favorites and the ones that I found the most useful and then you can go

the most useful and then you can go ahead and practice those and try those

ahead and practice those and try those out and if there are ones that you

out and if there are ones that you really want me to do and you think that

really want me to do and you think that I missed put it in the comments below

I missed put it in the comments below and I will see those and I'll try to

and I will see those and I'll try to make a list of those and make another

make a list of those and make another video on formulas and include all of

video on formulas and include all of those as well and now before we jump

those as well and now before we jump into the actual tutorial I want to give

into the actual tutorial I want to give a huge shout out to the sponsor of the

a huge shout out to the sponsor of the series and that is udemy you guys

series and that is udemy you guys already know if you have watched any of

already know if you have watched any of my videos that I absolutely love udem me

my videos that I absolutely love udem me I mean honestly they were the ones who

I mean honestly they were the ones who got me started and were able ble to give

got me started and were able ble to give me affordable courses for me to get

me affordable courses for me to get started as a data analyst I learned SQL

started as a data analyst I learned SQL and Excel and python all through udimi

and Excel and python all through udimi courses and so if you are looking for a

courses and so if you are looking for a platform to take a course I absolutely

platform to take a course I absolutely recommend you look at udemy they have

recommend you look at udemy they have fantastic sales going on right now

fantastic sales going on right now especially during the holiday season in

especially during the holiday season in this new year and so if you're looking

this new year and so if you're looking to take a full-fledged Excel course I

to take a full-fledged Excel course I have some of my favorites in the

have some of my favorites in the description below and now without

description below and now without further Ado let's jump onto my screen

further Ado let's jump onto my screen and get started with the tutorial all

and get started with the tutorial all right now before we start I want to say

right now before we start I want to say that this is not like every other

that this is not like every other tutorial that I have created created

tutorial that I have created created this one is very streamlined okay so I

this one is very streamlined okay so I already know exactly what I'm going to

already know exactly what I'm going to do there's not going to be much messing

do there's not going to be much messing around I left little notes here and

around I left little notes here and there um and I'm going to try to get

there um and I'm going to try to get through it because there's a lot of them

through it because there's a lot of them to get through um so all these ones at

to get through um so all these ones at the bottom now these are ones that I use

the bottom now these are ones that I use a lot that I think are useful again if

a lot that I think are useful again if you know other ones that you use a lot

you know other ones that you use a lot that think that I should be using which

that think that I should be using which I know there are ones that I left out of

I know there are ones that I left out of here you know put it in the comments um

here you know put it in the comments um I'll see the ones that people are liking

I'll see the ones that people are liking and I will I will create more videos on

and I will I will create more videos on the because I know there are so many I

the because I know there are so many I also will save this um excel in on the

also will save this um excel in on the GitHub so you can go and download it it

GitHub so you can go and download it it will be exactly what you're looking at

will be exactly what you're looking at right now I highly recommend trying

right now I highly recommend trying these formulas out for yourself so you

these formulas out for yourself so you can get a feel for how they work and how

can get a feel for how they work and how they're actually used and you can mess

they're actually used and you can mess around with it yourself so um as you can

around with it yourself so um as you can see at the bottom we're going to start

see at the bottom we're going to start with Max Min and then we're going to go

with Max Min and then we're going to go on to some more I think a little bit

on to some more I think a little bit more uh difficult things um and all

more uh difficult things um and all these things are super useful I'll try

these things are super useful I'll try to talk about how you can actually use

to talk about how you can actually use it as we go through it some are super

it as we go through it some are super self-explanatory but some may not be so

self-explanatory but some may not be so this one I think is super

this one I think is super self-explanatory but again one that

self-explanatory but again one that you're going to use all the time um and

you're going to use all the time um and so what we can do is we can say equal

so what we can do is we can say equal and that's how you kind of start off

and that's how you kind of start off saying this is going to be a formula in

saying this is going to be a formula in this cell equal means uh I am now

this cell equal means uh I am now creating a formula and we're going to

creating a formula and we're going to say

say MX and I'll hit Tab and so it'll kind of

MX and I'll hit Tab and so it'll kind of populate it and right here if you've

populate it and right here if you've never seen a formula before it'll to

never seen a formula before it'll to give you what the inputs need to be so

give you what the inputs need to be so it's going to say Max of number one

it's going to say Max of number one number two etc etc what we're going to

number two etc etc what we're going to do is we're going to give a range so

do is we're going to give a range so we're going to go from here down to here

we're going to go from here down to here you don't have to close the parenthesis

you don't have to close the parenthesis but you can I'm going to and then you

but you can I'm going to and then you hit enter and so for this date it's

hit enter and so for this date it's going to give us the max date now these

going to give us the max date now these are um the start dates for these people

are um the start dates for these people right here and so if we just kind of

right here and so if we just kind of glance through here we can see that 2013

glance through here we can see that 2013 was the last year and this one is

was the last year and this one is actually the latest in that year and so

actually the latest in that year and so it gave us the correct one the Min is

it gave us the correct one the Min is going to do the exact opposite it's

going to do the exact opposite it's going to give us uh the smallest and so

going to give us uh the smallest and so we'll give it the same range we'll close

we'll give it the same range we'll close the parenthesis and it's going to say

the parenthesis and it's going to say December 7th of 1995 and we can see that

December 7th of 1995 and we can see that that is correct so Michael Scott started

that is correct so Michael Scott started in 1995 the earliest of all the

in 1995 the earliest of all the employees um and you can do the exact

employees um and you can do the exact same thing for really any of these

same thing for really any of these columns we can see who the who's making

columns we can see who the who's making the most money or at least what the

the most money or at least what the higher salary is U so we'll do Max and

higher salary is U so we'll do Max and then we'll do the salary range and so

then we'll do the salary range and so this is this one again uh whoops what

this is this one again uh whoops what did I do oh I did the wrong range didn't

did I do oh I did the wrong range didn't I no I didn't do the wrong range it's

I no I didn't do the wrong range it's just there it goes uh this column was a

just there it goes uh this column was a date range or a date column for whatever

date range or a date column for whatever reason let me get rid of that uh and

reason let me get rid of that uh and then we can do equals Min and we'll do

then we can do equals Min and we'll do again we'll do the salary and at a quick

again we'll do the salary and at a quick glance we can see that Pam Beasley is

glance we can see that Pam Beasley is making the least and 65,000 is Michael

making the least and 65,000 is Michael Scott who's making uh that so super

Scott who's making uh that so super simple it shows the max it shows the Min

simple it shows the max it shows the Min you can select a range there you go

you can select a range there you go let's move on to if and ifs now if is um

let's move on to if and ifs now if is um I think pretty straightforward so all

I think pretty straightforward so all you're going to do is you're going to

you're going to do is you're going to say if this then that um ifs is a little

say if this then that um ifs is a little bit different so ifs is you can you can

bit different so ifs is you can you can put multiple conditions and as we're

put multiple conditions and as we're writing it I'll show you kind of what it

writing it I'll show you kind of what it the conditions that need to be met all

the conditions that need to be met all right so we're going to click right here

right so we're going to click right here we're going to say equal we're going to

we're going to say equal we're going to do if hit Tab and we need a logical test

do if hit Tab and we need a logical test uh and so we're going to give it a range

uh and so we're going to give it a range or or or something we're going to say if

or or or something we're going to say if it's equal greater to um something like

it's equal greater to um something like that then we're going to say if the

that then we're going to say if the value is true what's the what is going

value is true what's the what is going to be the output or if the value is

to be the output or if the value is false what's going to be the output so

false what's going to be the output so let's do this right

let's do this right here we'll do this age range and so if

here we'll do this age range and so if they are greater than let's say let's do

they are greater than let's say let's do 30 if they're greater than 30 we're

30 if they're greater than 30 we're going to do a comma and so if the value

going to do a comma and so if the value is true what what should be the output

is true what what should be the output uh if they're greater than 30 we're

uh if they're greater than 30 we're going to call them old and then if it is

going to call them old and then if it is false so if they're younger than 30 what

false so if they're younger than 30 what should it say and we're going to say

should it say and we're going to say young and we'll close the

young and we'll close the parenthesis and there you go so if

parenthesis and there you go so if they're over 30 then they are going to

they're over 30 then they are going to have young or if they're younger than 30

have young or if they're younger than 30 they're going to have young now this is

they're going to have young now this is something where you need to specify if

something where you need to specify if you want 30 and over or over 30 we chose

you want 30 and over or over 30 we chose over 30 so 30 is not included in that um

over 30 so 30 is not included in that um so they're going to be

so they're going to be young now uh let's get we don't actually

young now uh let's get we don't actually need two of these that's pretty

need two of these that's pretty self-explanatory the ifs is a little bit

self-explanatory the ifs is a little bit different right you can have multiple

different right you can have multiple conditions so let's open that up real

conditions so let's open that up real quick so ifs and now we have a logical

quick so ifs and now we have a logical test value if uh that's true then you

test value if uh that's true then you can do logical test two value if that's

can do logical test two value if that's true um so you can have multiple

true um so you can have multiple multiple multiple things now this one is

multiple multiple things now this one is a little bit different in this one oops

a little bit different in this one oops let me get out of this in this one you

let me get out of this in this one you had a value of true a value of false ifs

had a value of true a value of false ifs does not have that ifs is going to give

does not have that ifs is going to give you um different ranges in different

you um different ranges in different specific conditions

specific conditions and you can't say if this one's false

and you can't say if this one's false you're just going to have multiple

you're just going to have multiple conditions so let's do equals and ifs

conditions so let's do equals and ifs Tab and we'll do our first logical test

Tab and we'll do our first logical test so let's

so let's do

do um if the

um if the salesman or if that equals to

salesman or if that equals to salesman we're going to say we're going

salesman we're going to say we're going to respond with

to respond with sales so that's if the value is true

sales so that's if the value is true that's what we want the output to be now

that's what we want the output to be now we're going to go on to our logical test

we're going to go on to our logical test two so you're going to see this pattern

two so you're going to see this pattern right if this is our conditional or

right if this is our conditional or logical test so if this is true this is

logical test so if this is true this is what's going to be returned so you'll

what's going to be returned so you'll notice that's just a a pretty simple

notice that's just a a pretty simple pattern we can just do random things so

pattern we can just do random things so if it's equal to sales um and we'll just

if it's equal to sales um and we'll just do the same one if that is equal

do the same one if that is equal to say HR we can say fire

to say HR we can say fire immediately and now we're going to

immediately and now we're going to say if it's equal

to regional

regional manager and we

manager and we say give

say give Christmas bonus and we'll close the

Christmas bonus and we'll close the parenthesis and let's see what we get so

parenthesis and let's see what we get so as you can see there's no default value

as you can see there's no default value for true or false like like this one

for true or false like like this one there was a logical test and if it was

there was a logical test and if it was true there was a value and if it was

true there was a value and if it was false there was a value so for every

false there was a value so for every single one you'll get a value for this

single one you'll get a value for this one that's not exactly going to happen

one that's not exactly going to happen as you can see there are these

as you can see there are these Nas now when that happens it just means

Nas now when that happens it just means nothing met that condition so we never

nothing met that condition so we never said anything about supplier relations

said anything about supplier relations we never said anything about accountants

we never said anything about accountants but if it was part of that ifs statement

but if it was part of that ifs statement then it got something um and so that is

then it got something um and so that is how the ifs works now let's move on to

how the ifs works now let's move on to length uh this is exactly what we're

length uh this is exactly what we're going to do but you know some of the

going to do but you know some of the uses for this U for the length I've used

uses for this U for the length I've used it for a lot of different things um one

it for a lot of different things um one thing that I've used it for in the past

thing that I've used it for in the past and you know Max and ifs you know you

and you know Max and ifs you know you can use it for almost anything length is

can use it for almost anything length is there's a lot of different use cases one

there's a lot of different use cases one I used to work with a lot of um customer

I used to work with a lot of um customer data or patient data they had like

data or patient data they had like Social Security numbers and if you know

Social Security numbers and if you know there was bad Social Security numbers we

there was bad Social Security numbers we didn't want to include that and so we do

didn't want to include that and so we do like the length of that and if a social

like the length of that and if a social security number was let's say 10 numbers

security number was let's say 10 numbers or 11 numbers where it should only be

or 11 numbers where it should only be nine or or you know however many they

nine or or you know however many they are I think it's nine then we know that

are I think it's nine then we know that that social security number is incorrect

that social security number is incorrect and then we can get rid of that or

and then we can get rid of that or discard it from our results that's just

discard it from our results that's just an example right um so for this oops

an example right um so for this oops what I do that I did control Z to undo

what I do that I did control Z to undo that if you didn't know how to do that

that if you didn't know how to do that um so we're going to do equals Len which

um so we're going to do equals Len which is length um and again if you didn't see

is length um and again if you didn't see that it Returns the number of characters

that it Returns the number of characters in a text string so let's go right here

in a text string so let's go right here and let's go to uh let's go to their

and let's go to uh let's go to their last name and we'll give it a range so

last name and we'll give it a range so it's going to tell us how many

it's going to tell us how many characters are in that string so for

characters are in that string so for halber it's seven characters for

halber it's seven characters for flenderson it's 10 characters and we're

flenderson it's 10 characters and we're able to see a length and so again there

able to see a length and so again there are a lot of different use cases for

are a lot of different use cases for this uh the social security number was

this uh the social security number was one another one is phone numbers right

one another one is phone numbers right if you look at the length of the phone

if you look at the length of the phone numbers and there's ones that are like

numbers and there's ones that are like 12 numbers long you know those might not

12 numbers long you know those might not be ones that are accurate and you need

be ones that are accurate and you need to go look at them and see if you want

to go look at them and see if you want to include them in your results or your

to include them in your results or your output so that is how length is done

output so that is how length is done let's move right over to the left and

let's move right over to the left and right um I I might be going a little

right um I I might be going a little fast but uh you know I'm keeping it I'm

fast but uh you know I'm keeping it I'm keeping it live I'm keeping this on our

keeping it live I'm keeping this on our feet uh so let's keep going left and

feet uh so let's keep going left and right um are kind of like substrings if

right um are kind of like substrings if you've taken the the sequel um tutorial

you've taken the the sequel um tutorial series that I've done uh substrings are

series that I've done uh substrings are where you can choose a certain part of

where you can choose a certain part of the text string and you can extract data

the text string and you can extract data from that um and usually have to

from that um and usually have to reference a certain number so a certain

reference a certain number so a certain amount of characters that's the exact

amount of characters that's the exact same thing except uh unfortunately

same thing except uh unfortunately there's no substring there's substitute

there's no substring there's substitute but there's no substring left and right

but there's no substring left and right is really the closest thing that we have

is really the closest thing that we have so let's kind of take a look real quick

so let's kind of take a look real quick and see what we can do so we're going to

and see what we can do so we're going to do left and it's going to say Returns

do left and it's going to say Returns the specified number of characters from

the specified number of characters from the start of a text string so we're

the start of a text string so we're starting from the very far left and we

starting from the very far left and we need to choose our text and then choose

need to choose our text and then choose the number of characters that we're

the number of characters that we're going to be looking over so let's go

going to be looking over so let's go over here and let's just choose you know

over here and let's just choose you know start symol uh we'll get a little bit

start symol uh we'll get a little bit more advanced so we have um this is our

more advanced so we have um this is our text range so these are the the the ones

text range so these are the the the ones that we want to look at and then how

that we want to look at and then how many characters do we want to look

many characters do we want to look forward and we'll just choose three as

forward and we'll just choose three as an example and so you can see that it

an example and so you can see that it takes the first three characters from

takes the first three characters from every single thing now you can also do

every single thing now you can also do this with numbers it doesn't just have

this with numbers it doesn't just have to be um you know name with with actual

to be um you know name with with actual words or letters you can do the exact

words or letters you can do the exact same thing so you can say

same thing so you can say write um and we're going to choose our

write um and we're going to choose our our string uh and let's do this one so

our string uh and let's do this one so you know all of them start with 100 um

you know all of them start with 100 um and we'll just say we want to take the

and we'll just say we want to take the last one so this one is going to start

last one so this one is going to start from the very far right and go over one

from the very far right and go over one character

character so right here you can see this is our

so right here you can see this is our range and I just chose one so starting

range and I just chose one so starting from the very far right we go over one

from the very far right we go over one character and that's what we take and so

character and that's what we take and so that can definitely be useful another

that can definitely be useful another one that you can do and this one is one

one that you can do and this one is one that I have used so many times I mean

that I have used so many times I mean honestly countless times in in actually

honestly countless times in in actually using this in my job uh so we're going

using this in my job uh so we're going to go from the right and we're going to

to go from the right and we're going to look at a date so you know sometimes you

look at a date so you know sometimes you have these date structures month month

have these date structures month month day day year year year or year um you

day day year year year or year um you know day month year all these different

know day month year all these different and sometimes you just want to extract

and sometimes you just want to extract either the month or the year or or

either the month or the year or or something like that the day and so we

something like that the day and so we want to come in here we're just going to

want to come in here we're just going to extract the oops I wanted to make that

extract the oops I wanted to make that arrange we want to extract the year of

arrange we want to extract the year of the start dates so we're going to do

the start dates so we're going to do that and then we're going to go over

that and then we're going to go over four so we want to take the first four

four so we want to take the first four characters from the right to give us the

characters from the right to give us the entire year let's do that and now we can

entire year let's do that and now we can see exactly the year and this can be

see exactly the year and this can be just super super useful this is again

just super super useful this is again one that I've used used a lot and so

one that I've used used a lot and so that is one that you might want to

that is one that you might want to remember in case you're ever doing

remember in case you're ever doing analysis on you know start and end dates

analysis on you know start and end dates or or anything with um date data uh

or or anything with um date data uh again one that I highly recommend

again one that I highly recommend remembering let's go over to date to

remembering let's go over to date to text I actually probably should have

text I actually probably should have included that um before because I

included that um before because I actually used it in this one um if you

actually used it in this one um if you notice right here this is a text so in

notice right here this is a text so in in this one we just did that was a text

in this one we just did that was a text you can't do this right on um start and

you can't do this right on um start and end dates when it's a date uh format and

end dates when it's a date uh format and let me show you so this is a date now if

let me show you so this is a date now if I do equals and you know we just did

I do equals and you know we just did this uh let's do on the end date and

this uh let's do on the end date and I'll do the whole range give me a second

I'll do the whole range give me a second and we'll do

and we'll do four it's giving us completely random

four it's giving us completely random numbers why is that because underneath

numbers why is that because underneath the date range there are um numbers

the date range there are um numbers right so if I go right here and I make

right so if I go right here and I make this a general it's going to have the

this a general it's going to have the numbers and look these are the first

numbers and look these are the first four characters from the right and so

four characters from the right and so it's doing what it's supposed to do but

it's doing what it's supposed to do but uh it's not doing what we actually want

uh it's not doing what we actually want and that's the issue so how can we

and that's the issue so how can we convert this now there are a ton of

convert this now there are a ton of different ways um but the quickest

different ways um but the quickest probably the easiest besides actually

probably the easiest besides actually writing writing it out like this like

writing writing it out like this like 11-2

11-2 d201 which then converts it to a date

d201 which then converts it to a date format um but what you can do you know

format um but what you can do you know just so you know you can create a as a

just so you know you can create a as a text you can do 11-2

text you can do 11-2 d201 and now it will stay a text string

d201 and now it will stay a text string and as you can tell these are a little

and as you can tell these are a little bit different because this one is uh

bit different because this one is uh formatted or situated on the right and

formatted or situated on the right and this one's on the left that's how you

this one's on the left that's how you can tell the difference now if you don't

can tell the difference now if you don't want to do it by hand uh completely

want to do it by hand uh completely manually and waste hours of your time

manually and waste hours of your time you can do it in a very simple way so

you can do it in a very simple way so we're going to do uh text so this is the

we're going to do uh text so this is the exact um form for that we're going to

exact um form for that we're going to use so let's get rid of that one there

use so let's get rid of that one there we go so we're going to do equals we're

we go so we're going to do equals we're going to do uh oops text it says

going to do uh oops text it says converts a value to text in a specific

converts a value to text in a specific number format so for a date format we

number format so for a date format we can choose a date format and then it'll

can choose a date format and then it'll convert it to a text for us which saves

convert it to a text for us which saves so much time I promise you uh let's do

so much time I promise you uh let's do all of these just like we did and then

all of these just like we did and then we need to tell it what the format is if

we need to tell it what the format is if we don't if we tell it something

we don't if we tell it something incorrect it's going to give us a

incorrect it's going to give us a completely terrible output or just give

completely terrible output or just give us an error alog together so this is a

us an error alog together so this is a DayDay month Monon year year year year

DayDay month Monon year year year year format and that is what we're going to

format and that is what we're going to do so we're going to do

do so we're going to do ddmm y y YY and close that up and there

ddmm y y YY and close that up and there you go and now we well because it's in a

you go and now we well because it's in a formula what we need to do

formula what we need to do is copy

is copy this and past paste it right over here

this and past paste it right over here and now you can see that is a general

and now you can see that is a general this is something that we can use as a

this is something that we can use as a string and let's just check it just to

string and let's just check it just to make sure we're going to do right we're

make sure we're going to do right we're going to do this one let's do all of

going to do this one let's do all of them and we'll do

them and we'll do four and there you go so now it works

four and there you go so now it works that is what we are looking for um and

that is what we are looking for um and you can do that imagine doing that with

you can do that imagine doing that with millions of rows or you know let's say

millions of rows or you know let's say 10,000 rows it's going to be a breeze

10,000 rows it's going to be a breeze right it's going to take you two minutes

right it's going to take you two minutes or a minute

or a minute to do everything that you want to do

to do everything that you want to do instead of having to just do a bunch of

instead of having to just do a bunch of mess to convert it to a string which I

mess to convert it to a string which I promise you I've done it it just takes

promise you I've done it it just takes forever it's it's terrible so that is uh

forever it's it's terrible so that is uh date to text super helpful formula let's

date to text super helpful formula let's go over to trim now I I purposefully

go over to trim now I I purposefully messed up this column now why do I did I

messed up this column now why do I did I mess it up like this because when you're

mess it up like this because when you're working with real data you're going to

working with real data you're going to get data like this it it's messy it's

get data like this it it's messy it's dirty it just has random spaces at the

dirty it just has random spaces at the end for no reason um because sometimes

end for no reason um because sometimes you're going to be working with um data

you're going to be working with um data that is inputed by a user it's not like

that is inputed by a user it's not like a drop- down option so imagine

a drop- down option so imagine somebody's typing this in they

somebody's typing this in they accidentally put a space so they

accidentally put a space so they actually put an enter or something and

actually put an enter or something and then they submit it and this is how it's

then they submit it and this is how it's going to look in the database um and if

going to look in the database um and if you're a data engineer or you know

you're a data engineer or you know you're working with the raw data if they

you're working with the raw data if they don't clean that up then you're going to

don't clean that up then you're going to be working with that that dirty data and

be working with that that dirty data and I I guarantee you if you're working as a

I I guarantee you if you're working as a data analyst you're going to see stuff

data analyst you're going to see stuff like this not with maybe a last name but

like this not with maybe a last name but all sorts of data so we're going to go

all sorts of data so we're going to go right here we're going to say equals

right here we're going to say equals trim do open parenthesis actually this

trim do open parenthesis actually this says removes all spaces from a text

says removes all spaces from a text string except for a single space between

string except for a single space between words so like you know if it said

words so like you know if it said Halpert space uh or gy space Halpert it

Halpert space uh or gy space Halpert it won't take the space in between there

won't take the space in between there because it it kind of understands that

because it it kind of understands that the in normal language space is supposed

the in normal language space is supposed to be there so it won't do that um but

to be there so it won't do that um but we'll take that we'll give it this

we'll take that we'll give it this Range close that up and there you go now

Range close that up and there you go now it is nice and clean much more usable

it is nice and clean much more usable now let's look at concatenate one that I

now let's look at concatenate one that I have used just way way way too many

have used just way way way too many times um and something that I've used

times um and something that I've used concatenate for and you'll see this one

concatenate for and you'll see this one in a lot of demonstrations for a good

in a lot of demonstrations for a good reason is because a lot of people use it

reason is because a lot of people use it for this um so what you can do is you

for this um so what you can do is you can say equals um and well let me tell

can say equals um and well let me tell you what concatenate does real

you what concatenate does real quick so what concatenate does oops I'm

quick so what concatenate does oops I'm totally messing up here um but it joins

totally messing up here um but it joins two or more text strings into one string

two or more text strings into one string it basically joins things together and

it basically joins things together and adds them together so let's do

adds them together so let's do concatenate and we're going to add this

concatenate and we're going to add this first and last name again one that gets

first and last name again one that gets used all the time but that's because um

used all the time but that's because um it really is useful so you can do this

it really is useful so you can do this and you can say now now I want to

and you can say now now I want to include this so concatenating this and

include this so concatenating this and this and let's take a look so it says

this and let's take a look so it says Jim Halpert U but it's all connected and

Jim Halpert U but it's all connected and that's typically not how people write

that's typically not how people write their names so what we can do is we can

their names so what we can do is we can go back in here and we can do what my

go back in here and we can do what my demonstration up here already tells us

demonstration up here already tells us to do which is we're just going to add

to do which is we're just going to add another thing in here and if we add two

another thing in here and if we add two parentheses we can include anything in

parentheses we can include anything in here we can include a dash we can

here we can include a dash we can include an exclamation point or we can

include an exclamation point or we can just include a space so let's just

just include a space so let's just include a space really quick and just

include a space really quick and just like that it works perfectly and so now

like that it works perfectly and so now we have the full name now something that

we have the full name now something that you could use it for is something like

you could use it for is something like generating uh an email this is something

generating uh an email this is something that you absolutely could do um and it's

that you absolutely could do um and it's you know pretty simple so I'm going to

you know pretty simple so I'm going to do it like this I'm G to say oops what

do it like this I'm G to say oops what did I do I'm G to say um Dot and then at

did I do I'm G to say um Dot and then at the end I'm going to say at

the end I'm going to say at oops comma

oops comma quotation

quotation gmail.com and now I've created emails

gmail.com and now I've created emails for all of these people so just

for all of these people so just something that you can do with this um

something that you can do with this um and something that it it absolutely is

and something that it it absolutely is used for and you'll see that

used for and you'll see that demonstration almost everywhere because

demonstration almost everywhere because honestly it gets used a lot um by data

honestly it gets used a lot um by data analysts and so uh you know just a good

analysts and so uh you know just a good one to know understanding how that that

one to know understanding how that that concatenation works um let's go over to

concatenation works um let's go over to the next one

the next one so we are going to do substitute now

so we are going to do substitute now substitute's really interesting um there

substitute's really interesting um there are different ways you can do it I'm

are different ways you can do it I'm going to show it to you on these dates

going to show it to you on these dates real quick uh that's what we're going to

real quick uh that's what we're going to look at so changing a date format

look at so changing a date format changing how what it's supposed to look

changing how what it's supposed to look like is absolutely something that

like is absolutely something that happens all the time and um you know

happens all the time and um you know sometimes you'll even get it like

sometimes you'll even get it like this where it'll look like it'll be

this where it'll look like it'll be messy it'll be different a different um

messy it'll be different a different um I guess format so this one has all the

I guess format so this one has all the other ones have slashes where these ones

other ones have slashes where these ones have

have dashes and you know what you can do is

dashes and you know what you can do is if you want to well let me actually go

if you want to well let me actually go with the no instances real quick because

with the no instances real quick because this one is uh actually makes the most

this one is uh actually makes the most sense um so we'll do equals and we're

sense um so we'll do equals and we're going to say

going to say substitute and oops and let me say

substitute and oops and let me say substitute replaces existing text with

substitute replaces existing text with new text in a text string so if we do an

new text in a text string so if we do an open parenthesis it says we take the

open parenthesis it says we take the text have the old text we have the new

text have the old text we have the new text and then we have how what instance

text and then we have how what instance or how many times uh or or or what

or how many times uh or or or what instance are we looking at it and I'll

instance are we looking at it and I'll explain that in a little

explain that in a little bit so the text that we're going to be

bit so the text that we're going to be looking at is this one right here so

looking at is this one right here so let's take this

let's take this range and the old is we're going to take

range and the old is we're going to take this Dash and so let's take the

this Dash and so let's take the dash and then what do we want to replace

dash and then what do we want to replace replace it with we want to replace it

replace it with we want to replace it with this slash right here I think it's

with this slash right here I think it's a forward slash isn't that what it's

a forward slash isn't that what it's called it's called a forward slash am I

called it's called a forward slash am I crazy um and we're not going to put an

crazy um and we're not going to put an instance notice that that's in a bracket

instance notice that that's in a bracket that means it's optional we're going to

that means it's optional we're going to do none of that um and what it's going

do none of that um and what it's going to do is it's going to fix this so this

to do is it's going to fix this so this one is now in the correct format that we

one is now in the correct format that we want uh and that's fantastic that's you

want uh and that's fantastic that's you know that's what we tried to accomplish

know that's what we tried to accomplish given what we had now let's fix that if

given what we had now let's fix that if we want to do the exact same thing uh we

we want to do the exact same thing uh we can say

can say uh what are we doing substitute we can

uh what are we doing substitute we can do substitute we can do open parentheses

do substitute we can do open parentheses we'll give the range and now let's say

we'll give the range and now let's say we want to change all of them to a

we want to change all of them to a different format so instead of the um

different format so instead of the um forward slash I'm going to keep calling

forward slash I'm going to keep calling it that if that's correct we want to

it that if that's correct we want to give it a dash and so then we close that

give it a dash and so then we close that and now all of them are in this new

and now all of them are in this new format so it it's able to substitute a

format so it it's able to substitute a specific value for a new value and if

specific value for a new value and if you don't include an instance

you don't include an instance then it'll do it to every single one in

then it'll do it to every single one in there so let's go over here and we're

there so let's go over here and we're going to actually use the the um the the

going to actually use the the um the the instance num and I'll show you what that

instance num and I'll show you what that does uh and so really quick we'll do the

does uh and so really quick we'll do the exact same thing that we just did we'll

exact same thing that we just did we'll do the forward slash and we want to

do the forward slash and we want to replace it with this one again this Dash

replace it with this one again this Dash but we only want to do it on the first

but we only want to do it on the first instance of that forward slash and so as

instance of that forward slash and so as you can see all the ones that um all the

you can see all the ones that um all the ones that were replaced are the very

ones that were replaced are the very first instance whereas the second

first instance whereas the second instance which is the second time it

instance which is the second time it appears in this string does not get

appears in this string does not get touched so if we take

touched so if we take this and we put it right over here and

this and we put it right over here and we move it to

we move it to two it's kind of the opposite so the

two it's kind of the opposite so the first one wasn't touched the second one

first one wasn't touched the second one was so we're choosing which instance or

was so we're choosing which instance or which time it shows up in that string

which time it shows up in that string and then it replaces it if you do not

and then it replaces it if you do not choose an instance it chooses all of

choose an instance it chooses all of them so this can be super useful if you

them so this can be super useful if you want to do like a bulk replace um but

want to do like a bulk replace um but you only want to do it on a specific

you only want to do it on a specific column um and you just want to use a

column um and you just want to use a formula really quick right um and so you

formula really quick right um and so you can use this in a lot of different ways

can use this in a lot of different ways so that's how you're able to actually do

so that's how you're able to actually do it with the first instance the second

it with the first instance the second instance and if you don't include an

instance and if you don't include an instance at all let's go over to the sum

instance at all let's go over to the sum uh this is one I think everyone knows

uh this is one I think everyone knows how to use but I want to show you two

how to use but I want to show you two other ones um as well so let's go to the

other ones um as well so let's go to the sum and we're just going to do equals

sum and we're just going to do equals the sum and I hope you know what this is

the sum and I hope you know what this is well not hope I if you don't know what

well not hope I if you don't know what this is it just adds up all the numbers

this is it just adds up all the numbers in range so we're going to add sum means

in range so we're going to add sum means add so we're going to take this and it's

add so we're going to take this and it's going to give us the uh what all these

going to give us the uh what all these salaries are together so super super

salaries are together so super super simple Su is one of probably the most

simple Su is one of probably the most basic formulas that you can do um some

basic formulas that you can do um some if is a little bit different you can add

if is a little bit different you can add an if statement which we learned right

an if statement which we learned right back here you can add an if statement

back here you can add an if statement and then add it if it meets a certain

and then add it if it meets a certain criteria all right so we're going to do

criteria all right so we're going to do equals some if and then you're going to

equals some if and then you're going to need to give a range in criteria and you

need to give a range in criteria and you can include a some range if you would

can include a some range if you would like so we're going to do the salary

like so we're going to do the salary again we going to do a comma and now

again we going to do a comma and now here's our criteria let's do if they

here's our criteria let's do if they have greater than 50,000 for their

have greater than 50,000 for their salary and close our parenthesis so now

salary and close our parenthesis so now it's only going to add up if their

it's only going to add up if their salary is greater than 50,000 now his is

salary is greater than 50,000 now his is 50,000 exactly so that won't count but

50,000 exactly so that won't count but we have 63 and 65,000 which does equal

we have 63 and 65,000 which does equal 128,000 so it it just gives a specific

128,000 so it it just gives a specific criteria or an if statement then it does

criteria or an if statement then it does the addition uh so super useful on that

the addition uh so super useful on that one so that is how you do a su if and Su

one so that is how you do a su if and Su ifs is kind of the same thing as we did

ifs is kind of the same thing as we did back here there's the if and the ifs so

back here there's the if and the ifs so the ifs is going to be if it has it

the ifs is going to be if it has it meets multiple conditions so let's take

meets multiple conditions so let's take a look at that one so let's do um equals

a look at that one so let's do um equals some ifs now uh oops now the Syntax for

some ifs now uh oops now the Syntax for this one is going to be a little bit

this one is going to be a little bit different you'll see that in just a

different you'll see that in just a second this adds the cells specified by

second this adds the cells specified by a given set of conditions or criteria so

a given set of conditions or criteria so let's do an open open parentheses we

let's do an open open parentheses we give the sum range so let's do um the

give the sum range so let's do um the same one as before then we have our

same one as before then we have our criteria range so what are we looking at

criteria range so what are we looking at What's um this is the area that's going

What's um this is the area that's going to be added after all these if

to be added after all these if statements are done right so we have to

statements are done right so we have to initially set that now we're going to

initially set that now we're going to say okay what criteria are we basing

say okay what criteria are we basing this off of so let's put a comma and

this off of so let's put a comma and we're going to base it off of let's do

we're going to base it off of let's do this one we'll say um if the uh gender

this one we'll say um if the uh gender so we'll do comma if that's female oops

so we'll do comma if that's female oops if that's female and then we'll give

if that's female and then we'll give another one we can say if they're female

another one we can say if they're female and let's say they are greater than oops

and let's say they are greater than oops greater than 30 and we'll close that up

greater than 30 and we'll close that up and it's going to give us 88,000 so

and it's going to give us 88,000 so female female there's one two right here

female female there's one two right here so it's going to be this one and this

so it's going to be this one and this one that equals 88,000 so that's how

one that equals 88,000 so that's how that works you're able to incorporate

that works you're able to incorporate several different

several different conditions into uh the sum formula so

conditions into uh the sum formula so again I know this one's super simple but

again I know this one's super simple but you you can use it in a much more

you you can use it in a much more complex way if you use the sum if and

complex way if you use the sum if and the sum ifs um almost the exact same

the sum ifs um almost the exact same thing for this count I'm not going to go

thing for this count I'm not going to go super in depth into this one um I'll

super in depth into this one um I'll just kind of show you because count is

just kind of show you because count is um count and sum are kind of on the same

um count and sum are kind of on the same level of difficulty they're both pretty

level of difficulty they're both pretty beginner this is just going to give you

beginner this is just going to give you a count of how many cells um are there

a count of how many cells um are there so let's give this range um and so it's

so let's give this range um and so it's not going to add it it's just going to

not going to add it it's just going to give us a count so if we do right here

give us a count so if we do right here and scroll over them like highlight them

and scroll over them like highlight them this countdown here oops this countdown

this countdown here oops this countdown here is nine and so it's going to give

here is nine and so it's going to give us that count but we can do a count with

us that count but we can do a count with conditions exactly how we did it in the

conditions exactly how we did it in the sum so if we do count if Oops I did not

sum so if we do count if Oops I did not spell that right if we do count if we're

spell that right if we do count if we're going to give a range and a criteria

going to give a range and a criteria exact same as we did before so let's do

exact same as we did before so let's do this I me you can do this on basically

this I me you can do this on basically any of these it doesn't really for this

any of these it doesn't really for this demonstration it doesn't really matter

demonstration it doesn't really matter um but we'll say if their salary is

um but we'll say if their salary is greater than 45,000 so how many people

greater than 45,000 so how many people this is going to give us how many people

this is going to give us how many people have a salary over 45,000 and that's

have a salary over 45,000 and that's five so before in the sum if if we did

five so before in the sum if if we did that um we did 50,000 it adds everything

that um we did 50,000 it adds everything together the count is just going to

together the count is just going to count the amount of cells that meet that

count the amount of cells that meet that criteria and again count

criteria and again count ifs uh we're going to have a criteria

ifs uh we're going to have a criteria range and then we will specify what if

range and then we will specify what if statements we want to be uh to occur in

statements we want to be uh to occur in order to count those cells so let's do

order to count those cells so let's do we want you know we want to count it can

we want you know we want to count it can be any range or it can be any of these

be any range or it can be any of these we'll do the ID this time and now we can

we'll do the ID this time and now we can say you know want it to be is our

say you know want it to be is our criteria one we can say we want it to be

criteria one we can say we want it to be greater than want their ID to be greater

greater than want their ID to be greater than

than 1005 and let's say we want them to

be male so they have an ID over a certain

male so they have an ID over a certain um a certain range and then they are a

um a certain range and then they are a male so there's only three people that

male so there's only three people that meet that criteria and so it'll be

meet that criteria and so it'll be Michael Stanley and Kevin those are our

Michael Stanley and Kevin those are our three people and so it gives us a count

three people and so it gives us a count very useful to give quick numbers like

very useful to give quick numbers like this something I I genuinely use a lot

this something I I genuinely use a lot and I know I've said that a lot during

and I know I've said that a lot during this tutorial but that's because

this tutorial but that's because everything I'm showing you are things

everything I'm showing you are things that I've used a lot so I don't feel

that I've used a lot so I don't feel like um you know I'm speaking out of

like um you know I'm speaking out of turn here let's look at this one this

turn here let's look at this one this one is very um has some specific use

one is very um has some specific use cases um notice that this is a text

cases um notice that this is a text right now um if you do it when it is uh

right now um if you do it when it is uh in a date format it actually will not

in a date format it actually will not work I mean I can you can test it out

work I mean I can you can test it out yourself you just got to trust me it's

yourself you just got to trust me it's not going to work so what this does is

not going to work so what this does is it's going to give you the range from

it's going to give you the range from this day to this day that's what it's

this day to this day that's what it's going to do so let's do uh oops days

going to do so let's do uh oops days it's GNA we want to choose our end date

it's GNA we want to choose our end date so this is our end date it's kind of

so this is our end date it's kind of backward from what you think end date to

backward from what you think end date to start date you think start date to end

start date you think start date to end date so you have to start with this one

date so you have to start with this one and then we're going to choose the start

and then we're going to choose the start date and now it's going to tell us how

date and now it's going to tell us how many um how many uh days was it from

many um how many uh days was it from here to here and this one it's

here to here and this one it's 5,56 so Network days is extremely

5,56 so Network days is extremely similar except it takes out holidays and

similar except it takes out holidays and it takes out weekends and you can see

it takes out weekends and you can see how many working days has this person um

how many working days has this person um how many working days or network days

how many working days or network days has this person worked not including you

has this person worked not including you know weekends and holidays have they

know weekends and holidays have they actually worked since their start date

actually worked since their start date and their end date so let's do Network

and their end date so let's do Network days and we need our start date our end

days and we need our start date our end date and you can specify extra holidays

date and you can specify extra holidays if you'd like but there are a already

if you'd like but there are a already standard set holidays in there that it

standard set holidays in there that it takes out um so you know if you want to

takes out um so you know if you want to do that you can so we're going to do the

do that you can so we're going to do the start date again this one's different

start date again this one's different this one says start date end date and

this one says start date end date and then we're going to give the end

then we're going to give the end date and if you

date and if you notice they are going to be different

notice they are going to be different numbers is dramatically lower because

numbers is dramatically lower because it's taking out weekends and holidays so

it's taking out weekends and holidays so this is how many days uh calendar days

this is how many days uh calendar days they've worked and this is how many days

they've worked and this is how many days they've actually been in the office and

they've actually been in the office and worked and that is it um again there are

worked and that is it um again there are so many formulas I mean literally

so many formulas I mean literally hundreds of formulas that you can

hundreds of formulas that you can utilize and use and are out there for

utilize and use and are out there for you to try out yourself if there are

you to try out yourself if there are specific ones that I did not cover in

specific ones that I did not cover in this video please please put it in the

this video please please put it in the comments below so that I can you know

comments below so that I can you know show you how to do these things I I I

show you how to do these things I I I will say I've probably used a majority

will say I've probably used a majority of the ones that you're going to put in

of the ones that you're going to put in the comments already and if I haven't

the comments already and if I haven't used it I'll take a look at it and see

used it I'll take a look at it and see if it's really useful and I'll show you

if it's really useful and I'll show you that so thank you guys so much for

that so thank you guys so much for watching I hope that this has been

watching I hope that this has been helpful I I feel like a lot of these

helpful I I feel like a lot of these things are not things that I learned

things are not things that I learned before I started almost all these are

before I started almost all these are ones that I learned while I was on the

ones that I learned while I was on the job and so I'm hoping that you can get

job and so I'm hoping that you can get ahead of the curve and you can learn

ahead of the curve and you can learn learn these things before you actually

learn these things before you actually start so that when you get in there

start so that when you get in there you're just like killing it with the

you're just like killing it with the formulas and people are like whoa this

formulas and people are like whoa this guy is like this guy knows what he's

guy is like this guy knows what he's doing in Excel give him all the Excel

doing in Excel give him all the Excel work and then you become like you know

work and then you become like you know just the Excel guy um and everyone you

just the Excel guy um and everyone you know loves you for it so with that being

know loves you for it so with that being said thank you so much for watching I

said thank you so much for watching I really do hope this helped if you like

really do hope this helped if you like this video be sure to like And subscribe

this video be sure to like And subscribe below I'll see you in the next

below I'll see you in the next [Music]

[Music] video

what's going on everybody welcome back to another video in this Excel tutorial

to another video in this Excel tutorial we'll be looking at

[Music] xlup now if you don't already know what

xlup now if you don't already know what xlookup is it is a new feature in Excel

xlookup is it is a new feature in Excel to kind of replace vlookup or to be a

to kind of replace vlookup or to be a much better option at least in my mind

much better option at least in my mind is a much better option than V lookup

is a much better option than V lookup and so if you're someone who's either

and so if you're someone who's either used V lookup a a lot and you're trying

used V lookup a a lot and you're trying to you know learn this new Option or if

to you know learn this new Option or if you've never used it before this video

you've never used it before this video will be super helpful because I'll walk

will be super helpful because I'll walk you through kind of the options and what

you through kind of the options and what x lookup can do as well as the

x lookup can do as well as the difference between X lookup and V lookup

difference between X lookup and V lookup but before we get into the tutorial I

but before we get into the tutorial I want to give a huge shout out to today's

want to give a huge shout out to today's sponsor and that is udemy udemy is the

sponsor and that is udemy udemy is the go-to place if you want a full-fledged

go-to place if you want a full-fledged course in Excel I have three options of

course in Excel I have three options of courses that I have taken on em me so

courses that I have taken on em me so I'd highly recommend checking those out

I'd highly recommend checking those out they are having a huge sale on all their

they are having a huge sale on all their courses during this time and so if you

courses during this time and so if you are in the market for a course I highly

are in the market for a course I highly recommend checking out UD to me and

recommend checking out UD to me and getting one there now without further

getting one there now without further Ado let's jum on my screen and start the

Ado let's jum on my screen and start the tutorial all right so let's get me off

tutorial all right so let's get me off the screen because we all know why we're

the screen because we all know why we're here so I didn't include this in the

here so I didn't include this in the formulas video last week because I knew

formulas video last week because I knew this was going to be a large one and a

this was going to be a large one and a lot of people are going to want to know

lot of people are going to want to know how to do this what the difference

how to do this what the difference stream V lookup and X lookup is so it

stream V lookup and X lookup is so it has its own dedicated video to it so

has its own dedicated video to it so let's get started it is a Formula so

let's get started it is a Formula so we're going to come in here in this cell

we're going to come in here in this cell we're going to hit equal and then we're

we're going to hit equal and then we're going to start typing X lookup now I'm

going to start typing X lookup now I'm GNA hit tab in just a second but let's

GNA hit tab in just a second but let's read what this says it says searches a

read what this says it says searches a range or an array for a match and

range or an array for a match and Returns the corresponding item from a

Returns the corresponding item from a second range or array by default an

second range or array by default an exact match is used so really useful to

exact match is used so really useful to know um we'll talk a little bit more

know um we'll talk a little bit more about that in just a second let's hit

about that in just a second let's hit Tab and it's going to complete it and

Tab and it's going to complete it and it's going to start giving us or it's

it's going to start giving us or it's going to tell us what our input values

going to tell us what our input values need to be we're going to have our

need to be we're going to have our lookup value we're going to have our

lookup value we're going to have our lookup array our return array and then

lookup array our return array and then some options things like if not found so

some options things like if not found so if your option isn't found you know what

if your option isn't found you know what will be um you know the the uh output

will be um you know the the uh output that it gives us a match mode and a

that it gives us a match mode and a search mode and I'm going to show you um

search mode and I'm going to show you um kind of how to use every single one of

kind of how to use every single one of these things as you can see at the very

these things as you can see at the very bottom I've kind of already set up all

bottom I've kind of already set up all of the instructional um instructional

of the instructional um instructional content for this video and so we'll kind

content for this video and so we'll kind of get through all these different

of get through all these different scenarios so let's just start really

scenarios so let's just start really quickly with um how to use it very

quickly with um how to use it very simply with the lookup lookup array and

simply with the lookup lookup array and return array so we're going to come in

return array so we're going to come in here and we're going to give it our

here and we're going to give it our lookup value Now Toby Fenderson right

lookup value Now Toby Fenderson right over here in A3 is going to be our

over here in A3 is going to be our lookup value so that's who we're going

lookup value so that's who we're going to be searching for now we're going to

to be searching for now we're going to hit comma and now we're going to be

hit comma and now we're going to be needing to look up uh or to input our

needing to look up uh or to input our lookup array now an array is just uh you

lookup array now an array is just uh you know a range basically so we're going to

know a range basically so we're going to do this is where it's going to be

do this is where it's going to be searching for um that value this is

searching for um that value this is where it's searches for A3 so here's

where it's searches for A3 so here's Toby Fenderson here's Toby flenderson so

Toby Fenderson here's Toby flenderson so it will find it in this array right here

it will find it in this array right here then we're going to hit comma and now we

then we're going to hit comma and now we need to give it the return array what

need to give it the return array what it's going to return on that row when it

it's going to return on that row when it finds it so we're going to return his

finds it so we're going to return his email keep it really simple so what it

email keep it really simple so what it should do and let's close parentheses

should do and let's close parentheses what it should do is it should take Toby

what it should do is it should take Toby Fenderson it's going to search in this

Fenderson it's going to search in this column or in this array and then it's

column or in this array and then it's going to return the email when it finds

going to return the email when it finds Toby Fenderson so it's on Toby Fenderson

Toby Fenderson so it's on Toby Fenderson is on row six so it's going to find Toby

is on row six so it's going to find Toby flenderson it's going to come over here

flenderson it's going to come over here and it's going to return Toby flenderson

and it's going to return Toby flenderson dundermifflin corporate.com that's what

dundermifflin corporate.com that's what it should do let's see what it actually

it should do let's see what it actually does said enter and it returns it now if

does said enter and it returns it now if we drag it down like this it'll apply it

we drag it down like this it'll apply it to all of these names right here and it

to all of these names right here and it works exactly how it's supposed to um

works exactly how it's supposed to um again if you have never used vlookup you

again if you have never used vlookup you don't know how good you have it okay

don't know how good you have it okay vlookup um was extremely useful but just

vlookup um was extremely useful but just uh a bit complicated and I'll talk about

uh a bit complicated and I'll talk about that near the end of the video when we

that near the end of the video when we compare V lookup to xlookup but just

compare V lookup to xlookup but just know that if you're using X lookup for

know that if you're using X lookup for the first time and you're just getting

the first time and you're just getting into using Excel you guys have it good

into using Excel you guys have it good okay so just know that um now let's go

okay so just know that um now let's go over here to X lookup multiple rows

over here to X lookup multiple rows because you can return more than one

because you can return more than one output with um with X lookup so let's go

output with um with X lookup so let's go right in here and we're going to

right in here and we're going to basically write the exact same thing as

basically write the exact same thing as we did before so let's write X lookup

we did before so let's write X lookup we're going to do Toby flenderson as our

we're going to do Toby flenderson as our value we're going to search here and

value we're going to search here and we're going to do something a little bit

we're going to do something a little bit different this time we want to include

different this time we want to include our end date and the email so what we're

our end date and the email so what we're going to do is we're going to start here

going to do is we're going to start here we're going to go down all the way to

we're going to go down all the way to the bottom of end date and then we're

the bottom of end date and then we're also going to include the email and when

also going to include the email and when we do that it will uh in the output give

we do that it will uh in the output give us a row or a column for end dat and a

us a row or a column for end dat and a column for email so an output for both

column for email so an output for both so let's hit enter and now we can see

so let's hit enter and now we can see that we have the end date here and the

that we have the end date here and the email here now one of the downsides or

email here now one of the downsides or or something that I'm not a huge huge

or something that I'm not a huge huge fan of is well first off I love that you

fan of is well first off I love that you can do this that's fantastic um but it

can do this that's fantastic um but it have to be right next to each other so

have to be right next to each other so you're only going to get that output

you're only going to get that output exactly how it is in the columns so if I

exactly how it is in the columns so if I went and did this range um I would

went and did this range um I would include all of that um so H you know

include all of that um so H you know let's just for example let's pull that

let's just for example let's pull that down here so let's take

down here so let's take this and put it right here if I did

this and put it right here if I did instead of zero or or O2 to P10 if I

instead of zero or or O2 to P10 if I included age to email this whole range

included age to email this whole range and I hit enter it's all going to be

and I hit enter it's all going to be included so you know that's one of the

included so you know that's one of the small downsides of of that functionality

small downsides of of that functionality of when you can use multiple rows is

of when you can use multiple rows is that it's going to use the rows exactly

that it's going to use the rows exactly as they are you can't really customize

as they are you can't really customize it within the formula you can move

it within the formula you can move around um these columns to how you want

around um these columns to how you want it um so that is something to note and

it um so that is something to note and again you can pull this down and it'll

again you can pull this down and it'll be applied to all of those names let's

be applied to all of those names let's go over to X lookup exact match so let's

go over to X lookup exact match so let's open this up we're going to do equals

open this up we're going to do equals xlup as we've been doing and we're

xlup as we've been doing and we're actually going to be looking at the if

actually going to be looking at the if not found and the match mode U both you

not found and the match mode U both you know on this tab right here so let's do

know on this tab right here so let's do what we've been doing before we take our

what we've been doing before we take our value that we're looking up we take the

value that we're looking up we take the array that we're looking and we're going

array that we're looking and we're going to do the email and you know as you can

to do the email and you know as you can see this says Toby flender and not Toby

see this says Toby flender and not Toby flenderson so what we are going to do is

flenderson so what we are going to do is we're going to hit comma and if it's not

we're going to hit comma and if it's not found you can return um a value or a

found you can return um a value or a string that you want to return now for

string that you want to return now for simple purposes or for simple

simple purposes or for simple instructional purposes we're going to do

instructional purposes we're going to do not

not found and then we're going to close that

found and then we're going to close that off so let's do this and Toby Fenderson

off so let's do this and Toby Fenderson was not found and so it was returned not

was not found and so it was returned not found if Toby Fender was actually in

found if Toby Fender was actually in this full name then it would have

this full name then it would have returned the email and then if along the

returned the email and then if along the way you know one of these was not part

way you know one of these was not part of it then you know we would have uh we

of it then you know we would have uh we would have had the KN found all right so

would have had the KN found all right so let's go right up here we're actually

let's go right up here we're actually just going to copy this uh because I

just going to copy this uh because I want to reuse it um and then we're going

want to reuse it um and then we're going to go right here and we hit a comma now

to go right here and we hit a comma now this is our match mode option and so we

this is our match mode option and so we have four different options that we can

have four different options that we can choose from a zero is an exact match and

choose from a zero is an exact match and that is by default that is what we have

that is by default that is what we have or what we use then there's a minus one

or what we use then there's a minus one that's an exact match or next smaller

that's an exact match or next smaller item then there's a one which is an

item then there's a one which is an exact match or next larger item and then

exact match or next larger item and then there's a two which is a wild card

there's a two which is a wild card character match now we're going to do

character match now we're going to do that and we are going to um you know try

that and we are going to um you know try this out and it's not going to work and

this out and it's not going to work and not just because I forgot to put A4 um

not just because I forgot to put A4 um it's doing it because it's searching for

it's doing it because it's searching for Beasley but if there's not a wild card

Beasley but if there's not a wild card option already put in here um it doesn't

option already put in here um it doesn't recognize it so we need to indicate

recognize it so we need to indicate where that wild card needs to be so

where that wild card needs to be so we're going to do a double apostrophe or

we're going to do a double apostrophe or quotation marks we're going to put put

quotation marks we're going to put put an asterisk right here and then do

an asterisk right here and then do another one and we're going to hit an

another one and we're going to hit an Amper sand so we're going to have an

Amper sand so we're going to have an Amper sand right here and when that's

Amper sand right here and when that's going to say is anything that comes

going to say is anything that comes before A4 anything that comes before

before A4 anything that comes before Beasley is okay doesn't matter what it

Beasley is okay doesn't matter what it is as long as it has Beasley at the end

is as long as it has Beasley at the end that is going to be okay so we're going

that is going to be okay so we're going to have Pam that comes before Beasley

to have Pam that comes before Beasley and that's going to tell it and it's

and that's going to tell it and it's going to say okay I know that anything

going to say okay I know that anything that comes before Beasley is all right

that comes before Beasley is all right and so when we hit enter is now going to

and so when we hit enter is now going to return the output that we are looking

return the output that we are looking for and we can include that on these as

for and we can include that on these as well now this one is Meredith um and so

well now this one is Meredith um and so Meredith is at the beginning so we have

Meredith is at the beginning so we have Meredith Palmer so we can actually take

Meredith Palmer so we can actually take this and we're going to put this at the

this and we're going to put this at the end put the Amber sand right here and

end put the Amber sand right here and now it'll work and the exact same thing

now it'll work and the exact same thing for Kevin Malo right here Kevin Malone

for Kevin Malo right here Kevin Malone so it just didn't include uh the ne at

so it just didn't include uh the ne at the end and so it's still going to work

the end and so it's still going to work if we include that asterisk at the end

if we include that asterisk at the end now I know I said we were looking at

now I know I said we were looking at search order but I'm actually going to

search order but I'm actually going to kind of give you an exact match uh first

kind of give you an exact match uh first and then search order but it just kind

and then search order but it just kind of easier to show it over here so I'm

of easier to show it over here so I'm going to do X look up I'm going to look

going to do X look up I'm going to look up this value do a comma here's the

up this value do a comma here's the range this is our start date that's it's

range this is our start date that's it's going to be looking for and I want to

going to be looking for and I want to return the full name now no value in

return the full name now no value in here has one one 2000 but what we can do

here has one one 2000 but what we can do is we can do comma and then a comma for

is we can do comma and then a comma for the match mode and do an exact match or

the match mode and do an exact match or next

next larger and I know this is in the exact

larger and I know this is in the exact match part but it you know kind of

match part but it you know kind of refers to search ORD a little bit um

refers to search ORD a little bit um where it searches for the next largest

where it searches for the next largest value that's that's what that number one

value that's that's what that number one represents the next larger value so we

represents the next larger value so we have 112000 and if we look right here

have 112000 and if we look right here the next value above 112000 is

the next value above 112000 is 152000 and so it should should return

152000 and so it should should return Angela Martin let's see if that works

Angela Martin let's see if that works and there it is now let's look up the

and there it is now let's look up the actual search order um so let's do

actual search order um so let's do equals x

equals x lookup this is the value that we want to

lookup this is the value that we want to be searching for and we're going to be

be searching for and we're going to be looking in this start date and comma and

looking in this start date and comma and we want to return the name now let's get

we want to return the name now let's get over to search mode now the search mode

over to search mode now the search mode performs a search starting at the first

performs a search starting at the first item so at the very top going down so by

item so at the very top going down so by default it searches from first to last

default it searches from first to last but you can reverse that and do search

but you can reverse that and do search from last to first or you can do a

from last to first or you can do a binary search which is where it sorts in

binary search which is where it sorts in ascending order or sorts in descending

ascending order or sorts in descending order um and that's with the actual

order um and that's with the actual value and so we won't be able to show

value and so we won't be able to show this binary search or on ascending or

this binary search or on ascending or descending because our values are the

descending because our values are the same but if we had different values and

same but if we had different values and we were looking up um using this um next

we were looking up um using this um next largest we we would be able to show that

largest we we would be able to show that but I'm going to show you the search

but I'm going to show you the search from first to last and last to first so

from first to last and last to first so let's put in by default and this is what

let's put in by default and this is what it would be search From First to Last

it would be search From First to Last what the default would be so it starts

what the default would be so it starts at the very top it goes down and finds

at the very top it goes down and finds the first 56 2001 and returns Toby

the first 56 2001 and returns Toby flenderson now if we go in here and we

flenderson now if we go in here and we hit minus one that is going to search

hit minus one that is going to search from last to first so it's going to

from last to first so it's going to start at the bottom and go to the top

start at the bottom and go to the top and the first one that it finds is

and the first one that it finds is Michael Scott so that's that first one

Michael Scott so that's that first one starting from the bottom and then the

starting from the bottom and then the Michael Scott right there so these two

Michael Scott right there so these two the exact match and the search order can

the exact match and the search order can kind of be combined into um this one

kind of be combined into um this one right here we're using this

right here we're using this one um which is you know exact match or

one um which is you know exact match or next larger and you can include that in

next larger and you can include that in this binary search in this one as well

this binary search in this one as well all right now let's head over to the X

all right now let's head over to the X lookup horizontal I think we're we only

lookup horizontal I think we're we only have a few left yep X look up horizontal

have a few left yep X look up horizontal then we'll do X lookup with sum and then

then we'll do X lookup with sum and then I'm going to show you the V lookup at

I'm going to show you the V lookup at the end so let's go right here let's say

the end so let's go right here let's say equals X lookup the value that we want

equals X lookup the value that we want to be searching for is February that's

to be searching for is February that's what we're looking for hit comma and

what we're looking for hit comma and where do we want to search to find

where do we want to search to find February we want to search in uh these

February we want to search in uh these calendar months and then we hit another

calendar months and then we hit another comma and now we're going to be

comma and now we're going to be searching for paper so let's do paper

searching for paper so let's do paper and we'll hit enter and it found

and we'll hit enter and it found February and it return paper right here

February and it return paper right here and we can do that for paper printer and

and we can do that for paper printer and manila folders and so it's going to give

manila folders and so it's going to give us the 310 the 40 and the 118 from

us the 310 the 40 and the 118 from February now let's go right over here to

February now let's go right over here to XL up with some um I actually it's

XL up with some um I actually it's basically a carbon copy of this uh let's

basically a carbon copy of this uh let's take this over here real

take this over here real quick and place it right there because

quick and place it right there because it's the exact same thing except at the

it's the exact same thing except at the end we're going to use I'm going to show

end we're going to use I'm going to show you how to use sum with the X lookup at

you how to use sum with the X lookup at the same time now um we're going to be

the same time now um we're going to be using the formula sum and

using the formula sum and so we're going to do sum and then within

so we're going to do sum and then within the sum our first number is going to be

the sum our first number is going to be an X lookup and then our next value is

an X lookup and then our next value is also going to be an X lookup so let's do

also going to be an X lookup so let's do X lookup and now we're going to search

X lookup and now we're going to search for our very first value oops our very

for our very first value oops our very first lookup value so we're going to go

first lookup value so we're going to go to

to i1 and then we're going to search this

i1 and then we're going to search this again and we want whatever value oop

again and we want whatever value oop goes into that so let's close that

goes into that so let's close that parenthesis and now we're going to do a

parenthesis and now we're going to do a colon and another X

colon and another X lookup and now let's do March so now

lookup and now let's do March so now we're going to search for March we're

we're going to search for March we're going to do our search range where we're

going to do our search range where we're searching for that March and we want the

searching for that March and we want the paper as

paper as well and let's close that and then we

well and let's close that and then we also need to close that parentheses so

also need to close that parentheses so now we are basically adding this

now we are basically adding this February and and this March so it's

February and and this March so it's going to be 310 plus 150 it's adding

going to be 310 plus 150 it's adding those um two values and it should be uh

those um two values and it should be uh what 460 so let's see if that is our

what 460 so let's see if that is our output and it is so you can do this with

output and it is so you can do this with a lot of things not just some but you're

a lot of things not just some but you're able to use x lookup within different

able to use x lookup within different formulas if you're searching for a

formulas if you're searching for a specific value and a specific value um

specific value and a specific value um in in another um cell you can add those

in in another um cell you can add those together using X lookup which is

together using X lookup which is honestly it's pretty great so let's go

honestly it's pretty great so let's go over to V up so I wanted to show you

over to V up so I wanted to show you this because I wanted to show you where

this because I wanted to show you where it came from and what we used to do um

it came from and what we used to do um unless you are continuing to use V

unless you are continuing to use V lookup and what we can do now so X

lookup and what we can do now so X lookup I just showed you kind of

lookup I just showed you kind of everything um but super quickly I'm

everything um but super quickly I'm going to show you how vlookup used to

going to show you how vlookup used to work um in a super short way so that you

work um in a super short way so that you can understand how it used to be used

can understand how it used to be used and how it is used uh how X lookup is

and how it is used uh how X lookup is used now so let's go in here and we're

used now so let's go in here and we're going to say equals and we're going to

going to say equals and we're going to do a vlookup and so we have a lookup

do a vlookup and so we have a lookup value Val and so we're going to click

value Val and so we're going to click this we're going to hit Comma just like

this we're going to hit Comma just like we did before and now we're going to do

we did before and now we're going to do a table array and the table array is a

a table array and the table array is a little different in that you're

little different in that you're searching an entire area so let's do uh

searching an entire area so let's do uh H2 all the way through o oops o10 so

H2 all the way through o oops o10 so that's what that's what our table array

that's what that's what our table array is going to be then we're going to do a

is going to be then we're going to do a comma and now we have to do a column

comma and now we have to do a column index number which number um are we

index number which number um are we going to be um searching for which um

going to be um searching for which um value are we going to be searching for

value are we going to be searching for in here and so we want to search for

in here and so we want to search for eight because this is 1 2 3 4 five 6 7

eight because this is 1 2 3 4 five 6 7 eight we want to return that email and

eight we want to return that email and we're searching for the name right here

we're searching for the name right here in this very first column so we have

in this very first column so we have that comma and we're going to do eight

that comma and we're going to do eight and then in the range lookup you can do

and then in the range lookup you can do true which is an approximate match or

true which is an approximate match or false which is an exact match and we'll

false which is an exact match and we'll do

do false I don't know why it's not Auto

false I don't know why it's not Auto auto doing it but there we go and now we

auto doing it but there we go and now we will do it and it's going to return it

will do it and it's going to return it just as we had it um a lot of people uh

just as we had it um a lot of people uh I guess not everybody but some people

I guess not everybody but some people didn't like and the reason why they

didn't like and the reason why they created X lookup you had to do those

created X lookup you had to do those ranges and if you ever went in here and

ranges and if you ever went in here and then we let's say we um added another

then we let's say we um added another column which happens to data now it

column which happens to data now it gives completely different um different

gives completely different um different data so let's say for whatever reason we

data so let's say for whatever reason we added uh address so now we have these

added uh address so now we have these people address well now it's going to

people address well now it's going to give us a different um value it's going

give us a different um value it's going to have this end dates because if we go

to have this end dates because if we go in here now it doesn't um now the eighth

in here now it doesn't um now the eighth is this end date and the ninth is this

is this end date and the ninth is this email so if you have a vlookup that you

email so if you have a vlookup that you use for um you know a calculation or a

use for um you know a calculation or a table that you've created or different

table that you've created or different things in Excel you then have to go

things in Excel you then have to go through here and manually change this

through here and manually change this and so a lot of people didn't like that

and so a lot of people didn't like that CU if you you know needed to change data

CU if you you know needed to change data or you needed to change something or add

or you needed to change something or add an additional column you'd have to go

an additional column you'd have to go back and fix all of your vlookups they

back and fix all of your vlookups they wouldn't just automatically U Move with

wouldn't just automatically U Move with it which is what happens with xlookup

it which is what happens with xlookup and just to prove this uh let's go back

and just to prove this uh let's go back to the very first one which is the X

to the very first one which is the X lookup and right now the email is

lookup and right now the email is looking at O2 and through o10 um we're

looking at O2 and through o10 um we're just going to insert right here and that

just going to insert right here and that would be our new colum we'll do address

would be our new colum we'll do address oops address and notice that it hasn't

oops address and notice that it hasn't changed and why is that because it auto

changed and why is that because it auto changed for us from P2 to P10

changed for us from P2 to P10 understanding that it wanted to stick

understanding that it wanted to stick with when something was inserted here it

with when something was inserted here it wanted to stick with the original data

wanted to stick with the original data the original array that was selected and

the original array that was selected and so xlup does that work for you and it

so xlup does that work for you and it makes it a little bit easier to automate

makes it a little bit easier to automate things and create these processes in

things and create these processes in Excel without having to go fix it later

Excel without having to go fix it later which you had to do with lookup so that

which you had to do with lookup so that is it for today I hope that you know how

is it for today I hope that you know how to use x lookup a little bit better now

to use x lookup a little bit better now that you have watched this uh if you

that you have watched this uh if you enjoyed this video be sure to like And

enjoyed this video be sure to like And subscribe below and I will see you in

subscribe below and I will see you in the next

the next [Music]

[Music] video what's going on everybody welcome

video what's going on everybody welcome back to another Excel tutorial today

back to another Excel tutorial today we'll be looking at conditional

we'll be looking at conditional formatting

formatting [Music]

[Music] now if you've never heard of conditional

now if you've never heard of conditional form mounting before that's okay I had

form mounting before that's okay I had never heard of it before I became a data

never heard of it before I became a data analyst and so now that I've been using

analyst and so now that I've been using Excel a lot of course I use it quite a

Excel a lot of course I use it quite a bit and so I want to show you how to use

bit and so I want to show you how to use it conditional formatting is basically

it conditional formatting is basically just a way to see patterns and Trends

just a way to see patterns and Trends and data and that's a super simple way

and data and that's a super simple way of putting it um but it's very easy to

of putting it um but it's very easy to use and so hopefully I can show you how

use and so hopefully I can show you how to use it uh really easily in a lot of

to use it uh really easily in a lot of the things that I use the most and some

the things that I use the most and some of the things that I use it for so that

of the things that I use it for so that you can also know how to use conditional

you can also know how to use conditional formatting now before we jump into the

formatting now before we jump into the tutorial I want to give a huge shout out

tutorial I want to give a huge shout out to the sponsor of this Excel series and

to the sponsor of this Excel series and that is udemy you guys know by now that

that is udemy you guys know by now that I absolutely love udemy I've been using

I absolutely love udemy I've been using them for years and I've taken literally

them for years and I've taken literally hundreds of courses on udemy and I've

hundreds of courses on udemy and I've learned so so much especially when I was

learned so so much especially when I was first starting out as a data analyst uh

first starting out as a data analyst uh I learned a lot through their Excel

I learned a lot through their Excel courses on udemy and so I have actually

courses on udemy and so I have actually put the ones that I really like and I

put the ones that I really like and I have taken and enjoyed and think you

have taken and enjoyed and think you would as well in the description so if

would as well in the description so if you want to take those sure to check

you want to take those sure to check those out again huge shout out to UD me

those out again huge shout out to UD me for sponsoring the series now without

for sponsoring the series now without further Ado let's jump onto my screen

further Ado let's jump onto my screen and get started with the tutorial all

and get started with the tutorial all right so let's jump right into it on

right so let's jump right into it on this Home tab right here if we go all

this Home tab right here if we go all the way over to the right there is

the way over to the right there is conditional formatting and the

conditional formatting and the description that it gives us is easily

description that it gives us is easily spot Trends and patterns in your data

spot Trends and patterns in your data using bars colors and icons to visually

using bars colors and icons to visually highlight important values and that is

highlight important values and that is exactly how I would have defined it a

exactly how I would have defined it a really good job Microsoft exactly how I

really good job Microsoft exactly how I would have done it so what you'll see

would have done it so what you'll see right away is there's nothing too

right away is there's nothing too complex so we have some highlight cell

complex so we have some highlight cell rules um we have some top bottom rules

rules um we have some top bottom rules data bars color scales icon sets and

data bars color scales icon sets and then at the bottom we can create a rule

then at the bottom we can create a rule we can clear the rule and we can manage

we can clear the rule and we can manage our rule so if you create a rule then

our rule so if you create a rule then you can manage it so we're going to

you can manage it so we're going to start with these icon sets and I'm going

start with these icon sets and I'm going to show you how to use those and we'll

to show you how to use those and we'll work our way to the top and then I'll

work our way to the top and then I'll show you how to create some rules

show you how to create some rules yourself and how that all works so let's

yourself and how that all works so let's start off with the icon sets I'm going

start off with the icon sets I'm going to go over here to sales um and for this

to go over here to sales um and for this data we kind of have this um you know

data we kind of have this um you know Trend or or pattern that you can kind of

Trend or or pattern that you can kind of see over time so over the months um so

see over time so over the months um so if we go right here and let's use that

if we go right here and let's use that conditional forming let's use that icon

conditional forming let's use that icon sets and right here we can use these

sets and right here we can use these directional so you know we have this

directional so you know we have this kind of Time series each month that

kind of Time series each month that shows us how much paper they're selling

shows us how much paper they're selling and if we do this right here it's going

and if we do this right here it's going to show us if it's kind of average or if

to show us if it's kind of average or if it's below average or if it's above

it's below average or if it's above average or if it's going up so at a

average or if it's going up so at a really quick glance you can kind of see

really quick glance you can kind of see the pattern of this data set it's kind

the pattern of this data set it's kind of going mostly yellow and red there's

of going mostly yellow and red there's only two months where it's going up

only two months where it's going up significantly now we don't have to only

significantly now we don't have to only do that for one row or one column you

do that for one row or one column you can apply to all of them but as you can

can apply to all of them but as you can see all of these are red now why are

see all of these are red now why are they all red it's because they're using

they all red it's because they're using numbers for everything so they're

numbers for everything so they're comparing these 24s these 50s and 65s

comparing these 24s these 50s and 65s against these 450s and 750s and so

against these 450s and 750s and so they're all going to be red but if we do

they're all going to be red but if we do it individually if we do it each row if

it individually if we do it each row if we take it just like this and then we go

we take it just like this and then we go to Icon sets and do it it's going to be

to Icon sets and do it it's going to be much more representative of the actual

much more representative of the actual printers not of all the numbers as a

printers not of all the numbers as a whole and you can do other things uh the

whole and you can do other things uh the arrows are ones that you'll probably see

arrows are ones that you'll probably see the most often that's the one I've used

the most often that's the one I've used if I ever do use them um but you can you

if I ever do use them um but you can you know do ones like this where they have

know do ones like this where they have you know kind of a trend upward or a

you know kind of a trend upward or a trend downward um and so there's just

trend downward um and so there's just several more arrows this one only gives

several more arrows this one only gives you three as you can see this one gives

you three as you can see this one gives you five um and you can do you know

you five um and you can do you know colors or shapes or or different

colors or shapes or or different indicators and all these different

indicators and all these different things um and honestly it's kind of

things um and honestly it's kind of whatever you want to use whatever makes

whatever you want to use whatever makes sense for your data but you know I've

sense for your data but you know I've really only ever seen like these colors

really only ever seen like these colors being used I've never really seen these

being used I've never really seen these flags or anything like that but again it

flags or anything like that but again it just depends on what industry you work

just depends on what industry you work in you might you might see that let's go

in you might you might see that let's go right over here to the demographics um

right over here to the demographics um and let's look at our color scales now

and let's look at our color scales now color scales are going to be the

color scales are going to be the probably the most obvious thing that in

probably the most obvious thing that in datab bars are going to be the most

datab bars are going to be the most obvious things in here um if you go

obvious things in here um if you go right here and and you look at this

right here and and you look at this color scale if it's high if it's among

color scale if it's high if it's among the top ones it's green the lowest it's

the top ones it's green the lowest it's red and you can change that um to really

red and you can change that um to really any colors you want any colors that they

any colors you want any colors that they offer you um and it it does exactly what

offer you um and it it does exactly what it does it's a color scale a gradient of

it does it's a color scale a gradient of the colors from high to low or low to

the colors from high to low or low to high and so any color that you do you'll

high and so any color that you do you'll be able to kind of see um you know

be able to kind of see um you know what's good and what's not good that

what's good and what's not good that really is um color scales in a nutshell

really is um color scales in a nutshell data bars are again super super

data bars are again super super straightforward it's going to be either

straightforward it's going to be either a gradient fill or a solid fill so let's

a gradient fill or a solid fill so let's look at the gradient fill if we do a

look at the gradient fill if we do a blue gradient fill I'll actually let's

blue gradient fill I'll actually let's get rid of our um let's go over here

get rid of our um let's go over here let's go to clear rules from selected

let's go to clear rules from selected cells we haven't looked at that yet but

cells we haven't looked at that yet but that's how you clear it let's go to data

that's how you clear it let's go to data bars and we'll use this blue gradient so

bars and we'll use this blue gradient so with this blue gradient you know this

with this blue gradient you know this one is or sorry this one is the highest

one is or sorry this one is the highest one so it's going to be completely

one so it's going to be completely filled and this one is 36,000 almost

filled and this one is 36,000 almost half of this I'm pretty close and so

half of this I'm pretty close and so it's almost half um this one again you

it's almost half um this one again you know it's not used very often

know it's not used very often I you don't see these a lot to be honest

I you don't see these a lot to be honest you just don't um but if you do see it

you just don't um but if you do see it that's how you use it that's how it can

that's how you use it that's how it can be done again pretty easy uh as I just

be done again pretty easy uh as I just showed a second ago if you want to clear

showed a second ago if you want to clear the rules you can clear from the

the rules you can clear from the selected cells that's what we're doing

selected cells that's what we're doing so I have column G selected and I'm

so I have column G selected and I'm going to I'm going to clear that if you

going to I'm going to clear that if you want to clear the rules for the entire

want to clear the rules for the entire sheet you can do that as well so it

sheet you can do that as well so it would affect every single column and row

would affect every single column and row we'll just do this for now so now let's

we'll just do this for now so now let's go look at the top bottom rules so so

go look at the top bottom rules so so this is the top 10 items top 10% bottom

this is the top 10 items top 10% bottom 10 items bottom 10% above average and

10 items bottom 10% above average and below average and they're going to do

below average and they're going to do exactly what you think they are going to

exactly what you think they are going to do if you select above average it is

do if you select above average it is going to select or highlight the cells

going to select or highlight the cells that are above the average in column G

that are above the average in column G so let's look at the salaries that are

so let's look at the salaries that are above average all right and so uh the

above average all right and so uh the ones that are at the very top are

ones that are at the very top are Michael Scotts Toby flenderson and

Michael Scotts Toby flenderson and Dwight shro uh no shock there um I

Dwight shro uh no shock there um I believe the average is somewhere around

believe the average is somewhere around like

like 48,500 or something so I think this one

48,500 or something so I think this one just is just below it and so all these

just is just below it and so all these other ones are below average and that's

other ones are below average and that's just because you know Michael Scott and

just because you know Michael Scott and Dwight Sho are and Toby are kind of

Dwight Sho are and Toby are kind of bringing up that average quite a bit so

bringing up that average quite a bit so everyone else is going to fall beneath

everyone else is going to fall beneath that so at a super quick glance you're

that so at a super quick glance you're able to just highlight the cells and

able to just highlight the cells and you're able to see who is above average

you're able to see who is above average and you know you can do this in a lot of

and you know you can do this in a lot of different ways in Excel but this is just

different ways in Excel but this is just a really simple fast way to do that um

a really simple fast way to do that um let's get rid of that real quick and

let's get rid of that real quick and let's go back up here and now we can

let's go back up here and now we can oops let's go to top bottom rules and

oops let's go to top bottom rules and now we can see the below average and

now we can see the below average and it's going to highlight all the other

it's going to highlight all the other ones and so it works exactly how you

ones and so it works exactly how you think it is going to work and this is

think it is going to work and this is the default way that it highlights these

the default way that it highlights these cells so it highlights them this kind of

cells so it highlights them this kind of um seeth through red and then it

um seeth through red and then it highlights the actual text or or the um

highlights the actual text or or the um characters in there red as well now I'm

characters in there red as well now I'm not going to go through and show you

not going to go through and show you every single one of these top bottom

every single one of these top bottom rules I think they're pretty

rules I think they're pretty self-explanatory I just kind of wanted

self-explanatory I just kind of wanted to show you what happens when you do use

to show you what happens when you do use one of them it's going to highlight that

one of them it's going to highlight that cell so let's go up here to the

cell so let's go up here to the Highlight cells rules and honestly these

Highlight cells rules and honestly these are the ones that I use by far the most

are the ones that I use by far the most uh all these other ones combined I do

uh all these other ones combined I do not use more than this highlight cells

not use more than this highlight cells rules um and the one in here that I use

rules um and the one in here that I use more than any other conditional

more than any other conditional formatting rule is this duplicate values

formatting rule is this duplicate values so I'll start with that really quick and

so I'll start with that really quick and I'll kind of show you a few few of these

I'll kind of show you a few few of these other ones but this duplicate values to

other ones but this duplicate values to me is one of the most useful ones um and

me is one of the most useful ones um and so let's kind of show you how that works

so let's kind of show you how that works if we go to the start date you can see

if we go to the start date you can see that we have a duplicate value right

that we have a duplicate value right here and if we go over here to

here and if we go over here to conditional formatting highlight cells

conditional formatting highlight cells rules and duplicate values it is going

rules and duplicate values it is going to highlight um the uh duplicate and

to highlight um the uh duplicate and that says duplicate right here now we

that says duplicate right here now we can go through here and click on unique

can go through here and click on unique um and then it would highlight all the

um and then it would highlight all the ones that are not duplicates um so you

ones that are not duplicates um so you can use it you know kind of in a similar

can use it you know kind of in a similar inverse way uh it's just different

inverse way uh it's just different different but I use the duplicate almost

different but I use the duplicate almost always um another thing that you can do

always um another thing that you can do is go over here and you can change the

is go over here and you can change the color um or you can even do a custom um

color um or you can even do a custom um which I never do that it's not um

which I never do that it's not um something I spend a lot of time doing I

something I spend a lot of time doing I typically just stick with this one so

typically just stick with this one so you can do that and it's going to

you can do that and it's going to highlight um you know something that has

highlight um you know something that has a duplicate value in there now why do I

a duplicate value in there now why do I use this so much well I work with a lot

use this so much well I work with a lot of different types of data sets but one

of different types of data sets but one thing that you'll find in almost all of

thing that you'll find in almost all of them is they have some type of ID and

them is they have some type of ID and they're going to have some type of um

they're going to have some type of um personal information whether that's a

personal information whether that's a social security number or an address or

social security number or an address or um you

um you know or a cell phone number or something

know or a cell phone number or something like that there is going to be data that

like that there is going to be data that is going to to identify that person now

is going to to identify that person now I work a lot with pharmaceutical data a

I work a lot with pharmaceutical data a lot with Pharmacy data um as well as

lot with Pharmacy data um as well as Healthcare data so like names Social

Healthcare data so like names Social Security numbers addresses phone numbers

Security numbers addresses phone numbers all those things all that customer or or

all those things all that customer or or client information and oftentimes when I

client information and oftentimes when I get a new data set and I have it in

get a new data set and I have it in Excel or I convert it to excel I will

Excel or I convert it to excel I will start using these duplicates to try to

start using these duplicates to try to find issues with the data and I find

find issues with the data and I find them all the time either there's an

them all the time either there's an employee ID or some type of customer ID

employee ID or some type of customer ID or client ID that has a duplicate in

or client ID that has a duplicate in there that should not be in there or

there that should not be in there or there's multiple Social Security numbers

there's multiple Social Security numbers or there's an issue in some other way

or there's an issue in some other way and I'm able to find those things and

and I'm able to find those things and spot those patterns using this

spot those patterns using this duplicates and I promise you I use this

duplicates and I promise you I use this one almost every single time I open a

one almost every single time I open a new data set or I work with a new

new data set or I work with a new clients working with their data um and

clients working with their data um and so I wanted to show you this one I

so I wanted to show you this one I wanted to really press upon you that

wanted to really press upon you that this one is a really really really good

this one is a really really really good one to know and learn how to use it's

one to know and learn how to use it's not complicated it's not hard it just

not complicated it's not hard it just shows you you know you know

shows you you know you know if there's a duplicate value but I

if there's a duplicate value but I wanted you to know how I use it and how

wanted you to know how I use it and how often I use it so that you can you know

often I use it so that you can you know pick that up and put that in your tool

pick that up and put that in your tool kit in your back pocket so that you can

kit in your back pocket so that you can use that later on if you have uh if you

use that later on if you have uh if you have a similar need or if you're trying

have a similar need or if you're trying to do something similar to what I was

to do something similar to what I was just talking about so that is how

just talking about so that is how duplicates work again super great it's

duplicates work again super great it's obviously not super useful when you're

obviously not super useful when you're only using um 10 rows but when you have

only using um 10 rows but when you have you know 50,000 100,000 and there should

you know 50,000 100,000 and there should be zero duplicates in there and you

be zero duplicates in there and you highlight it and then uh you come right

highlight it and then uh you come right here use the

here use the filter and we're going to filter and

filter and we're going to filter and we're going to sort by the color and it

we're going to sort by the color and it allows you to sort by the color and you

allows you to sort by the color and you have duplicates in there then that's a

have duplicates in there then that's a problem and you identified a problem

problem and you identified a problem super quickly uh and you know some of

super quickly uh and you know some of those things they slip by because nobody

those things they slip by because nobody checks it and so that's something that I

checks it and so that's something that I I often check and if you go here and you

I often check and if you go here and you sort by color and there isn't an option

sort by color and there isn't an option to do um this this pink red color and

to do um this this pink red color and that means there aren't any duplicates

that means there aren't any duplicates and that a really good thing most of the

and that a really good thing most of the time that's a really good thing so let's

time that's a really good thing so let's go ahead and we're going to clear that

go ahead and we're going to clear that as well

as well as get rid of our conditional formatting

as get rid of our conditional formatting rules now another one that I use a lot

rules now another one that I use a lot is this one right here which is the text

is this one right here which is the text that contains honestly this one comes a

that contains honestly this one comes a lot in handy especially when you're

lot in handy especially when you're looking for like a specific keyword in

looking for like a specific keyword in my uh case a lot of times I was using

my uh case a lot of times I was using this when I was going through drug names

this when I was going through drug names I am not a doctor I do not pretend to be

I am not a doctor I do not pretend to be a doctor and so when I was looking for

a doctor and so when I was looking for laraza Pam or something like that um I

laraza Pam or something like that um I would just search for like lorz or

would just search for like lorz or something and and not Lorax but loras

something and and not Lorax but loras you know I I would just search for it

you know I I would just search for it and then all the ones that contain that

and then all the ones that contain that would pop up I can bring them to the top

would pop up I can bring them to the top and I can see them and to me that's

and I can see them and to me that's super super useful and I would do that

super super useful and I would do that all the time and so in this case we're

all the time and so in this case we're looking at emails and let's say we all

looking at emails and let's say we all only wanted to pull all the ones that

only wanted to pull all the ones that are Gmail and so now we can go through

are Gmail and so now we can go through and we can you know click okay and

and we can you know click okay and that's going to pop up or we want all

that's going to pop up or we want all the ones that have Dunder oops Dunder

the ones that have Dunder oops Dunder Mifflin and if we click on that all the

Mifflin and if we click on that all the ones that are Dunder Mifflin come up or

ones that are Dunder Mifflin come up or have done their Mylin in it and again we

have done their Mylin in it and again we can um sort by or we can um and so we

can um sort by or we can um and so we can sort by right here and we can bring

can sort by right here and we can bring all those to the top and so super super

all those to the top and so super super useful um and another use for it that

useful um and another use for it that you may not think of is something like

you may not think of is something like if it's you know there's some incorrect

if it's you know there's some incorrect data in there this happens often with

data in there this happens often with phone numbers addresses um start dates

phone numbers addresses um start dates or or or dates in general date formats

or or or dates in general date formats where you can go in here and you can say

where you can go in here and you can say text that contains and if you know you

text that contains and if you know you put in a oops a dash and it has it in

put in a oops a dash and it has it in there then you know that that is that is

there then you know that that is that is wrong now that is really all I wanted to

wrong now that is really all I wanted to show you in the Highlight cells rules uh

show you in the Highlight cells rules uh the duplicate values and the text

the duplicate values and the text contains are by far the ones that I use

contains are by far the ones that I use the most all the other ones I have used

the most all the other ones I have used um these ones not so much but in these

um these ones not so much but in these highlight cells rules I use you know

highlight cells rules I use you know these two all the time um sometimes I

these two all the time um sometimes I use this between I don't really use

use this between I don't really use these other ones as much although I have

these other ones as much although I have used them and so you got nothing else

used them and so you got nothing else from this video I just wanted you to

from this video I just wanted you to know that these two are super useful and

know that these two are super useful and if you haven't used them before to maybe

if you haven't used them before to maybe try them out and see if you can apply

try them out and see if you can apply them to your own data sets now we've

them to your own data sets now we've looked at all of these preset ones in

looked at all of these preset ones in conditional formatting but you can also

conditional formatting but you can also do a new rule and so if we click on new

do a new rule and so if we click on new rule right here and we go down to use a

rule right here and we go down to use a formula to determine which cells to

formula to determine which cells to format we can add our own formula in

format we can add our own formula in here that will then highlight exactly

here that will then highlight exactly what we want and so if there isn't a

what we want and so if there isn't a preset rule that you like and it doesn't

preset rule that you like and it doesn't have the option that you want you can do

have the option that you want you can do almost any formula that you want in our

almost any formula that you want in our formulas video that we did a few weeks

formulas video that we did a few weeks ago and you can put it in here and then

ago and you can put it in here and then you can format uh what you want the cell

you can format uh what you want the cell to look like if it meets that criteria

to look like if it meets that criteria so let's take this right over here um

so let's take this right over here um and before we start this formula I just

and before we start this formula I just want you to note that you know I have

want you to note that you know I have h11 highlighted that's going to come

h11 highlighted that's going to come into play in just a little bit but I

into play in just a little bit but I want you to be aware that h11 is the

want you to be aware that h11 is the cell that we're highlighted so what

cell that we're highlighted so what we're going to do is we are going to

we're going to do is we are going to create our formula now if you've never

create our formula now if you've never created a formula I highly recommend uh

created a formula I highly recommend uh watching my formulas tutorial because

watching my formulas tutorial because that is going to show you how to do this

that is going to show you how to do this um but we're all we're going to do is

um but we're all we're going to do is we're going to do equals that's how you

we're going to do equals that's how you start the uh how you actually create a

start the uh how you actually create a formula and we're going to give it this

formula and we're going to give it this range right here and so it's going to

range right here and so it's going to take everything from G2 to G10 now these

take everything from G2 to G10 now these dollar signs are super important if you

dollar signs are super important if you don't know how to use them or you don't

don't know how to use them or you don't know what they do um you're going to

know what they do um you're going to mess up this formula a lot uh and so

mess up this formula a lot uh and so what this dollar sign basically does is

what this dollar sign basically does is it's basically hardcoding it in there it

it's basically hardcoding it in there it is only going to look at G2 and is only

is only going to look at G2 and is only going to look at G10 or through G10

going to look at G10 or through G10 because that colon and this can come

because that colon and this can come into play because if you have something

into play because if you have something selected like the h11 it's going to mess

selected like the h11 it's going to mess it up because now if you have h11

it up because now if you have h11 selected like we do you'll see this in a

selected like we do you'll see this in a second it's not going to be applied to

second it's not going to be applied to this um and again I'll show you that in

this um and again I'll show you that in just a minute but we don't want this

just a minute but we don't want this hardcoded in there okay but we do have

hardcoded in there okay but we do have to select the proper range in a second

to select the proper range in a second um so we're going to get rid of this

um so we're going to get rid of this we're going to get rid of the dollar

we're going to get rid of the dollar signs because we want to pretty fluid

signs because we want to pretty fluid and be able to applied to be applied

and be able to applied to be applied basically anywhere we want let's go into

basically anywhere we want let's go into this

this formula um if it meets our criteria

formula um if it meets our criteria let's give it um let's give it a border

let's give it um let's give it a border and we'll give it um we'll give it some

and we'll give it um we'll give it some color we're going to say if this is

color we're going to say if this is greater than

greater than 50,000 so let's hit okay and nothing

50,000 so let's hit okay and nothing happened so let's go back and see why so

happened so let's go back and see why so if we go to our manage rules you can see

if we go to our manage rules you can see that so as the G2 to G G10 is greater

that so as the G2 to G G10 is greater than 50,000 but it only is being applied

than 50,000 but it only is being applied to this h11 cell which really makes no

to this h11 cell which really makes no sense um so if we had wanted to get it

sense um so if we had wanted to get it done the first time we needed to have

done the first time we needed to have basically selected that G2 to G10 right

basically selected that G2 to G10 right away um but we can do that now so let's

away um but we can do that now so let's get rid of this and we're going to say

get rid of this and we're going to say G2 to

G2 to G10 and that is hardcoded in there

G10 and that is hardcoded in there that's should be fine still um but let's

that's should be fine still um but let's see what it

see what it does and so now every every single thing

does and so now every every single thing is highlighted and why is that uh that's

is highlighted and why is that uh that's because when we changed it it also

because when we changed it it also changed the format of it because we

changed the format of it because we changed the cell that we were looking at

changed the cell that we were looking at so we need to come back here and that's

so we need to come back here and that's why again you want to do this the right

why again you want to do this the right way the first time we're going to come

way the first time we're going to come back here we're going to give it this

back here we're going to give it this range and we're going to get rid of

range and we're going to get rid of these dollar

these dollar [Music]

[Music] signs and now we're going to hit okay

signs and now we're going to hit okay and so now it's being applied G2 to G10

and so now it's being applied G2 to G10 and G2 to G10 and we'll keep it like

and G2 to G10 and we'll keep it like that and we'll apply it and now it works

that and we'll apply it and now it works properly so now everything that's above

properly so now everything that's above 50,000 is being highlighted again if

50,000 is being highlighted again if that was confusing um it it is confusing

that was confusing um it it is confusing it genuinely is and so if you wanted to

it genuinely is and so if you wanted to do this right the first time without

do this right the first time without having to make a bunch of changes you'd

having to make a bunch of changes you'd want to highlight these before you start

want to highlight these before you start and then you want to go in and create

and then you want to go in and create the rule we'll do this really quick just

the rule we'll do this really quick just to kind of show you what I'm talking

to kind of show you what I'm talking about we'll say equals we'll give it

about we'll say equals we'll give it this range

this range get rid of these real quick because

get rid of these real quick because again I don't want this hardcoded in

again I don't want this hardcoded in there it will ruin our formula and then

there it will ruin our formula and then we'll say greater than 30 um and we'll

we'll say greater than 30 um and we'll give this nice green uh and so now if

give this nice green uh and so now if they're over the age of 30 it will be

they're over the age of 30 it will be highlighted and we didn't have to go

highlighted and we didn't have to go back and change anything we didn't have

back and change anything we didn't have to go back and fix anything like we did

to go back and fix anything like we did in the first one um that was all for

in the first one um that was all for demonstration purposes but again you

demonstration purposes but again you need to really be aware of that that is

need to really be aware of that that is something that I think think almost

something that I think think almost everybody's going to mess up at some

everybody's going to mess up at some point if you don't already know about it

point if you don't already know about it then you definitely are going to make

then you definitely are going to make that mistake now if we come over here in

that mistake now if we come over here in this area uh we go to our manage rules

this area uh we go to our manage rules and not just the current selection but

and not just the current selection but this whole worksheet then you can see

this whole worksheet then you can see that we have these two formulas now you

that we have these two formulas now you can go in and edit any of these by

can go in and edit any of these by double clicking or clicking on it and

double clicking or clicking on it and then hitting edit rule you can also

then hitting edit rule you can also delete these rules or duplicate these

delete these rules or duplicate these rules um I just wanted to show you what

rules um I just wanted to show you what you are able to do with them but if we

you are able to do with them but if we uh go ahead and we get rid of this um so

uh go ahead and we get rid of this um so let's say we delete that rule and we hit

let's say we delete that rule and we hit apply uh you know the rule is going to

apply uh you know the rule is going to go away that's that I mean it's as

go away that's that I mean it's as simple as that so that is how you can

simple as that so that is how you can create your own rule I want to be again

create your own rule I want to be again very specific in the fact that that is a

very specific in the fact that that is a confusing piece and if you mess that up

confusing piece and if you mess that up you're going to be you know fixing a

you're going to be you know fixing a bunch of different stuff and not

bunch of different stuff and not understanding why your rule is not

understanding why your rule is not working properly it's just because it's

working properly it's just because it's confusing those dollar signs are are

confusing those dollar signs are are really important to watch out for and

really important to watch out for and that is all all there is to it with

that is all all there is to it with conditional formatting again conditional

conditional formatting again conditional formatting is um you know it's not

formatting is um you know it's not anything super confusing we've looked at

anything super confusing we've looked at more complicated things but it's a

more complicated things but it's a really really useful tool to use to look

really really useful tool to use to look at these patterns and Trends super

at these patterns and Trends super quickly and to find um these outliers or

quickly and to find um these outliers or these specific values that you're

these specific values that you're looking for very quickly and if you're

looking for very quickly and if you're looking at just thousands and tens of

looking at just thousands and tens of thousands or hundreds of thousands of

thousands or hundreds of thousands of rows this is one of the fastest ways to

rows this is one of the fastest ways to find these things without having to kind

find these things without having to kind of wait and filter and use these um

of wait and filter and use these um these these filters right here because

these these filters right here because again this can just take forever um and

again this can just take forever um and so if you haven't or if you've never

so if you haven't or if you've never worked with a ton of data and tried to

worked with a ton of data and tried to use this before it can take honestly

use this before it can take honestly like 10 minutes for something simple

like 10 minutes for something simple that you could do with conditional

that you could do with conditional formatting in like 10 seconds so

formatting in like 10 seconds so definitely something to mess with and

definitely something to mess with and use when you are working with your own

use when you are working with your own data sets uh I hope this was helpful I

data sets uh I hope this was helpful I mean honestly I use this all the time so

mean honestly I use this all the time so you know I hope that somebody out there

you know I hope that somebody out there can can use this uh for their own work

can can use this uh for their own work that they're currently using thank you

that they're currently using thank you guys so much for watching I really

guys so much for watching I really appreciate it again huge shout out to

appreciate it again huge shout out to you me for sponsoring this Excel series

you me for sponsoring this Excel series if you like this video be sure to like

if you like this video be sure to like And subscribe below I'll see you in the

And subscribe below I'll see you in the next

next [Music]

[Music] video what's going on everybody welcome

video what's going on everybody welcome back to another Excel tutorial today we

back to another Excel tutorial today we will be looking at

will be looking at [Music]

[Music] charts

now if you have data in Excel and you want to visually show that with bars or

want to visually show that with bars or graphs or anything like that you can do

graphs or anything like that you can do that really simply and I'm going to show

that really simply and I'm going to show you how to do that today and a lot of

you how to do that today and a lot of people are a little bit intimidated

people are a little bit intimidated because they think it's a little bit

because they think it's a little bit complicated but I promise you by the end

complicated but I promise you by the end of this video you will know how to do it

of this video you will know how to do it like a pro it's not that difficult it's

like a pro it's not that difficult it's just you need to know where to look

just you need to know where to look where to click and how to actually

where to click and how to actually filter through things to make sure that

filter through things to make sure that you're visually showing the things that

you're visually showing the things that you want to show but before we actually

you want to show but before we actually jump into the the tutorial I want to

jump into the the tutorial I want to give a huge shout out to the sponsor of

give a huge shout out to the sponsor of this Excel series and that is udem me

this Excel series and that is udem me you may not know this but I probably get

you may not know this but I probably get at least 15 to 50 companies every single

at least 15 to 50 companies every single month reaching out to me wanting to

month reaching out to me wanting to sponsor the channel and promote their

sponsor the channel and promote their product and I turn down almost every

product and I turn down almost every single one because I either don't know

single one because I either don't know their product or I don't believe in

their product or I don't believe in their product and so I'm not going to

their product and so I'm not going to you know go and promote that on my

you know go and promote that on my channel but unud me is one that I have

channel but unud me is one that I have consistently promoted over the past year

consistently promoted over the past year and that's because I truly believe in

and that's because I truly believe in their product I've been taking courses

their product I've been taking courses off their platform for years and I've

off their platform for years and I've honestly learned so much and I cannot

honestly learned so much and I cannot recommend them enough so if you want to

recommend them enough so if you want to take a full-fledged Excel course I have

take a full-fledged Excel course I have my recommendations in the description if

my recommendations in the description if you want to check those out thank you

you want to check those out thank you again to UD me for sponsoring this Excel

again to UD me for sponsoring this Excel Series so without further Ado let's jump

Series so without further Ado let's jump onto my screen and get started with the

onto my screen and get started with the tutorial all right so let's jump right

tutorial all right so let's jump right into it right here we have the Dunder

into it right here we have the Dunder Mifflin sales report and over here we

Mifflin sales report and over here we have all the products that they were

have all the products that they were selling along with the months that they

selling along with the months that they were sold in and so in January they sold

were sold in and so in January they sold 450 reams of paper down here we have the

450 reams of paper down here we have the total it items per month and so in

total it items per month and so in January they sold 898 units of uh

January they sold 898 units of uh products or or things that they sold at

products or or things that they sold at the very end we have the year end total

the very end we have the year end total so this is the total amount of paper

so this is the total amount of paper that they sold throughout the year now

that they sold throughout the year now we're going to use this data right here

we're going to use this data right here for all of our charts now you may not

for all of our charts now you may not have data exactly like this it can come

have data exactly like this it can come in lots of different flavors but you're

in lots of different flavors but you're going to get the basic gist of how to

going to get the basic gist of how to use charts how to edit it how to

use charts how to edit it how to customize it to fit what you need and

customize it to fit what you need and then we're going to kind of put it right

then we're going to kind of put it right over here and kind of create its own

over here and kind of create its own sheet where we can kind of visualize all

sheet where we can kind of visualize all the things that we want to

the things that we want to show so let's jump right back over here

show so let's jump right back over here into sales and first thing we need to do

into sales and first thing we need to do is kind of highlight the data that we're

is kind of highlight the data that we're going to be working with now I'm going

going to be working with now I'm going to start with everything but um you know

to start with everything but um you know I'll show you along the way we don't

I'll show you along the way we don't actually want everything but we can

actually want everything but we can filter that stuff out as we go so let's

filter that stuff out as we go so let's go right here and we're going to insert

go right here and we're going to insert and we're going to go over to charts now

and we're going to go over to charts now this is the chart section there's lots

this is the chart section there's lots of different types of charts um but the

of different types of charts um but the first thing that we're going to be

first thing that we're going to be looking at is right here this is a 2d

looking at is right here this is a 2d column or kind of like a bar chart and

column or kind of like a bar chart and we're just going to click right here and

we're just going to click right here and we're going to pull this

we're going to pull this down so now that we have this down here

down so now that we have this down here there are a few things that I want to

there are a few things that I want to show you before we actually really get

show you before we actually really get into it I kind of want to show you the

into it I kind of want to show you the options that you have so if you go up

options that you have so if you go up here we have different uh chart Styles

here we have different uh chart Styles and so if I hover over them you can see

and so if I hover over them you can see that each one kind of looks a little bit

that each one kind of looks a little bit different and it really doesn't matter

different and it really doesn't matter uh it doesn't really change the data in

uh it doesn't really change the data in any way just how you visualize it and so

any way just how you visualize it and so if that is important if that is

if that is important if that is something that you um you want to stick

something that you um you want to stick with a certain theme or a certain look

with a certain theme or a certain look then go for that uh the other thing

then go for that uh the other thing that's really nice to have over here is

that's really nice to have over here is this switch row and column so right down

this switch row and column so right down here you can see this purple and you can

here you can see this purple and you can see this red those are our rows and

see this red those are our rows and columns and we can switch that right

columns and we can switch that right here so if we go like this now instead

here so if we go like this now instead of the months being right here the

of the months being right here the months are the colors and the actual

months are the colors and the actual product is right here let's click it

product is right here let's click it again and it'll go back and so now we

again and it'll go back and so now we have this kind of Time series now we

have this kind of Time series now we have January through the end of your

have January through the end of your total now this one is one that I think

total now this one is one that I think is super helpful you know it you can do

is super helpful you know it you can do it down here as well if you go to this

it down here as well if you go to this filter um but both of these are super

filter um but both of these are super helpful because you sometimes just want

helpful because you sometimes just want to select all the data and then kind of

to select all the data and then kind of get in there and mess with with it

get in there and mess with with it something that we want to get rid of is

something that we want to get rid of is this total items per month so we want to

this total items per month so we want to remove that and then we also want to

remove that and then we also want to remove this year-end total because both

remove this year-end total because both of those are are kind of the end result

of those are are kind of the end result they're not the actual data per month or

they're not the actual data per month or or per product so we're going to get rid

or per product so we're going to get rid of those and we're going to apply that

of those and we're going to apply that and as you can see just right off the

and as you can see just right off the bat our data is changed dramatically uh

bat our data is changed dramatically uh and that's because we aren't including

and that's because we aren't including these these large large numbers that

these these large large numbers that were kind of throwing off uh the

were kind of throwing off uh the visualization for us so this one right

visualization for us so this one right here as is is already pretty good um

here as is is already pretty good um what we can do right here is we can

what we can do right here is we can change this and we're just going to

change this and we're just going to say products

say products sold per

sold per month now what we can do if we want to

month now what we can do if we want to move it to another um to another sheet

move it to another um to another sheet is we can actually move the chart and we

is we can actually move the chart and we can select where we want to move it we

can select where we want to move it we can move it to chart sheet and we can do

can move it to chart sheet and we can do that or something that I do um almost

that or something that I do um almost 99% of the time I just copy and I come

99% of the time I just copy and I come over here and I'm going to paste it and

over here and I'm going to paste it and so now we have this um this chart right

so now we have this um this chart right over here as well as back here and so I

over here as well as back here and so I typically tend to do that because now we

typically tend to do that because now we can still go over here and change this

can still go over here and change this one as much as we want so if we want to

one as much as we want so if we want to go in here we can alter this one and it

go in here we can alter this one and it won't affect the other one so we just

won't affect the other one so we just have basically two copies so we're going

have basically two copies so we're going to keep this one right here this is

to keep this one right here this is going to be our first

going to be our first visualization um and as I said said it's

visualization um and as I said said it's it's fairly straightforward if you've

it's fairly straightforward if you've ever done any types of charts or graphs

ever done any types of charts or graphs before um right here it's January

before um right here it's January February March April May and if you

February March April May and if you hover over these you can see that that's

hover over these you can see that that's the the paper and if we just glance you

the the paper and if we just glance you know the paper is their biggest product

know the paper is their biggest product by far and so that blue um which is

by far and so that blue um which is their paper is going to be the biggest

their paper is going to be the biggest every single month so that makes perfect

every single month so that makes perfect sense now what if we want to change up

sense now what if we want to change up uh the the kind so what if we want to

uh the the kind so what if we want to change up the kind of visualization that

change up the kind of visualization that it offers us well we have a lot of

it offers us well we have a lot of different options let's go right over

different options let's go right over here to change chart type now this is

here to change chart type now this is going to offer you just about everything

going to offer you just about everything you could possibly imagine or want and

you could possibly imagine or want and even things that you absolutely would

even things that you absolutely would never ever want ever um and so I'm going

never ever want ever um and so I'm going to show you some of the good ones and

to show you some of the good ones and I'm going to show you some just

I'm going to show you some just absolutely insane ones that uh Excel

absolutely insane ones that uh Excel came up with which cannot I could not

came up with which cannot I could not imagine a scenario that these are ever

imagine a scenario that these are ever used um but Within These columns you can

used um but Within These columns you can do they're called cluster columns uh

do they're called cluster columns uh these stacked columns so would look just

these stacked columns so would look just like this those are often used as

like this those are often used as well um and then we have ones that

well um and then we have ones that they're just not used often let's look

they're just not used often let's look let's take a look at this one right

let's take a look at this one right here I mean it's tough it's tough to

here I mean it's tough it's tough to look at um but let's let's put it right

look at um but let's let's put it right here this is basically the same thing

here this is basically the same thing that we just had except visualized in a

that we just had except visualized in a different um we'll call it more unique

different um we'll call it more unique way

way uh and let's for the sake of it let's

uh and let's for the sake of it let's put it over here um these two things

put it over here um these two things show the same information they show the

show the same information they show the same data just one is shown well and one

same data just one is shown well and one is not shown well um I'm not a fan of

is not shown well um I'm not a fan of these 3D type of

these 3D type of visualizations I I just don't like them

visualizations I I just don't like them but maybe you do and and you want to use

but maybe you do and and you want to use that that's fantastic let's go back um

that that's fantastic let's go back um something else that you'll probably use

something else that you'll probably use a lot are things like these um these

a lot are things like these um these line graphs okay so these are line

line graphs okay so these are line graphs and they're different types so

graphs and they're different types so they're these stacked um 100% stacked

they're these stacked um 100% stacked line lines with markers different

line lines with markers different flavors for this this type of line graph

flavors for this this type of line graph and so you can go in here and take a

and so you can go in here and take a look again um not my favorite but they

look again um not my favorite but they have it as an option if you CH so choose

have it as an option if you CH so choose to do this um but I kind of I'm kind of

to do this um but I kind of I'm kind of a simple guy um but I'm going to go in

a simple guy um but I'm going to go in here and it's pretty cluster

here and it's pretty cluster um I want to kind of take the ones that

um I want to kind of take the ones that have the highest

have the highest sales or the highest total amount sold

sales or the highest total amount sold so that would be paper manila folders

so that would be paper manila folders and three ring binders so let's go in

and three ring binders so let's go in here we want to keep paper we want to

here we want to keep paper we want to keep uh manila

keep uh manila folders and we want to keep three ring

folders and we want to keep three ring binders and let's apply that and so now

binders and let's apply that and so now it's a lot cleaner and we're just going

it's a lot cleaner and we're just going to copy this and we're going to put it

to copy this and we're going to put it over here and I'm just putting these all

over here and I'm just putting these all over here for you U because we'll look

over here for you U because we'll look at this at the end and just kind of see

at this at the end and just kind of see different options and and ways to do

different options and and ways to do things as we have gone through this

things as we have gone through this tutorial so let's go back here now

tutorial so let's go back here now something else that we haven't looked at

something else that we haven't looked at is the actual colors and color schemes

is the actual colors and color schemes that you can do so let's go right here

that you can do so let's go right here to these chart Styles and we can go to

to these chart Styles and we can go to color now color is um something that

color now color is um something that probably is quite overlooked um in

probably is quite overlooked um in actual charts and graphs some terrible

actual charts and graphs some terrible colors like this or or this um where

colors like this or or this um where they're really close together especially

they're really close together especially when you have a lot of them um for

when you have a lot of them um for example let's just pretend we put all of

example let's just pretend we put all of them back really quickly it is near

them back really quickly it is near impossible to distinguish these colors

impossible to distinguish these colors um we

um we wouldn't we wouldn't want that let's go

wouldn't we wouldn't want that let's go back to this color you know when you

back to this color you know when you have it like uh in some of these colors

have it like uh in some of these colors at least it at least distinguishes them

at least it at least distinguishes them so you can kind of see what you're

so you can kind of see what you're working with with um but when you have

working with with um but when you have it in these monochromatic options

it in these monochromatic options sometimes they're just impossible to

sometimes they're just impossible to distinguish so be sure to choose the

distinguish so be sure to choose the right colors that you're using so that

right colors that you're using so that if somebody who's never seen this data

if somebody who's never seen this data before looks at it they can easily

before looks at it they can easily distinguish uh the product and the month

distinguish uh the product and the month that you are looking at but let's go

that you are looking at but let's go just back up here we'll choose this

just back up here we'll choose this default option um well let's choose this

default option um well let's choose this one right here this one's nice although

one right here this one's nice although there's lots of yellows and oranges

there's lots of yellows and oranges let's see this one this one's not bad

let's see this one this one's not bad greens blues uh and like yellows so

greens blues uh and like yellows so that's nice um other things that we want

that's nice um other things that we want to look at and there are these chart

to look at and there are these chart elements right here other things that we

elements right here other things that we can add are things like data labels um

can add are things like data labels um and right here it's super messy um but

and right here it's super messy um but if we went back and we got rid of some

if we went back and we got rid of some of these things like the printer Staples

of these things like the printer Staples highlighters pens and total we apply

highlighters pens and total we apply that it's a little bit easier to

that it's a little bit easier to distinguish um

distinguish um and that's you know something that you

and that's you know something that you may be interested in doing you can also

may be interested in doing you can also add this data table at the bottom which

add this data table at the bottom which is the actual columns and rows that you

is the actual columns and rows that you have for this visualization right here

have for this visualization right here now let's expand this quite a bit I'm

now let's expand this quite a bit I'm going to make this extremely large if

going to make this extremely large if you have something like this it actually

you have something like this it actually can be pretty nice um you know maybe we

can be pretty nice um you know maybe we get rid of these data labels but it can

get rid of these data labels but it can be easy because you're putting it all in

be easy because you're putting it all in one place you can also make this two

one place you can also make this two separate visualizations so you can have

separate visualizations so you can have one visualization just like this and

one visualization just like this and right underneath it you can have the

right underneath it you can have the actual rows and columns but this option

actual rows and columns but this option allows you to put it all in one so let's

allows you to put it all in one so let's put this back down because that is way

put this back down because that is way too big and uh wait let's expand it a

too big and uh wait let's expand it a little bit now if you notice right here

little bit now if you notice right here we have our Legend up top um it is

we have our Legend up top um it is possible to actually change that you can

possible to actually change that you can go right here and you can move this um

go right here and you can move this um kind of wherever you want um but it's

kind of wherever you want um but it's not exactly easy to put based off how we

not exactly easy to put based off how we have it right here if we go into to this

have it right here if we go into to this chart elements we go down to Legend and

chart elements we go down to Legend and we hit this little arrow right here we

we hit this little arrow right here we can select it on the right the top the

can select it on the right the top the left and the bottom or we can just go to

left and the bottom or we can just go to more options uh which allows us to push

more options uh which allows us to push it anywhere but um let's say I want to

it anywhere but um let's say I want to do it just like this I'm going to put on

do it just like this I'm going to put on the right and I actually want to bring

the right and I actually want to bring it down right here and you know that's

it down right here and you know that's just an option if you want to kind of

just an option if you want to kind of customize it a little further makes a

customize it a little further makes a little cleaner uh you can do that with

little cleaner uh you can do that with almost any of these things so if you

almost any of these things so if you click on this oops if you click on this

click on this oops if you click on this you can move this anywhere as well so if

you can move this anywhere as well so if you want to move this over here on top

you want to move this over here on top of it you can and make it look terrible

of it you can and make it look terrible or you can move it uh right back over

or you can move it uh right back over here you know this is something that you

here you know this is something that you can move around uh you just kind of want

can move around uh you just kind of want to make sure you're doing it the right

to make sure you're doing it the right way so let's get this back where was

way so let's get this back where was there we go now before we go any further

there we go now before we go any further let's copy that and put it right over

let's copy that and put it right over here with our other uh charts and graphs

here with our other uh charts and graphs and if you see over here on this side we

and if you see over here on this side we have this this format chart area notice

have this this format chart area notice I haven't showed you this at all yet

I haven't showed you this at all yet that is because I genuinely just don't

that is because I genuinely just don't use this almost at all um there are some

use this almost at all um there are some good stuff in here um and I'm sure that

good stuff in here um and I'm sure that you know if you were someone who really

you know if you were someone who really wants to go in there and super customize

wants to go in there and super customize it you can do that um but I honestly I

it you can do that um but I honestly I just never get in here and I never you

just never get in here and I never you know change the glow or the Shadows um

know change the glow or the Shadows um just not something I use and some of

just not something I use and some of these are only for these three 3D

these are only for these three 3D formatting which I never use and so I'm

formatting which I never use and so I'm not going to show you and walk through

not going to show you and walk through these things again I I really don't use

these things again I I really don't use it and so if you want to go in there and

it and so if you want to go in there and mess with it uh you know by all means go

mess with it uh you know by all means go for it it's just not something that I

for it it's just not something that I want to take the time to show you and

want to take the time to show you and with that being said let's go back over

with that being said let's go back over to this chart sheet that we have and it

to this chart sheet that we have and it was super super easy to get these um

was super super easy to get these um charts and graphs and and and whatnot

charts and graphs and and and whatnot there are lots of different options

there are lots of different options again if we go back here and we go up

again if we go back here and we go up here to chart design and go to the

here to chart design and go to the change chart type and again there are a

change chart type and again there are a ton of different options like a pie

ton of different options like a pie chart um like this it's it's you know

chart um like this it's it's you know you can try to figure this out and use

you can try to figure this out and use these um but you know I wanted to show

these um but you know I wanted to show you the ones that you'll probably use

you the ones that you'll probably use the most which are these columns and

the most which are these columns and line charts and they all kind of are

line charts and they all kind of are similar in their own way this bar chart

similar in their own way this bar chart is basically you know this column chart

is basically you know this column chart just on its side and so they all have

just on its side and so they all have their different flavor they all have

their different flavor they all have their different way of visualizing the

their different way of visualizing the data but but in essence they're using

data but but in essence they're using the data in a similar way to to

the data in a similar way to to visualize it and represent the data

visualize it and represent the data itself especially things like these box

itself especially things like these box and whisker plots or these waterfall

and whisker plots or these waterfall charts uh you know these are things that

charts uh you know these are things that usually require specific data to kind of

usually require specific data to kind of use uh and and so I'm just using data

use uh and and so I'm just using data that you'll probably see the most of um

that you'll probably see the most of um like this this sales data so I hope that

like this this sales data so I hope that this given you a pretty good um you know

this given you a pretty good um you know quick understanding of how to use these

quick understanding of how to use these how to customize them how to copy and

how to customize them how to copy and paste them over to to a different sheet

paste them over to to a different sheet to create some type of little uh chart

to create some type of little uh chart and visualization sheet that you can use

and visualization sheet that you can use to show your employers and and visualize

to show your employers and and visualize the data that you are working with thank

the data that you are working with thank you guys so much for watching I really

you guys so much for watching I really appreciate it again huge shout out to

appreciate it again huge shout out to you to me for sponsoring this Excel

you to me for sponsoring this Excel series if you like this video be sure to

series if you like this video be sure to like And subscribe below and I'll see

like And subscribe below and I'll see you in the next

you in the next [Music]

what's going on everybody welcome back to the Excel tutorial Series today we'll

to the Excel tutorial Series today we'll be looking at how to clean data in

[Music] Excel now knowing how to clean data in

Excel now knowing how to clean data in Excel is actually extremely useful and

Excel is actually extremely useful and there are a ton of techniques to do this

there are a ton of techniques to do this I'm going to be showing you the ones

I'm going to be showing you the ones that I probably use the most I feel like

that I probably use the most I feel like are the most helpful to kind of do the

are the most helpful to kind of do the bulk or the majority of the data

bulk or the majority of the data cleaning that you're going to do in

cleaning that you're going to do in Excel like I said there's so many

Excel like I said there's so many different ways and very specific things

different ways and very specific things that you can do but I'm going to

that you can do but I'm going to highlight some of the bigger ones that I

highlight some of the bigger ones that I find the most useful and some of you may

find the most useful and some of you may be thinking well I'll just do my data

be thinking well I'll just do my data cleaning in SQL or python or when I get

cleaning in SQL or python or when I get it ready to put it in Tableau um but

it ready to put it in Tableau um but honestly a lot of the data cleaning at

honestly a lot of the data cleaning at least a lot of the big stuff I tend to

least a lot of the big stuff I tend to do in Excel IF the data set is small

do in Excel IF the data set is small enough to fit in Excel and so I think

enough to fit in Excel and so I think it's actually really really useful to

it's actually really really useful to know how to do this because you'll most

know how to do this because you'll most likely be doing it more than you think

likely be doing it more than you think now before we jump into the tutorial I

now before we jump into the tutorial I want to give a shout out to the sponsor

want to give a shout out to the sponsor of this video and is brand new sponsor

of this video and is brand new sponsor it is unlocked by Z by HP unlocked is a

it is unlocked by Z by HP unlocked is a movie that's actually broken up into

movie that's actually broken up into four parts and each of them have a

four parts and each of them have a unique data science challenge associated

unique data science challenge associated with it now I'm going to read this next

with it now I'm going to read this next part because it's extremely interesting

part because it's extremely interesting each challenge represents a different

each challenge represents a different topic so there's data visualization text

topic so there's data visualization text analysis audio signal processing and

analysis audio signal processing and computer vision and you can submit your

computer vision and you can submit your answers in your work on their website

answers in your work on their website for a chance to win one of 10 zbook

for a chance to win one of 10 zbook Studio laptops or a free trip to the

Studio laptops or a free trip to the kaggle World Championships so I'll leave

kaggle World Championships so I'll leave a link in the description where you can

a link in the description where you can go watch the movie and then do the

go watch the movie and then do the challenges and then submit your answers

challenges and then submit your answers for a chance to win you should also go

for a chance to win you should also go check out their hackathon where you can

check out their hackathon where you can do these projects with other people just

do these projects with other people just like you who are trying to figure out

like you who are trying to figure out these answers and submit them to win as

these answers and submit them to win as well so go check that out thank you

well so go check that out thank you again to the sponsor of this video

again to the sponsor of this video unlocked by Z by HP now without further

unlocked by Z by HP now without further Ado let's jump onto my screen and get

Ado let's jump onto my screen and get started with the tutorial all right so

started with the tutorial all right so let's jump right into it I have this US

let's jump right into it I have this US president data set I got the base data

president data set I got the base data set from kaggle uh but I added some of

set from kaggle uh but I added some of my own data and then I messed some stuff

my own data and then I messed some stuff up as well just to kind of um

up as well just to kind of um demonstrate some of these things that

demonstrate some of these things that we're going to be looking at today this

we're going to be looking at today this is not a full project so you know we're

is not a full project so you know we're actually going to be using this to

actually going to be using this to create any visualizations or anything

create any visualizations or anything like that so you know all this is just

like that so you know all this is just for demonstration purposes but we will

for demonstration purposes but we will be doing a full project in about two or

be doing a full project in about two or three videos uh in this Excel Series

three videos uh in this Excel Series where we're going to be doing from start

where we're going to be doing from start to finish with a real data set so you

to finish with a real data set so you know if that's something that you're you

know if that's something that you're you wanting then we will absolutely be doing

wanting then we will absolutely be doing that now something that you may be

that now something that you may be wondering is how do you actually

wondering is how do you actually identify what you need need to clean in

identify what you need need to clean in the data what do you know to look for

the data what do you know to look for well some of the obvious things are

well some of the obvious things are things like formatting and

things like formatting and standardization so things like you know

standardization so things like you know this James Monroe is in all caps that

this James Monroe is in all caps that happens all the time within real data um

happens all the time within real data um and and so you know you want to

and and so you know you want to standardize that or this all lowercase

standardize that or this all lowercase you want to standardize that you want

you want to standardize that you want that all to be the same there's also

that all to be the same there's also things like um right here or we have

things like um right here or we have this wig and this wig with a bunch of

this wig and this wig with a bunch of random stuff after it this happens all

random stuff after it this happens all the time where it's not completely

the time where it's not completely standardized um and you may even notice

standardized um and you may even notice um you know there are some spelling

um you know there are some spelling errors in here and I'll we'll kind of

errors in here and I'll we'll kind of look through that in a little bit and

look through that in a little bit and then you know there are things like

then you know there are things like additional spaces where there shouldn't

additional spaces where there shouldn't be spaces there are things like

be spaces there are things like currencies that you need to be aware of

currencies that you need to be aware of if you were importing this into or going

if you were importing this into or going to be importing this into a SQL database

to be importing this into a SQL database um things like currencies can be just a

um things like currencies can be just a problem or be really um unnecessary it

problem or be really um unnecessary it may actually cause more issues in the

may actually cause more issues in the long run so you may just want to you

long run so you may just want to you know take that to the base value and

know take that to the base value and then dates are always an issue always

then dates are always an issue always always always um so always look at your

always always um so always look at your dates make sure they're they're

dates make sure they're they're formatted correctly make sure they're

formatted correctly make sure they're all the same these are the types of

all the same these are the types of things that right when I glance at this

things that right when I glance at this data set these are things that I'm

data set these are things that I'm looking for um one other thing that is

looking for um one other thing that is actually the first thing that we're

actually the first thing that we're going to start out with is you want to

going to start out with is you want to make sure that your data is not

make sure that your data is not duplicated because if your data has

duplicated because if your data has duplicate data in it and you don't want

duplicate data in it and you don't want that it's not supposed to be there there

that it's not supposed to be there there are some specific use cases where

are some specific use cases where duplicated data is okay um you know you

duplicated data is okay um you know you want to get rid of that and it's very

want to get rid of that and it's very easy to do in Excel uh the first thing

easy to do in Excel uh the first thing we're going to do we're going to go uh

we're going to do we're going to go uh to this data tab we're going to go right

to this data tab we're going to go right over here and we're going to get see if

over here and we're going to get see if there's any uh duplicates in our data so

there's any uh duplicates in our data so we're just going to go up to remove

we're just going to go up to remove duplicates it's going to automatically

duplicates it's going to automatically choose all of your columns to to check

choose all of your columns to to check against so it's going to for from a all

against so it's going to for from a all the way through I it's going to see is

the way through I it's going to see is the exact same data in all these rows

the exact same data in all these rows and if it is it's going to get rid of it

and if it is it's going to get rid of it um and so we're going to click okay and

um and so we're going to click okay and it did find one duplicate and I'll show

it did find one duplicate and I'll show you that one real quick um because you

you that one real quick um because you know it was right here so Barack Obama

know it was right here so Barack Obama was here twice and then I'm going to hit

was here twice and then I'm going to hit control I hit control Z to go back I'm

control I hit control Z to go back I'm going hit control y to go forward and it

going hit control y to go forward and it removed that uh that row completely now

removed that uh that row completely now in this example you may be able to spot

in this example you may be able to spot that with your eye but in a real data

that with your eye but in a real data set where you have 10,000 100,000 rows

set where you have 10,000 100,000 rows there's absolutely no way you're going

there's absolutely no way you're going to see that or very very unlikely that

to see that or very very unlikely that you are going to see that there's

you are going to see that there's duplicated data in there so just running

duplicated data in there so just running a a a quick um dup or or removing of

a a a quick um dup or or removing of duplicates that is really important to

duplicates that is really important to make sure that you um have gotten rid of

make sure that you um have gotten rid of those things so that's one of the first

those things so that's one of the first things that I do um we're going to go

things that I do um we're going to go into a lot of these different uh columns

into a lot of these different uh columns and I'm going to kind of show you

and I'm going to kind of show you different techniques or things that I do

different techniques or things that I do when I look at actual data so I'm going

when I look at actual data so I'm going to come right over here I'm going to

to come right over here I'm going to insert and this is what I actually do I

insert and this is what I actually do I I usually create a separate column

I usually create a separate column especially when I'm working with this

especially when I'm working with this because I don't want to change this one

because I don't want to change this one um I don't want to go in here and you

um I don't want to go in here and you know say um equals upper equals proper

know say um equals upper equals proper Etc there's a lot of different ways that

Etc there's a lot of different ways that you can change um names or not a lot but

you can change um names or not a lot but the main ones that you can change names

the main ones that you can change names and all of them are completely okay so

and all of them are completely okay so for example I'm going to hit equal upper

for example I'm going to hit equal upper oops upper and I'm going to go like this

oops upper and I'm going to go like this and close my parentheses so I selected

and close my parentheses so I selected this S I close my parenthese hit enter

this S I close my parenthese hit enter it is and I'm going to hit um in the

it is and I'm going to hit um in the bottom right I'm going toit double click

bottom right I'm going toit double click this and it's going to apply to all of

this and it's going to apply to all of them it is completely okay to have your

them it is completely okay to have your data like this if you want it to be like

data like this if you want it to be like that um if you want it to be all lower

that um if you want it to be all lower you can do that if you want it to be in

you can do that if you want it to be in proper case you can do that um there are

proper case you can do that um there are oops there are different um uses for all

oops there are different um uses for all of them and honestly as long as it's all

of them and honestly as long as it's all the same typically it's okay but if um

the same typically it's okay but if um you know for example if you're selling

you know for example if you're selling this to like a third party company or

this to like a third party company or something like that they may have um

something like that they may have um what they want for their ingestion

what they want for their ingestion process when they take your file in if

process when they take your file in if you send you know a weekly file or a

you send you know a weekly file or a monthly file they may want it exactly

monthly file they may want it exactly how they want it and you can change that

how they want it and you can change that to to what they want um but as long as

to to what they want um but as long as it's standardized for you it's all the

it's standardized for you it's all the same for you that is a good thing so now

same for you that is a good thing so now we have all of these um in the proper

we have all of these um in the proper case that's typically what I I do or I

case that's typically what I I do or I use upper those are the ones I use the

use upper those are the ones I use the most I don't usually use um lower and if

most I don't usually use um lower and if you go in here and you type in

you go in here and you type in lower you know it changes it to all

lower you know it changes it to all lower I don't typically do that um and

lower I don't typically do that um and I'm gon to add I'm oops I'm gonna say

I'm gon to add I'm oops I'm gonna say president Dash fixed and so now all of

president Dash fixed and so now all of these names um all of these uh different

these names um all of these uh different uppercase and lowercase these are all

uppercase and lowercase these are all fixed and and it just makes it so much

fixed and and it just makes it so much easier to read and you don't have

easier to read and you don't have different um uppercase and lowercase

different um uppercase and lowercase issues it's all the same so I'm going to

issues it's all the same so I'm going to keep keep that right

keep keep that right there uh if we move a little bit to the

there uh if we move a little bit to the right if you look at this prior now this

right if you look at this prior now this prior is a mess it it has stuff all over

prior is a mess it it has stuff all over and to be honest this is not really

and to be honest this is not really something that I would probably be using

something that I would probably be using um like in a real data set I would look

um like in a real data set I would look at this column and I would say this is

at this column and I would say this is pretty useless um if I had a very

pretty useless um if I had a very specific use case for this this data in

specific use case for this this data in this column I might try to you know

this column I might try to you know parse it out and do something but I

parse it out and do something but I don't uh this this is a completely

don't uh this this is a completely useless com to me so I'm actually going

useless com to me so I'm actually going to skip this one I'm going to go to this

to skip this one I'm going to go to this party one and this party one to me it

party one and this party one to me it looks pretty important because this is

looks pretty important because this is something that I know I can Group by um

something that I know I can Group by um and I can create visualizations with and

and I can create visualizations with and and kind of break that out and if you

and kind of break that out and if you look right here we're going to add um

look right here we're going to add um we're going to add a filter so now let's

we're going to add a filter so now let's open up party and take a look so if we

open up party and take a look so if we look right here we have Democratic

look right here we have Democratic democratic-republican Federalist

democratic-republican Federalist nonpartisan repu Republican Republicans

nonpartisan repu Republican Republicans wig and wig with a a date and some

wig and wig with a a date and some information in the back of it and then

information in the back of it and then some blanks um and it's really important

some blanks um and it's really important when we're when we're looking at these

when we're when we're looking at these um ones that we think we might Group by

um ones that we think we might Group by that we have these um properly grouped

that we have these um properly grouped so Republican and Republicans to me

so Republican and Republicans to me right off the bat looks like a spelling

right off the bat looks like a spelling error and so I'm just going to deselect

error and so I'm just going to deselect All I'm going to go to Republican

All I'm going to go to Republican Republicans and it's literally

Republicans and it's literally Republican all the way down except for

Republican all the way down except for for this last one and to me that's just

for this last one and to me that's just something that I would update so I would

something that I would update so I would just go right here I do that if I didn't

just go right here I do that if I didn't do that and then I try to create let's

do that and then I try to create let's say a pivot table on here I'll have its

say a pivot table on here I'll have its own group of Republicans and it wouldn't

own group of Republicans and it wouldn't be added to Republican and maybe that's

be added to Republican and maybe that's on purpose but let's just presume that

on purpose but let's just presume that we know this data extremely well and

we know this data extremely well and that's not supposed to be like that

that's not supposed to be like that right again that that just comes back to

right again that that just comes back to knowing your data really well

knowing your data really well understanding what it um you know what

understanding what it um you know what it should look like and we know that it

it should look like and we know that it should not be like that so we're going

should not be like that so we're going to fix that uh the next thing that we're

to fix that uh the next thing that we're going to fix um and as you can see it it

going to fix um and as you can see it it got rid of it next thing we're going to

got rid of it next thing we're going to fix is this wig

fix is this wig um that's just like an error that's

um that's just like an error that's that's some issue on the the data side

that's some issue on the the data side and we're just going to fix that by

and we're just going to fix that by updating it and that's it I would always

updating it and that's it I would always be keeping um a a copy of this with the

be keeping um a a copy of this with the raw data uh somewhere else because this

raw data uh somewhere else because this is presumably like a working document

is presumably like a working document this is not

this is not a um you know you aren't saving over

a um you know you aren't saving over your original file let's just say that

your original file let's just say that and then let's take a look at these

and then let's take a look at these blanks real quick um okay so there are

blanks real quick um okay so there are these rows right here that have nothing

these rows right here that have nothing I think we're okay but if we see

I think we're okay but if we see anything different 47 48 okay so yeah

anything different 47 48 okay so yeah it's just these ones right here that

it's just these ones right here that have no data in it anyways it's just

have no data in it anyways it's just seeing it in the filter so not an issue

seeing it in the filter so not an issue at all so okay we're looking good we've

at all so okay we're looking good we've gone all the way over we we fixed this

gone all the way over we we fixed this President we skipped this one um we we

President we skipped this one um we we cleaned up this party and I kept this

cleaned up this party and I kept this one in here because I'm not exactly sure

one in here because I'm not exactly sure if that's a Democratic or republican so

if that's a Democratic or republican so I'm going to keep it its own thing um

I'm going to keep it its own thing um I'm not a huge uh history buff in that

I'm not a huge uh history buff in that aspect the next one right here is um the

aspect the next one right here is um the next one right here is really easy uh

next one right here is really easy uh this is something that happens all the

this is something that happens all the time especially on actually most often

time especially on actually most often it's happens on numerical data so like

it's happens on numerical data so like uh you know there'll be a number of 1,1

uh you know there'll be a number of 1,1 and then there'll be a space after it

and then there'll be a space after it for absolutely no reason uh and it

for absolutely no reason uh and it happens all the time it does happen like

happens all the time it does happen like this as well um where you'll see this

this as well um where you'll see this and all you got to do is do trim and

and all you got to do is do trim and select the the cell we're going to close

select the the cell we're going to close that parenthesis and we're going to

that parenthesis and we're going to apply that all the way down what is so

apply that all the way down what is so fantastic about the trim is that it's

fantastic about the trim is that it's really intuitive and it knows basically

really intuitive and it knows basically everything it needs to do for example um

everything it needs to do for example um it gets gets rid of the um spaces before

it gets gets rid of the um spaces before it gets rid of extra spaces in the

it gets rid of extra spaces in the middle and um it'll get rid of extra

middle and um it'll get rid of extra spaces at the end um which you wouldn't

spaces at the end um which you wouldn't be able to see but they are there and

be able to see but they are there and they they absolutely can cause issues if

they they absolutely can cause issues if you have spaces at the end that you

you have spaces at the end that you cannot see um let's take this one for

cannot see um let's take this one for example like if I had spaces at the end

example like if I had spaces at the end that can cause issues when you insert or

that can cause issues when you insert or or or put that into a database um that

or or put that into a database um that happens a lot with numbers um you know

happens a lot with numbers um you know when you're putting that into SQL

when you're putting that into SQL that can cause issues and so you really

that can cause issues and so you really it is important to actually do that trim

it is important to actually do that trim um and you can do that on all of your

um and you can do that on all of your columns or just ones that you know

columns or just ones that you know you're having issues with but once you

you're having issues with but once you import that data into SQL you will know

import that data into SQL you will know if there's an issue or not when you

if there's an issue or not when you actually try to start using it so we're

actually try to start using it so we're going to say Vice and we're going to say

going to say Vice and we're going to say fixed oops there we go uh this next one

fixed oops there we go uh this next one is one that you'll run into a lot when

is one that you'll run into a lot when you're working with numerical data you

you're working with numerical data you will encounter so many different issues

will encounter so many different issues um one that I run into a lot is I I've

um one that I run into a lot is I I've worked with a lot of cost data or

worked with a lot of cost data or pricing data and when it's in an Excel

pricing data and when it's in an Excel it h it sometimes comes in with um these

it h it sometimes comes in with um these currencies like a dollar sign a pound

currencies like a dollar sign a pound sign things like that and when you put

sign things like that and when you put that into

that into SQL it just is a nuisance right you're

SQL it just is a nuisance right you're not going to be able to run um it's

not going to be able to run um it's going to go in as a text or it's going

going to go in as a text or it's going to be like a string right because it has

to be like a string right because it has that special character and you don't

that special character and you don't want that you don't want to have to then

want that you don't want to have to then go in and then change things around you

go in and then change things around you just want to be able to start um you

just want to be able to start um you know doing calculations on those numbers

know doing calculations on those numbers so what you can do is sometimes it'll

so what you can do is sometimes it'll come in as a text sometimes it'll come

come in as a text sometimes it'll come in as um currency which I think this

in as um currency which I think this one's a currency we are just going to

one's a currency we are just going to change that to be a number and then

change that to be a number and then we're going to get rid of these

we're going to get rid of these oops and get rid of those that it

oops and get rid of those that it doesn't look as pretty but that is much

doesn't look as pretty but that is much more useful than actually having the

more useful than actually having the currency on there with the decimals this

currency on there with the decimals this actually is so much easier when you when

actually is so much easier when you when you want to use it for almost anything

you want to use it for almost anything because you're able to add and uh do

because you're able to add and uh do things properly in other systems in

things properly in other systems in Excel I think it does understand it um

Excel I think it does understand it um but you know that can cause issues so

but you know that can cause issues so there is how you do that the next thing

there is how you do that the next thing that we're going to look at is these

that we're going to look at is these dates and just notoriously whenever I

dates and just notoriously whenever I see a date field I know there's going to

see a date field I know there's going to be an issue with it it's very rare

be an issue with it it's very rare that I get a date field that is perfect

that I get a date field that is perfect uh it just it is genuinely is um is a

uh it just it is genuinely is um is a novelty when that happens and most of

novelty when that happens and most of the time it has to do with um let's say

the time it has to do with um let's say a date comes into Excel and it's in a

a date comes into Excel and it's in a text format or date comes into Excel and

text format or date comes into Excel and they're not the same in this example

they're not the same in this example they are not the same um and we just

they are not the same um and we just want them to all be similar they say

want them to all be similar they say date on if you look right here it says

date on if you look right here it says date it says date it looks like it

date it says date it looks like it should be the the same um but if we go

should be the the same um but if we go like this it all looks the same right

like this it all looks the same right there's no issues at all if we were to

there's no issues at all if we were to um try to use that it may or may not be

um try to use that it may or may not be an issue but we don't want to leave that

an issue but we don't want to leave that to chance later on if you're using this

to chance later on if you're using this with python or something like that it

with python or something like that it can cause issues U maybe not in SQL

can cause issues U maybe not in SQL because it may um see the underlying um

because it may um see the underlying um what's in the underlying cell not just

what's in the underlying cell not just what we see but some systems won't and

what we see but some systems won't and so you want to make sure that they're

so you want to make sure that they're all the same

all the same and so you know what we were doing back

and so you know what we were doing back here with um oops with the party and we

here with um oops with the party and we were looking at this uh this filter and

were looking at this uh this filter and identifying the issues I usually do that

identifying the issues I usually do that on date fields as well and and

on date fields as well and and oftentimes um I know just for just for

oftentimes um I know just for just for demonstration purposes ofttimes I will

demonstration purposes ofttimes I will get something like that and then I'll

get something like that and then I'll come up here and I'll notice that

come up here and I'll notice that there's this one random number that

there's this one random number that happens all the time all the time um and

happens all the time all the time um and so you know you want to make sure that

so you know you want to make sure that you um that you look at these things and

you um that you look at these things and just just do at least a quick glance if

just just do at least a quick glance if not kind of doing a kind of a deep dive

not kind of doing a kind of a deep dive into it but all we're going to do is

into it but all we're going to do is we're going to do both of these and

we're going to do both of these and we're going to do a short date and let's

we're going to do a short date and let's take a look and see if that fixed it and

take a look and see if that fixed it and so now they are all the same format and

so now they are all the same format and that is fantastic that is exactly what

that is fantastic that is exactly what we want we're going to go back through

we want we're going to go back through here we're going to get rid of these um

here we're going to get rid of these um again this is a

again this is a working um this is a working document

working um this is a working document oops uh we need to we're I'm going to do

oops uh we need to we're I'm going to do um control shift down oops let me go

um control shift down oops let me go back up do control shift down and copy

back up do control shift down and copy and what I'm going to do right now is

and what I'm going to do right now is I'm actually going to copy let me do it

I'm actually going to copy let me do it right here I'll show you sometimes I do

right here I'll show you sometimes I do this does just depends I'm going to go

this does just depends I'm going to go right here I'm going to hit rightclick

right here I'm going to hit rightclick and I'm going to paste as a value which

and I'm going to paste as a value which means it's not going to take the

means it's not going to take the calculation or the formula that I just

calculation or the formula that I just did

did uh it's going to actually paste it as

uh it's going to actually paste it as that value so we just replaced it um

that value so we just replaced it um right here you can see up here it says

right here you can see up here it says equals trim of

equals trim of G2 this now now that I copied and pasted

G2 this now now that I copied and pasted it over as a value um it got rid of that

it over as a value um it got rid of that um calculation and now it is actually a

um calculation and now it is actually a string so we don't need this anymore and

string so we don't need this anymore and I'll do the same thing over here as

I'll do the same thing over here as well I'm going to control shift down

well I'm going to control shift down copy and I just hit the right key uh or

copy and I just hit the right key uh or the left key sorry now I'm going to

the left key sorry now I'm going to right click and I'm going to do paste as

right click and I'm going to do paste as a value and again it has this proper and

a value and again it has this proper and now it doesn't have the proper it's

now it doesn't have the proper it's actually the value that was here so

actually the value that was here so that's really important to note uh and

that's really important to note uh and we're going to get rid of that one and

we're going to get rid of that one and so now what we have is is already

so now what we have is is already looking much better now one of the last

looking much better now one of the last things I we're going to look at is

things I we're going to look at is deleting columns that we are not going

deleting columns that we are not going to use and this is why it's so important

to use and this is why it's so important to keep a backup or or or the raw data

to keep a backup or or or the raw data not in this file because if you start

not in this file because if you start saving over this file and this is your

saving over this file and this is your raw file uh that can mess up a lot of

raw file uh that can mess up a lot of things and that happens to me before and

things and that happens to me before and it's terrible and then you have to

it's terrible and then you have to request another file or you have to go

request another file or you have to go back and find it or something like that

back and find it or something like that it's terrible um so so this is our

it's terrible um so so this is our working document so we can mess with

working document so we can mess with this and do whatever we want for our

this and do whatever we want for our purposes now for us um I can already

purposes now for us um I can already tell you that this prior is a bunch of

tell you that this prior is a bunch of nonsense and we do not need it we're not

nonsense and we do not need it we're not going to use it for anything and it and

going to use it for anything and it and if we have um this is a small very small

if we have um this is a small very small data set this only has like um let's say

data set this only has like um let's say you know one two three four five six

you know one two three four five six seven eight we have like eight columns

seven eight we have like eight columns that we're you know kind of using that

that we're you know kind of using that has data eight or nine now that's a

has data eight or nine now that's a small data I've had ones with literally

small data I've had ones with literally like hundreds um and and it has so many

like hundreds um and and it has so many columns uh so much data and sometimes

columns uh so much data and sometimes it's good to just trim it back to the

it's good to just trim it back to the things you know you're going to use this

things you know you're going to use this to me is absolutely useless um we're

to me is absolutely useless um we're going to delete that

going to delete that and then right over here it's pretty

and then right over here it's pretty redundant um it's just one number off

redundant um it's just one number off but if we scroll down just a little bit

but if we scroll down just a little bit um it goes it's basically just counts

um it goes it's basically just counts it's a you could even call it a unique

it's a you could even call it a unique um identifier if you want sure why not

um identifier if you want sure why not but we don't need both um so we're going

but we don't need both um so we're going to get rid of this first one and now we

to get rid of this first one and now we have more of the useful and relevant

have more of the useful and relevant data rather than the stuff that we

data rather than the stuff that we absolutely know that we are not going to

absolutely know that we are not going to use um these date updated and date

use um these date updated and date created we may never use them but we

created we may never use them but we might um so it doesn't hurt to keep it

might um so it doesn't hurt to keep it on hand those other ones are ones that

on hand those other ones are ones that we are almost certain we will never use

we are almost certain we will never use again keep a backup just in case you

again keep a backup just in case you need it you can always go back and get

need it you can always go back and get it so you know if you go back to what we

it so you know if you go back to what we started with and you look at what we

started with and you look at what we have now it is much cleaner it's much

have now it is much cleaner it's much more usable and these are small subtle

more usable and these are small subtle changes um especially with this very

changes um especially with this very small data set of only like 50 rows or

small data set of only like 50 rows or or 46 rows but you're going to be

or 46 rows but you're going to be working with data sets that are

working with data sets that are thousands tens of thousands hundreds of

thousands tens of thousands hundreds of thousands of rows and you need to know

thousands of rows and you need to know how to kind of look at this data

how to kind of look at this data standardize it um format it properly for

standardize it um format it properly for what you're going to be using it for if

what you're going to be using it for if you're keeping it in Excel there are

you're keeping it in Excel there are different things that you may do than if

different things that you may do than if you're putting it into a database or

you're putting it into a database or going to be using it in you know um

going to be using it in you know um using python to to access it so you need

using python to to access it so you need to kind of know your use case but these

to kind of know your use case but these are some things that I do all the time

are some things that I do all the time to kind of clean up the data before I

to kind of clean up the data before I use it for something whether I'm

use it for something whether I'm creating pivot tables or I'm inserting

creating pivot tables or I'm inserting it into or I'm putting it into SQL these

it into or I'm putting it into SQL these are things I do all the time and so

are things I do all the time and so hopefully that helps give you kind of an

hopefully that helps give you kind of an idea of some of the things that you

idea of some of the things that you should be looking for when you're

should be looking for when you're actually cleaning data and it's really

actually cleaning data and it's really important to understand why you're

important to understand why you're actually making these changes and the

actually making these changes and the reason you're making these changes

reason you're making these changes because some of the things that I did

because some of the things that I did today may not be things you want to do

today may not be things you want to do on a different data set that has

on a different data set that has different uses and different um purposes

different uses and different um purposes for so you know take everything that

for so you know take everything that I've said and and apply it um with a

I've said and and apply it um with a little grain of salt to your data set

little grain of salt to your data set because your spefic specific needs may

because your spefic specific needs may be different than what I wanted when I

be different than what I wanted when I was cleaning my data set so I hope this

was cleaning my data set so I hope this was helpful I hope you this gave you a

was helpful I hope you this gave you a small glimpse of some of the things that

small glimpse of some of the things that I'm looking for when I clean a data set

I'm looking for when I clean a data set or I get a new data set in and I'm kind

or I get a new data set in and I'm kind of you know analyzing it figuring out

of you know analyzing it figuring out what I need to fix in it I hope this has

what I need to fix in it I hope this has been helpful uh with that being said

been helpful uh with that being said thank you so much for watching I really

thank you so much for watching I really appreciate it if you like this video be

appreciate it if you like this video be sure to like And subscribe below and

sure to like And subscribe below and I'll see you in the next

I'll see you in the next [Music]

[Music] video

[Music] what's going on everybody welcome back

what's going on everybody welcome back to the Excel tutorial Series today we're

to the Excel tutorial Series today we're going to create an entire project in

going to create an entire project in [Music]

[Music] Excel now if you've never done a

Excel now if you've never done a complete project in Excel where you take

complete project in Excel where you take the data you clean it then you create an

the data you clean it then you create an actual dashboard where people can click

actual dashboard where people can click on things and filter things this is

on things and filter things this is going to be a really great learning

going to be a really great learning opportunity as well as potentially you

opportunity as well as potentially you know a simple project that you can use

know a simple project that you can use for your portfolio or you can spice

for your portfolio or you can spice things up and go a little farther than

things up and go a little farther than what we're going to be doing in today's

what we're going to be doing in today's video I will walk you through every

video I will walk you through every single step of the way and hopefully we

single step of the way and hopefully we learn something together and without

learn something together and without further Ado let's jump right into it

further Ado let's jump right into it let's jump onto my screen and get

let's jump onto my screen and get started with the project all right so

started with the project all right so this is the data set that we're going to

this is the data set that we're going to be working with I will leave a link in

be working with I will leave a link in the description to my GitHub where you

the description to my GitHub where you can go and download it so you can be

can go and download it so you can be working with the exact same data set

working with the exact same data set that I am using now before we actually

that I am using now before we actually get into this data and start looking at

get into this data and start looking at it I'm going to show you what the final

it I'm going to show you what the final dashboard is going to look like um we're

dashboard is going to look like um we're going to create a few different types of

going to create a few different types of visualizations nothing too crazy um and

visualizations nothing too crazy um and then we'll create some filters as well

then we'll create some filters as well so we can kind of you know create some

so we can kind of you know create some interactive filters with our data so

interactive filters with our data so let's go right on over to our data set

let's go right on over to our data set now I'm going to hide this because we

now I'm going to hide this because we are not going to use that but what I am

are not going to use that but what I am going to do before we do anything is I'm

going to do before we do anything is I'm going to create a

going to create a dashboard and I'm going to create a

dashboard and I'm going to create a pivot table oops

pivot table oops and I'm going to create a working sheet

and I'm going to create a working sheet so um all these things have different

so um all these things have different uses and I'll explain that as we go

uses and I'll explain that as we go along so this is our data set um I'm

along so this is our data set um I'm going to copy this over to our working

going to copy this over to our working sheet when I go into you know an Excel

sheet when I go into you know an Excel and I'm working on something I don't

and I'm working on something I don't like to you know use just the one that I

like to you know use just the one that I was using in case I mess something up

was using in case I mess something up and it saves over or's some issue I like

and it saves over or's some issue I like to create a working sheet and keep the

to create a working sheet and keep the raw data right over here it just makes

raw data right over here it just makes my life easier I don't have to save it

my life easier I don't have to save it and then you know open up a different

and then you know open up a different Excel to compare them so we have our

Excel to compare them so we have our bike buyers this is our working sheets

bike buyers this is our working sheets this is our raw data this is the one

this is our raw data this is the one we're actually be working on today so

we're actually be working on today so let's um let's start looking at it

let's um let's start looking at it really quick and just kind of glance and

really quick and just kind of glance and see what data we're working with and

see what data we're working with and then we'll start cleaning it up making

then we'll start cleaning it up making it more useful for what we are going to

it more useful for what we are going to be using it for and then we'll start

be using it for and then we'll start building out the dashboard so right here

building out the dashboard so right here we have an ID that should be be a unique

we have an ID that should be be a unique ID to each person uh this is their

ID to each person uh this is their marital status so married or single this

marital status so married or single this is their gender male female we have

is their gender male female we have their income children their education

their income children their education their occupation do they own a home how

their occupation do they own a home how many cars they own how long their

many cars they own how long their commute is the region where they live

commute is the region where they live their age and if they purchased a bike

their age and if they purchased a bike and this column right here is extremely

and this column right here is extremely important this is going to tell us

important this is going to tell us whether they did or did not buy a bike

whether they did or did not buy a bike so we got their information they're

so we got their information they're looking for a bike but they either

looking for a bike but they either decided not to buy a bike or they did

decided not to buy a bike or they did buy a bike and we're going to be using

buy a bike and we're going to be using that one a lot in in this video and so

that one a lot in in this video and so um you know this is basically the data

um you know this is basically the data set that we're working with um some of

set that we're working with um some of the demographics and and information

the demographics and and information behind the person so what we want to do

behind the person so what we want to do when we are cleaning the data before we

when we are cleaning the data before we do anything uh I like to see if there

do anything uh I like to see if there are any duplicates in here um what we're

are any duplicates in here um what we're going to do is come right up here we can

going to do is come right up here we can go to

go to uh where is it right here we got remove

uh where is it right here we got remove duplicates so we're going to click on

duplicates so we're going to click on that it selects every single one we just

that it selects every single one we just want to see if there's any useless

want to see if there's any useless duplicated data that we do not need uh

duplicated data that we do not need uh and the data is a header so we're going

and the data is a header so we're going to click

to click okay all right so we had a ton of

okay all right so we had a ton of duplicates in there uh for whatever

duplicates in there uh for whatever reason so yeah we do have duplicates in

reason so yeah we do have duplicates in there so I'm glad we did that otherwise

there so I'm glad we did that otherwise we would have uh you know not good data

we would have uh you know not good data and we don't want that let's start right

and we don't want that let's start right over here um the ID of course we're not

over here um the ID of course we're not going to change the marital status and

going to change the marital status and gender are M's s's fs and M's um this

gender are M's s's fs and M's um this isn't inherently a bad thing to have it

isn't inherently a bad thing to have it like this but you know we have to think

like this but you know we have to think about it from the perspective of someone

about it from the perspective of someone who's going to be using this dashboard

who's going to be using this dashboard do they know what M ands is do they know

do they know what M ands is do they know what M uh and F is and if they don't

what M uh and F is and if they don't it's better to just spell it out for the

it's better to just spell it out for the most part um so let's just do that so

most part um so let's just do that so we're going to click on the column B

we're going to click on the column B we're going to hit controll H that's

we're going to hit controll H that's going to bring up our find and replace

going to bring up our find and replace now there's an m in both of these

now there's an m in both of these columns and there's different things one

columns and there's different things one is married and one means male so we're

is married and one means male so we're going to do is we're going to search by

going to do is we're going to search by columns um and we'll have match case I

columns um and we'll have match case I don't think that's going to change

don't think that's going to change anything but that just means an exact

anything but that just means an exact match uh and we're going to do m equals

match uh and we're going to do m equals and we're going to replace it with

and we're going to replace it with married and we'll replace all awesome

married and we'll replace all awesome and then we do s is

and then we do s is single this one is super easy we're

single this one is super easy we're going to do the exact same thing right

going to do the exact same thing right here so column C to hit contrl H we'll

here so column C to hit contrl H we'll do still has by column so we'll do m is

do still has by column so we'll do m is male we'll replace all of those and F is

male we'll replace all of those and F is female and replace all those that's

female and replace all those that's great uh you know the next column right

great uh you know the next column right here is income and in a SE in a previous

here is income and in a SE in a previous video I talked about how I don't

video I talked about how I don't typically like it in this format and

typically like it in this format and that's true um if you're doing calcul

that's true um if you're doing calcul ations on it or or any other thing it

ations on it or or any other thing it can mess it up sometimes having the

can mess it up sometimes having the dollar sign or it being a currency we're

dollar sign or it being a currency we're not really going to mess with it too

not really going to mess with it too much right now um what we can do is just

much right now um what we can do is just kind

kind of make sure all of it's currency um

of make sure all of it's currency um we'll just go like that to make it a

we'll just go like that to make it a little simpler but we're not going to

little simpler but we're not going to change it to like a

change it to like a numeric um we will use this in the

numeric um we will use this in the visualization we'll see how it looks and

visualization we'll see how it looks and if we need to we'll come back and change

if we need to we'll come back and change it if not we'll keep it how it is um so

it if not we'll keep it how it is um so so that's all we're going to do to that

so that's all we're going to do to that one uh the children those look good we

one uh the children those look good we have

have education partial College partial High

education partial College partial High School this looks fine to me um if

School this looks fine to me um if there's any spelling errors or anything

there's any spelling errors or anything like that of course we need to clean

like that of course we need to clean that up it doesn't look like there

that up it doesn't look like there is

is occupation skilled manual manual okay

occupation skilled manual manual okay those should be separate are they a

those should be separate are they a homeowner should just be yes or no all

homeowner should just be yes or no all right we have Cars 1 2 3 4 good night

right we have Cars 1 2 3 4 good night who owns four cars um and then we have

who owns four cars um and then we have the commute distance uh and you know

the commute distance uh and you know there's nothing terrible about this it's

there's nothing terrible about this it's giving you ranges um which can be a good

giving you ranges um which can be a good thing I say let's keep it for now but I

thing I say let's keep it for now but I have a feeling when we get further and

have a feeling when we get further and we start using in the visualization we

we start using in the visualization we may want to change this so let's just

may want to change this so let's just hold off for now um but if needed we

hold off for now um but if needed we will come back to this and we'll change

will come back to this and we'll change this um and then we have our region and

this um and then we have our region and that looks totally fine and we have our

that looks totally fine and we have our age now when you're using ages typically

age now when you're using ages typically you have some type of like age bracket

you have some type of like age bracket or or age range and you do that because

or or age range and you do that because there are so many ages in here right

there are so many ages in here right it's 25 all the way down to 89 and if

it's 25 all the way down to 89 and if you're using that in some type of

you're using that in some type of visualization it could just get really

visualization it could just get really messy and so you'll create kind of you

messy and so you'll create kind of you know just brackets around these so that

know just brackets around these so that you can kind of condense it and make it

you can kind of condense it and make it a little bit easier to understand so

a little bit easier to understand so let's do that and just create a new

let's do that and just create a new column and then then we can use that for

column and then then we can use that for our dashboard so let's go right up here

our dashboard so let's go right up here we're just going to create a new column

we're just going to create a new column uh we'll call this age

uh we'll call this age brackets and what we can do is we can

brackets and what we can do is we can use an if statement to kind of say if

use an if statement to kind of say if it's older than or less than and and and

it's older than or less than and and and kind of give them these ranges um that's

kind of give them these ranges um that's one way to do it and that's the way

one way to do it and that's the way we're going to do it right now so let's

we're going to do it right now so let's go up here and what we want to do is we

go up here and what we want to do is we want to say is going to we're going to

want to say is going to we're going to say equals and we're going to do if and

say equals and we're going to do if and we're going to close that parenthesis

we're going to close that parenthesis now what we're going to say is if

now what we're going to say is if this we'll go right back up here if this

this we'll go right back up here if this is less than so we're going do this 31

is less than so we're going do this 31 and we're going to say comma so if they

and we're going to say comma so if they are less than 31 what do we want to call

are less than 31 what do we want to call them what do we want their their you

them what do we want their their you know name to be we'll call them

know name to be we'll call them adolescent oops that's not how you spell

adolescent oops that's not how you spell adolescent adolescent um and then if

adolescent adolescent um and then if they're not what we're going to do is

they're not what we're going to do is we're going to say it's

we're going to say it's invalid okay and let's just see if this

invalid okay and let's just see if this one works

one works first all right it's not working at all

first all right it's not working at all um okay so basically what we did was um

um okay so basically what we did was um incorrect we did it backward uh we want

incorrect we did it backward uh we want to do I said uh L2 is greater than 31 no

to do I said uh L2 is greater than 31 no we want to do like this so let's do that

we want to do like this so let's do that now all right and it should pull up

now all right and it should pull up where if they're under the age of 31 so

where if they're under the age of 31 so if they're 30 or below is basically what

if they're 30 or below is basically what it's saying so if they're 31 they'll be

it's saying so if they're 31 they'll be invalid but if they're 30 or below it's

invalid but if they're 30 or below it's adolescent so it is working properly um

adolescent so it is working properly um and let's see what it see what it says

and let's see what it see what it says perfect so this one is working and and

perfect so this one is working and and now what we want to do is we actually

now what we want to do is we actually want to build on this and make it uh

want to build on this and make it uh kind of like a nested if statement if

kind of like a nested if statement if you've ever heard of that or done that

you've ever heard of that or done that before so this is our first first if

before so this is our first first if statement and this is going to be this

statement and this is going to be this is invalid this is our value if false

is invalid this is our value if false statement this whole statement is going

statement this whole statement is going to become our value if false for a

to become our value if false for a different if statement um so let let me

different if statement um so let let me write it out and hopefully that'll make

write it out and hopefully that'll make sense but we're going to say if do open

sense but we're going to say if do open parentheses and we're going to do it

parentheses and we're going to do it like this and let's just get rid of this

like this and let's just get rid of this for a

for a second all right uh what did I do and

second all right uh what did I do and let me do

let me do oops give me a

oops give me a second okay we have our if let me just

second okay we have our if let me just write that out again we have our if

write that out again we have our if there we go so now what we're going to

there we go so now what we're going to do is we're going to write basically the

do is we're going to write basically the next part of it so we're going to say if

next part of it so we're going to say if that L2 is and we're going to do this

that L2 is and we're going to do this time we're going to do greater than or

time we're going to do greater than or equal to 31 so now it's going to include

equal to 31 so now it's going to include that 31 so right here we did anything

that 31 so right here we did anything less than 31 so it's 30 and below this

less than 31 so it's 30 and below this one is going to be 31 and above so we're

one is going to be 31 and above so we're going to say these people are middle

going to say these people are middle Ag and if not then it's going to go to

Ag and if not then it's going to go to this if statement and then we need to

this if statement and then we need to close it I believe so now let's try this

close it I believe so now let's try this all

all right fantastic now if um everybody

right fantastic now if um everybody should be in one of these areas right

should be in one of these areas right everyone should either be an adolescent

everyone should either be an adolescent or middle age because basically all

or middle age because basically all we're saying is is if they're older than

we're saying is is if they're older than 31 or 30 or below that's all these two

31 or 30 or below that's all these two statements do so we have um you know our

statements do so we have um you know our next group now we can add and go even

next group now we can add and go even further into this and now we can use

further into this and now we can use this entire thing as the um what was it

this entire thing as the um what was it called the value if false section so

called the value if false section so that's what we're going to do we're

that's what we're going to do we're going to do one more so we're have three

going to do one more so we're have three different categories so we're going to

different categories so we're going to say if and do uh an open parenthesis and

say if and do uh an open parenthesis and we're going to say if oh actually Let's

we're going to say if oh actually Let's Do It

Do It um let's not do it to this one

um let's not do it to this one let's do to this top one just

let's do to this top one just easier uh so we're going to say if open

easier uh so we're going to say if open parenthesis we're going to say L2 and

parenthesis we're going to say L2 and this time we're going to say anybody

this time we're going to say anybody over the age of 50 uh or we can do 55

over the age of 50 uh or we can do 55 let's do 55 so we'll do 55 and we're

let's do 55 so we'll do 55 and we're going to call them

going to call them old and we'll do a comma and this is the

old and we'll do a comma and this is the value if false statement and we need to

value if false statement and we need to close our parenthesis so let's try this

close our parenthesis so let's try this anybody over the age of 55 should have

anybody over the age of 55 should have old um you know maybe we'll do 54 so

old um you know maybe we'll do 54 so anybody who is 55 is considered old I

anybody who is 55 is considered old I think that's fair I think that's fair

think that's fair I think that's fair guys oops I should have

guys oops I should have done I should have done that to this one

done I should have done that to this one let me get out of this and we'll do

let me get out of this and we'll do 54 my dad is 55 that's why I'm doing it

54 my dad is 55 that's why I'm doing it like this this is fre

like this this is fre dead CU he should be in this old

dead CU he should be in this old category to be fair so now we have

category to be fair so now we have adolescent adolescent middle-age and old

adolescent adolescent middle-age and old these are three categories so we can now

these are three categories so we can now have these buckets these different

have these buckets these different groups of Ages and it's much more usable

groups of Ages and it's much more usable than these individual ages um and so we

than these individual ages um and so we will be using this in our in our

will be using this in our in our dashboard for sure now our next one is

dashboard for sure now our next one is the purchased bike uh and we're not

the purchased bike uh and we're not going to do anything with that so you

going to do anything with that so you know that is that is that one and you

know that is that is that one and you know there wasn't a ton to clean up here

know there wasn't a ton to clean up here we removed some duplicates um I don't

we removed some duplicates um I don't know why it says that what did I do

married married what does this mean even mean I did I write that did I mess this

mean I did I write that did I mess this up guys

up guys oh when I did the m and the S uh

oh when I did the m and the S uh replacement in there it replaced it with

replacement in there it replaced it with married and single it's supposed to say

married and single it's supposed to say marital status

marital status oops thanks for catching that guys

oops thanks for catching that guys thanks for catching that I hope that's

thanks for catching that I hope that's how you spell marital uh we'll see so

how you spell marital uh we'll see so uh we are going to keep it just like

uh we are going to keep it just like this now what we are going to

now now what we are going to do is build pivot tables with this data so we had

pivot tables with this data so we had our raw data we have our working sheet

our raw data we have our working sheet and now we want to create pivot tables

and now we want to create pivot tables and pivot tables is how you actually

and pivot tables is how you actually help build your dashboards or help build

help build your dashboards or help build your visualizations so we're going to go

your visualizations so we're going to go right here we're going to hit whoops get

right here we're going to hit whoops get rid of that we're going to go right here

rid of that we're going to go right here we're going to insert and we're going to

we're going to insert and we're going to say pivot table and it's going to ask us

say pivot table and it's going to ask us what

what range so we're going to go back to the

range so we're going to go back to the working sheet and we'll just click here

working sheet and we'll just click here and hit control

and hit control a this is going to select all of our

a this is going to select all of our data for us so it's really easy and

data for us so it's really easy and we're going to hit okay and so now we

we're going to hit okay and so now we have all of

have all of our pivot I don't need I don't need to

our pivot I don't need I don't need to pull it out that far that was way too

pull it out that far that was way too far and now we have all of our pivot

far and now we have all of our pivot table information over here and so that

table information over here and so that should make it really easy to you know

should make it really easy to you know actually build out so what we're going

actually build out so what we're going to do is start selecting what columns

to do is start selecting what columns and what data we actually want to work

and what data we actually want to work with so the first one that we're going

with so the first one that we're going to build out is a dashboard that is

to build out is a dashboard that is basically looking at the average income

basically looking at the average income of somebody who either bought or did not

of somebody who either bought or did not buy a bike so we need in this one we're

buy a bike so we need in this one we're going to need their income that's

going to need their income that's definitely going to be a value right

definitely going to be a value right here um but we want to break it out by

here um but we want to break it out by male and female so let's look at their

male and female so let's look at their gender we going to pull that down into

gender we going to pull that down into the rows so

the rows so um this is basically a sum and no let's

um this is basically a sum and no let's look

look at let's make this an average so I just

at let's make this an average so I just went to the um I clicked right here I

went to the um I clicked right here I went to the value field settings and

went to the value field settings and we're just going to do an

we're just going to do an average all right and then we are going

average all right and then we are going to make these um and as you can see

to make these um and as you can see there's four decimal points um we'll

there's four decimal points um we'll keep it as is right now but we may need

keep it as is right now but we may need to go back and change something then

to go back and change something then we're going to look at if they purchased

we're going to look at if they purchased a bik or not and we're going to put that

a bik or not and we're going to put that right here so so we can see that uh

right here so so we can see that uh right here for the people who did not

right here for the people who did not buy a bike the females their their

buy a bike the females their their average salary was 53,000 the average

average salary was 53,000 the average salary for the average salary for males

salary for the average salary for males was 56,000 for yes the ones who did buy

was 56,000 for yes the ones who did buy a bike the average salary was 55 for

a bike the average salary was 55 for female and 60 for male so the people who

female and 60 for male so the people who had a little bit more money are buying

had a little bit more money are buying bikes and you can also see that uh the

bikes and you can also see that uh the men are making more money in this data

men are making more money in this data set just overall in general um so let's

set just overall in general um so let's make the visualization really quick but

make the visualization really quick but you know I don't know I'm not a huge fan

you know I don't know I'm not a huge fan of these decimal points and maybe we can

of these decimal points and maybe we can just change that in the visualization

just change that in the visualization we'll see um oops that's not what I

we'll see um oops that's not what I meant to

meant to do um let's do that so what we are going

do um let's do that so what we are going to do is we're going to click into here

to do is we're going to click into here we're going to click insert and we're

we're going to click insert and we're going to go to these recommended charts

going to go to these recommended charts and it's going to bring up basically

and it's going to bring up basically every single type that we would want um

every single type that we would want um and we can just click in here and see

and we can just click in here and see which one looks good uh oh yeah I love

which one looks good uh oh yeah I love those 3D ones those are my favorite you

those 3D ones those are my favorite you guys know that uh let's let's use this

guys know that uh let's let's use this one right here pretty simple um whoops

one right here pretty simple um whoops let's pull this right over here and as

let's pull this right over here and as is it looks pretty good um you know it

is it looks pretty good um you know it shows male female we have the average or

shows male female we have the average or the incomes right here whether they did

the incomes right here whether they did or did not purchase it um and so at a

or did not purchase it um and so at a glance it's pretty easy to see let's see

glance it's pretty easy to see let's see if there's anything um you know if you

if there's anything um you know if you want to change up style-wise go for it

want to change up style-wise go for it I'm just going to keep it as is um but

I'm just going to keep it as is um but let's see if there's anything we need to

let's see if there's anything we need to add right do we want to add these access

add right do we want to add these access titles uh for the most part I I tend to

titles uh for the most part I I tend to do that um it makes it pretty easy to

do that um it makes it pretty easy to see so we can go in here and we can just

see so we can go in here and we can just click it like this and we'll say

click it like this and we'll say income and we'll say oops and we'll do

income and we'll say oops and we'll do gender so that's what that

gender so that's what that is and and let's go back in here do we

is and and let's go back in here do we want to add a chart title we definitely

want to add a chart title we definitely want to add a chart title uh for most of

want to add a chart title uh for most of these we'll add a chart title for sure

these we'll add a chart title for sure so we'll say average

so we'll say average income per

income per purchase um I don't know if that's 100%

purchase um I don't know if that's 100% right but we'll we'll we'll use it uh if

right but we'll we'll we'll use it uh if we need to change it to be you know by

we need to change it to be you know by gender or something we can but um for

gender or something we can but um for now let's see do we want to add data

now let's see do we want to add data labels uh definitely not uh a data table

labels uh definitely not uh a data table um we can do this it may make it a

um we can do this it may make it a little easier to read I will say that

little easier to read I will say that again these numbers are just these

again these numbers are just these decimal points are really throwing me

decimal points are really throwing me off let's go see if um we can change it

off let's go see if um we can change it in here let's go

in here let's go to see if we can just make these numbers

to see if we can just make these numbers okay and um we can keep it like that or

okay and um we can keep it like that or we can even do something like this add

we can even do something like this add commas yeah I'm going to keep it just

commas yeah I'm going to keep it just like this I I think this just looks the

like this I I think this just looks the best um again I'm I'm getting adding

best um again I'm I'm getting adding commas here I'm changing the um decimal

commas here I'm changing the um decimal place right here it just makes it look a

place right here it just makes it look a little nicer a little cleaner um so

little nicer a little cleaner um so let's keep this exactly how it is um we

let's keep this exactly how it is um we can always change things if we want to

can always change things if we want to uh if we want to come back to it so we

uh if we want to come back to it so we created our pivot table and then we

created our pivot table and then we created our visualization basically

created our visualization basically exactly what we're going to do for all

exactly what we're going to do for all of these because again all of these need

of these because again all of these need um you know all of these need pivot

um you know all of these need pivot tables in order to create the

tables in order to create the visualization so let's um get out of

visualization so let's um get out of here we're going to scroll down and

here we're going to scroll down and we're going to create our next pivot

we're going to create our next pivot table and once we get done with all of

table and once we get done with all of the pivot tables that we need all the

the pivot tables that we need all the visualizations that we need then we will

visualizations that we need then we will um we will start so we're going to do

um we will start so we're going to do control a we're going do okay and

control a we're going do okay and basically do the exact same thing that

basically do the exact same thing that we did um this time we're going to look

we did um this time we're going to look at the distance so for this one I wanted

at the distance so for this one I wanted to see you know I try to you know I

to see you know I try to you know I created this already I've already done

created this already I've already done this entire project through but I

this entire project through but I haven't really talked about why or what

haven't really talked about why or what we're going to look at for this one you

we're going to look at for this one you know know we're looking at is their

know know we're looking at is their income does it change whether they

income does it change whether they bought or didn't buy one um so if they

bought or didn't buy one um so if they said yes you know is there a reason are

said yes you know is there a reason are they making more money is you know are

they making more money is you know are price points are the customers do they

price points are the customers do they make more money so you we cater to them

make more money so you we cater to them or not uh that's a good question uh

or not uh that's a good question uh another thing is you know we're we sell

another thing is you know we're we sell bikes or this person sells bikes so

bikes or this person sells bikes so commuting distance definitely makes a

commuting distance definitely makes a difference you know does the person who

difference you know does the person who is buying a bike live one mile away from

is buying a bike live one mile away from where they work or 20 miles away uh this

where they work or 20 miles away uh this will help us determine this next

will help us determine this next visualization will help us determine you

visualization will help us determine you know who who is doing that or who's

know who who is doing that or who's buying it so what we are going to do is

buying it so what we are going to do is we are going to look at the um that one

we are going to look at the um that one that we were looking at earlier the

that we were looking at earlier the commute distance so we're going to bring

commute distance so we're going to bring that right over here so we have these

that right over here so we have these you know one mile 10 Mile 1.2

you know one mile 10 Mile 1.2 Etc now we are going to uh again we're

Etc now we are going to uh again we're going to look at if they purchased a

going to look at if they purchased a bike

bike that's really important and let's make

that's really important and let's make that the column as well so now what we

that the column as well so now what we have is a count of these Nos and yeses

have is a count of these Nos and yeses whether they did or did not buy a bike

whether they did or did not buy a bike um one of the issues I already see and

um one of the issues I already see and we'll I'm going to visualize it and then

we'll I'm going to visualize it and then I'll show you that this 10 miles you

I'll show you that this 10 miles you know it's right next to the 0.1 so it's

know it's right next to the 0.1 so it's not an order um and that could be that

not an order um and that could be that could be an issue um so we may have to

could be an issue um so we may have to revise that somehow to put it at the

revise that somehow to put it at the very bottom because we can either do

very bottom because we can either do ascending

ascending or descending uh either one I don't

or descending uh either one I don't think is going to work so we may have to

think is going to work so we may have to work through that in just a second um I

work through that in just a second um I don't know if I did that my I plan for

don't know if I did that my I plan for that um yeah so it has this big dip

that um yeah so it has this big dip um yeah so let's let's create it um

um yeah so let's let's create it um that's okay we're going to figure this

that's okay we're going to figure this one out together because I honestly um I

one out together because I honestly um I didn't plan for this one so okay we have

didn't plan for this one so okay we have 0.1 miles that's exactly where it needs

0.1 miles that's exactly where it needs to be the one the two the five that's

to be the one the two the five that's exactly where it needs to be this 10

exactly where it needs to be this 10 miles is not and let's see if I change

miles is not and let's see if I change that 10 10 plus miles to 10 miles plus

that 10 10 plus miles to 10 miles plus let's see if that'll put it down here

let's see if that'll put it down here because I I don't know if it's looking

because I I don't know if it's looking at I don't know if it's reading it weird

at I don't know if it's reading it weird um but let's go into this working sheet

um but let's go into this working sheet and let's go right here and we're going

and let's go right here and we're going to do controll H and we'll do oops not

to do controll H and we'll do oops not this

this one um 10 miles plus let's get that in

one um 10 miles plus let's get that in there and we're going to do

there and we're going to do 10 uh

10 uh miles plus I I don't know if that's

miles plus I I don't know if that's actually going to work um we will see so

actually going to work um we will see so let's go back to the pivot

let's go back to the pivot table let's re go to the data let's

table let's re go to the data let's refresh uh no it didn't it didn't change

refresh uh no it didn't it didn't change it um okay so let's think about this

it um okay so let's think about this maybe if we change it to like a letter

maybe if we change it to like a letter it might change down here so start it

it might change down here so start it with uh miles that could work um let's

with uh miles that could work um let's try it it okay it's already

try it it okay it's already selected let's do the 10 plus miles okay

selected let's do the 10 plus miles okay so let's

so let's do

do um M uh more than 10

um M uh more than 10 miles and we'll replace all let's get

miles and we'll replace all let's get rid of

rid of this let's go to the pivot and refresh

this let's go to the pivot and refresh all right okay so it's not perfect but

all right okay so it's not perfect but it works

it works um and for what we're doing I think

um and for what we're doing I think we'll keep it how it is so we have our

we'll keep it how it is so we have our second one uh and you know there are

second one uh and you know there are different ways you can kind of change

different ways you can kind of change this one um you know on the last one we

this one um you know on the last one we did a ton of different stuff we can

did a ton of different stuff we can do just

do just do commute

do commute distance and we can

distance and we can say what do we want to say on this one

say what do we want to say on this one what is this oh this is the count um do

what is this oh this is the count um do we have to do we have to keep this

we have to do we have to keep this one um no there we go I'm just going to

one um no there we go I'm just going to do um just one and

do um just one and say commute

say commute distance and let's add a

distance and let's add a title chart title we can make this one

title chart title we can make this one um let's

um let's say

say distance per customer uh that's not 100%

distance per customer uh that's not 100% true because it's no or yes um that's

true because it's no or yes um that's that's the important part of this it's

that's the important part of this it's distance um average distance uh let's

distance um average distance uh let's see we'll just say customer

commute all right and we'll keep it just like that all right perfect I don't

like that all right perfect I don't think um let me see I don't think

think um let me see I don't think there's anything else we need to add on

there's anything else we need to add on that one all right now let's go right

that one all right now let's go right down here we're going to create our very

down here we're going to create our very last one uh we only had three so you

last one uh we only had three so you know sometimes you'll have a ton

know sometimes you'll have a ton sometimes you'll have like one on each

sometimes you'll have like one on each sheet and you'll create multiple sheets

sheet and you'll create multiple sheets but um do contr a um now we have our

but um do contr a um now we have our thing now this one we're going to be

thing now this one we're going to be looking at these age brackets that we

looking at these age brackets that we were looking at that we created um

were looking at that we created um something that I do honestly a lot is is

something that I do honestly a lot is is kind of bracket things in into groups

kind of bracket things in into groups like this and you know for this I'm just

like this and you know for this I'm just kind of made them up but you know it's

kind of made them up but you know it's good to know how to do this because I I

good to know how to do this because I I promise you this one happens a lot or I

promise you this one happens a lot or I use this one a ton and then we just want

use this one a ton and then we just want to look at who purchased a bike uh so

to look at who purchased a bike uh so the same thing as we did before so like

the same thing as we did before so like purchase a bike count of the purchase um

purchase a bike count of the purchase um you know pretty easy so we just have to

you know pretty easy so we just have to count of either no or yes for these age

count of either no or yes for these age ranges um and let's go to the insert

ranges um and let's go to the insert we'll go to

we'll go to recommendation um I personally like a

recommendation um I personally like a good line for this one um so

good line for this one um so let's this is already interesting we

let's this is already interesting we could do something like

could do something like this that's nice see this one versus

this that's nice see this one versus this it just adds a dot it looks nice

this it just adds a dot it looks nice we'll keep that one

we'll keep that one um so just really quick at a glance

um so just really quick at a glance really interesting people under the age

really interesting people under the age of 30 are not buying that many bikes um

of 30 are not buying that many bikes um age 30 to

age 30 to 54 uh 31 to 54 buying a ton of bikes uh

54 uh 31 to 54 buying a ton of bikes uh they buy more bikes or look at bikes

they buy more bikes or look at bikes more than anybody really interesting um

more than anybody really interesting um but yeah we'll make the dashboard in a

but yeah we'll make the dashboard in a little bit um let's make these chart

little bit um let's make these chart titles we'll

titles we'll do vert oops the

do vert oops the horizontal we just call

horizontal we just call this age

bracket um and then we'll add a chart title um again you can add some extra

title um again you can add some extra stuff if you want

stuff if you want to um but you don't need to uh none of

to um but you don't need to uh none of this other stuff we really need I'm just

this other stuff we really need I'm just kind of looking at the stuff we do need

kind of looking at the stuff we do need or do want uh so what do we want to call

or do want uh so what do we want to call this one let's call it customer

this one let's call it customer age brackets um and it's not perfect but

age brackets um and it's not perfect but we'll keep it as is for comparison um

we'll keep it as is for comparison um let me see if I can copy

let me see if I can copy um or or use this um real quick instead

um or or use this um real quick instead of the age brackets I'm going to get rid

of the age brackets I'm going to get rid of this and use the age

of this and use the age and then let's

and then let's use um let's insert

use um let's insert recommendation we use a

recommendation we use a line and we'll use this

line and we'll use this so This compared to this just think of

so This compared to this just think of it like if a customer or consumer or or

it like if a customer or consumer or or not a customer if somebody you're

not a customer if somebody you're working with is trying to use this

working with is trying to use this dashboard to understand this dashboard

dashboard to understand this dashboard this is going to be just it's going to I

this is going to be just it's going to I don't know it might melt their brain

don't know it might melt their brain just makes no sense it makes sense it's

just makes no sense it makes sense it's just all over the place it's really hard

just all over the place it's really hard to make sense of this it really is I

to make sense of this it really is I mean you can kind of see a pattern going

mean you can kind of see a pattern going up around like the mid-30s and then it

up around like the mid-30s and then it Trends downward but it's hard to see um

Trends downward but it's hard to see um it really is so doing these um these

it really is so doing these um these brackets really helps and you can even

brackets really helps and you can even add you know adolescent um you know 0o

add you know adolescent um you know 0o to 30 underneath it and in fact we may

to 30 underneath it and in fact we may want to do that um why not why not let's

want to do that um why not why not let's do that oh whoops um so why don't why

do that oh whoops um so why don't why don't we do that why don't we go back

don't we do that why don't we go back I'm just going to I'm doing this on the

I'm just going to I'm doing this on the Fly why don't we go

Fly why don't we go back uh what am I doing

back uh what am I doing whoops and this is all calculated but

whoops and this is all calculated but let's do

let's do adolescent 0

adolescent 0 to

to 30 let's do

30 let's do middleaged 31 through

middleaged 31 through 54 and then old 55 plus let's see if

54 and then old 55 plus let's see if this breaks anything I hope it doesn't

this breaks anything I hope it doesn't um and we'll go back to our pivot table

um and we'll go back to our pivot table let's refresh the

data uh okay it did mess with stuff okay never mind guys that was a

stuff okay never mind guys that was a terrible idea don't do that um perfect

terrible idea don't do that um perfect uh let's get rid of that that was a

uh let's get rid of that that was a terrible idea don't do that I'm glad we

terrible idea don't do that I'm glad we tested it out though I like I like to

tested it out though I like I like to see if it was going to work no it messed

see if it was going to work no it messed with the um the Order of Things um I I

with the um the Order of Things um I I intentionally named them adolescent

intentionally named them adolescent middle- Ag and old because it's it it

middle- Ag and old because it's it it makes sense for the visualization um but

makes sense for the visualization um but you know if if I change something and it

you know if if I change something and it messes with it I'm not going to mess

messes with it I'm not going to mess with it it was just an idea on the Fly

with it it was just an idea on the Fly guys come on all right so let's start

guys come on all right so let's start building out our dashboard now um when

building out our dashboard now um when we're building our dashboard what I

we're building our dashboard what I personally like to do is to have this

personally like to do is to have this pivot table sheet and then I will copy

pivot table sheet and then I will copy them over and later we'll hide these

them over and later we'll hide these other sheets beats um and I'll explain

other sheets beats um and I'll explain that a little bit but I like to have

that a little bit but I like to have this this one for us so we're going to

this this one for us so we're going to copy this so I just click on it hit

copy this so I just click on it hit controlc we're going to paste it right

controlc we're going to paste it right over

over here uh let's just make them small for

here uh let's just make them small for now that's oh gosh no let's not do that

now that's oh gosh no let's not do that oh these look terrible okay anyways

oh these look terrible okay anyways um let's copy this one

um let's copy this one over

over oops okay what did I just

oops okay what did I just do oh I didn't copy this one

do oh I didn't copy this one whoops it's not

whoops it's not copying okay we're going to go

copying okay we're going to go copy hit

copy hit paste

paste fantastic oops guys look away this is

fantastic oops guys look away this is this is tough to watch this is tough for

this is tough to watch this is tough for me to watch I'm the one doing it it is

me to watch I'm the one doing it it is tough for me to watch all right let's go

tough for me to watch all right let's go to this last one I'm I'm gonna try it

to this last one I'm I'm gonna try it again all right it worked this time so

again all right it worked this time so now we have um our our three

now we have um our our three visualizations this is perfect but now

visualizations this is perfect but now we actually want to create a dashboard

we actually want to create a dashboard now how do you do that how do you make

now how do you do that how do you make it look nice U and then we're going to

it look nice U and then we're going to add some you know filters and stuff like

add some you know filters and stuff like that how do we make it look nice um what

that how do we make it look nice um what happened here what changed what did we

do oh my goodness gracious all right let's copy

this let's paste this let's get rid of this I don't even know how that happened

this I don't even know how that happened I've never seen that before that was

I've never seen that before that was wild uh Excel is trying to destroy my

wild uh Excel is trying to destroy my whole video I mean I'm doing this for

whole video I mean I'm doing this for you Excel good night okay no problem at

you Excel good night okay no problem at all what we're going to do and how you

all what we're going to do and how you make this at least look nice um first

make this at least look nice um first off we can get rid of these grid lines

off we can get rid of these grid lines pretty easily and I recommend when you

pretty easily and I recommend when you do that when you make a dashboard just

do that when you make a dashboard just makes it look cleaner makes it look like

makes it look cleaner makes it look like an actual dashboard um let's go to view

an actual dashboard um let's go to view and grid lines so we can get rid of

and grid lines so we can get rid of these grid lines it just makes it look

these grid lines it just makes it look nicer um we're going to make you know we

nicer um we're going to make you know we can choose any color here here I'm just

can choose any color here here I'm just going to get choose a

going to get choose a color I like this and let's we're we're

color I like this and let's we're we're basically creating like a header right

basically creating like a header right if you're using like Tableau or

if you're using like Tableau or something um we're going to merge and

something um we're going to merge and center so it takes every single cell

center so it takes every single cell that we have highlighted creates it into

that we have highlighted creates it into one let's call this um bike sales uh I

one let's call this um bike sales uh I have I think I called it bike sales

have I think I called it bike sales dashboard let's just call it that um you

dashboard let's just call it that um you know see what happens let's get that

know see what happens let's get that let's make it white and and make it much

let's make it white and and make it much larger than it

larger than it is okay okay

is okay okay um sure let's do that doesn't look bad

um sure let's do that doesn't look bad um what is it doing there we go uh let's

um what is it doing there we go uh let's bre that Center perfect um it's not

bre that Center perfect um it's not perfect but we're going to use it all

perfect but we're going to use it all right so now we kind of want to organize

right so now we kind of want to organize these and you know everybody has their

these and you know everybody has their different way of doing it uh I'm just

different way of doing it uh I'm just going to start building it out myself

going to start building it out myself self and just see how it

self and just see how it looks uh and then we'll go from there I

looks uh and then we'll go from there I like this one there um we can

like this one there um we can put this one I I this one's a kind of a

put this one I I this one's a kind of a longer one so I'll probably put it at

longer one so I'll probably put it at the bottom let's see how it

the bottom let's see how it looks um but we'll put this one right

looks um but we'll put this one right here try to line it up geez let's let's

here try to line it up geez let's let's zoom in a little bit let's try to line

zoom in a little bit let's try to line this up see what it looks

like let's extend it to the end that doesn't look too bad uh needs

end that doesn't look too bad uh needs to move up just a hair and I'll show you

to move up just a hair and I'll show you how to kind of align these in a second

how to kind of align these in a second but um that looks not bad and we'll kind

but um that looks not bad and we'll kind of try to align these as well let me

of try to align these as well let me zoom out and extend this the length of

zoom out and extend this the length of this just to make it look nice um you

this just to make it look nice um you know now what you can do and you know

know now what you can do and you know this is something that's pretty simple

this is something that's pretty simple is you can get both of these and we're

is you can get both of these and we're going to go to shape format and we can

going to go to shape format and we can just align these it's really nice to

just align these it's really nice to align especially if like the top and

align especially if like the top and maybe like the left to right but like

maybe like the left to right but like we're going to align these to the top

we're going to align these to the top and they just kind of align themselves

and they just kind of align themselves on the very top now these look much

on the very top now these look much better this one is a larger dashboard or

better this one is a larger dashboard or a larger visualization so I'm going to

a larger visualization so I'm going to keep it how it is um and I'm going to

keep it how it is um and I'm going to keep this one how it is so it is going

keep this one how it is so it is going to be a little bit smaller as you can

to be a little bit smaller as you can tell and then we'll have this one um and

tell and then we'll have this one um and I'm going to do that

I'm going to do that um I this is going to bother me if I

um I this is going to bother me if I don't align these so let me do this I'm

don't align these so let me do this I'm shape format align to the

shape format align to the right and it's not exactly what I wanted

right and it's not exactly what I wanted to happen

because oh jeez what am I doing that's not exactly what I wanted to happen I

not exactly what I wanted to happen I actually wanted this one to align uh

actually wanted this one to align uh this one to align with this one it did

this one to align with this one it did the opposite um so let me just scoot

the opposite um so let me just scoot this back all right visually looks fine

this back all right visually looks fine but that's how you do it if you want to

but that's how you do it if you want to do it um I I I if you have multiple of

do it um I I I if you have multiple of them like this it you can make it look

them like this it you can make it look bad so we have our dashboards this is

bad so we have our dashboards this is already looking really good I I like how

already looking really good I I like how this looks colors are coordinated it we

this looks colors are coordinated it we have a kind of a theme throughout um and

have a kind of a theme throughout um and it looks nice I actually I actually kind

it looks nice I actually I actually kind of want to change this one um

of want to change this one um to

to um let's

see maybe if I did like that it look nicer than all of them yeah this does

nicer than all of them yeah this does look nicer um it doesn't change much

look nicer um it doesn't change much either guys I'm should I do it all right

either guys I'm should I do it all right we're going for it we're changing the

we're going for it we're changing the design on the Fly should I do it for all

design on the Fly should I do it for all of

of them let's

them let's see it doesn't fit doesn't fit um all

see it doesn't fit doesn't fit um all right guys just ignore what I'm doing uh

right guys just ignore what I'm doing uh don't do any of this I'm just messing

don't do any of this I'm just messing around at this point so this is really

around at this point so this is really great to have it really is and what we

great to have it really is and what we want to do is there are other elements

want to do is there are other elements there are other things that people would

there are other things that people would like to feel a to filter by and be able

like to feel a to filter by and be able to look at but it's not in this

to look at but it's not in this visualization um to be more specific one

visualization um to be more specific one field that's could be really interesting

field that's could be really interesting is married versus single are single

is married versus single are single people buying more or um married people

people buying more or um married people buying more you know it it'd be nice to

buying more you know it it'd be nice to filter on it so we're going to click on

filter on it so we're going to click on uh any of these actually and we're going

uh any of these actually and we're going to go up to Pivot chart analyze and

to go up to Pivot chart analyze and we'll click insert slicer now we can

we'll click insert slicer now we can choose which ones we want to be able to

choose which ones we want to be able to filter on all at the same time or one at

filter on all at the same time or one at a time I'm just going to do the first

a time I'm just going to do the first one by itself and then I'll show you how

one by itself and then I'll show you how to do other ones um but this one is the

to do other ones um but this one is the marital status so this is the married

marital status so this is the married single the one we were just looking at

single the one we were just looking at and we can drag this right over

and we can drag this right over here bring it in a little

here bring it in a little bit all right and we don't need all that

bit all right and we don't need all that space so we're going to boop boop boop

space so we're going to boop boop boop boop all the way up

boop all the way up now while we're doing this um it only

now while we're doing this um it only because we selected this uh this

because we selected this uh this visualization it only is working on that

visualization it only is working on that one right now we of course wanted to

one right now we of course wanted to apply to all of them is not hard to do

apply to all of them is not hard to do all we're going to do is we're going to

all we're going to do is we're going to click on we're going to make sure we're

click on we're going to make sure we're clicking on this we're going to go up to

clicking on this we're going to go up to slicer we're going to hit report

slicer we're going to hit report connections um and if you remember we

connections um and if you remember we have this um this pivot table that we're

have this um this pivot table that we're working with um and this is where all of

working with um and this is where all of our pivots are coming from so we're

our pivots are coming from so we're going to actually apply it to all of

going to actually apply it to all of them this is our sheet U and this is the

them this is our sheet U and this is the name of the pivot table now again we

name of the pivot table now again we created that fourth one we're not using

created that fourth one we're not using it but we're going to apply it to all of

it but we're going to apply it to all of them so now when we click on it it's

them so now when we click on it it's going to apply to all of them so at a

going to apply to all of them so at a quick glance let's see what single

quick glance let's see what single people are doing

people are doing um interesting interesting um you know

um interesting interesting um you know when I'm looking at the just these

when I'm looking at the just these numbers right here married people these

numbers right here married people these individuals are making a lot more like

individuals are making a lot more like eight um sometimes eight to like 10,000

eight um sometimes eight to like 10,000 more on average than their single

more on average than their single counterpart um you know again that's a

counterpart um you know again that's a rough estimate but it's it's interesting

rough estimate but it's it's interesting so now what we can do is we're going to

so now what we can do is we're going to create more of these so we're going to

create more of these so we're going to go to uh pivot chart analyze we're going

go to uh pivot chart analyze we're going to go to slicer now we already did

to go to slicer now we already did marital status but what if we want to

marital status but what if we want to look at things like uh region and maybe

look at things like uh region and maybe something like their education so let's

something like their education so let's bring up both of those and look now two

bring up both of those and look now two of them come up so let's add the region

of them come up so let's add the region right here

right here we'll bring that in just a little bit

we'll bring that in just a little bit see if we can match it nailed it all

see if we can match it nailed it all right now we're going to put that up

right now we're going to put that up we'll bring this one

we'll bring this one down just like this bring it over see if

down just like this bring it over see if I can match it again come

I can match it again come on N almost nailed it I don't know if I

on N almost nailed it I don't know if I nailed it but it's close all right kind

nailed it but it's close all right kind of bring this up a little bit bring this

of bring this up a little bit bring this up and we have to do the exact same

up and we have to do the exact same thing that we did with this one because

thing that we did with this one because right now again it only applies to that

right now again it only applies to that one um chart so what we want to do is we

one um chart so what we want to do is we want to go to slicer report connections

want to go to slicer report connections add it to all of them okay do the same

add it to all of them okay do the same thing with education or connections bada

thing with education or connections bada bing bada boom We are looking good and

bing bada boom We are looking good and now uh let's get rid of all of them it's

now uh let's get rid of all of them it's just going to be everybody so now we can

just going to be everybody so now we can kind of slice and dice and choose what

kind of slice and dice and choose what we want we want to look at people who

we want we want to look at people who have a bachelor's degree who live in

have a bachelor's degree who live in Europe and are single

Europe and are single and this is the information that we have

and this is the information that we have on those people so now we can narrow it

on those people so now we can narrow it down by certain demographics even

down by certain demographics even further and look at this key information

further and look at this key information so we may not you know look at counts

so we may not you know look at counts and averages of these things but we're

and averages of these things but we're able to filter on them uh and that's

able to filter on them uh and that's really great to know so bachelor's

really great to know so bachelor's degrees on average are making 60s 70,000

degrees on average are making 60s 70,000 um let's look at um let's look at

um let's look at um let's look at graduate

graduate degrees okay a little

degrees okay a little more um but you know again I'm just

more um but you know again I'm just looking at random stuff um but you can

looking at random stuff um but you can mess around with this take a look at

mess around with this take a look at some stuff um this to me I want to make

some stuff um this to me I want to make this color darker I feel like it look

this color darker I feel like it look nicer darker there we go oh yeah that's

nicer darker there we go oh yeah that's way better this to me is it's a good

way better this to me is it's a good dashboard right you have key information

dashboard right you have key information that you're looking at nice

that you're looking at nice visualizations it's color coordinated

visualizations it's color coordinated you have these slicers on the side um to

you have these slicers on the side um to me this is a fantas fantastic just

me this is a fantas fantastic just simple dashboard and there are so many

simple dashboard and there are so many other things that you can do with this

other things that you can do with this data and you can make it unique and you

data and you can make it unique and you can add your own spin on it and I highly

can add your own spin on it and I highly recommend that you do that push yourself

recommend that you do that push yourself go past what we just did today and add

go past what we just did today and add your own stuff and and use this and then

your own stuff and and use this and then you can add this to your portfolio

you can add this to your portfolio website and show this off and show

website and show this off and show people that you know how to use Excel

people that you know how to use Excel which is a fantastic thing to know how

which is a fantastic thing to know how to use and show off so with that being

to use and show off so with that being said I hope that this project was

said I hope that this project was helpful I hope that you learned

helpful I hope that you learned something along the way I know I did um

something along the way I know I did um I was learning things as we were going

I was learning things as we were going and I hope that you didn't mind that I

and I hope that you didn't mind that I took some detours along the way um for

took some detours along the way um for your amusement as well as my learning uh

your amusement as well as my learning uh so with that being said thank you so

so with that being said thank you so much for joining me I really appreciate

much for joining me I really appreciate it I hope you have a good day and

it I hope you have a good day and [Music]

[Music] goodbye what's going on everybody

goodbye what's going on everybody welcome back to another video today we

welcome back to another video today we are starting our Tableau tutorial

[Music] series now this series is for absolute

series now this series is for absolute beginners so if you have never used TBL

beginners so if you have never used TBL blow before you are in the perfect place

blow before you are in the perfect place I'm going to take you all the way from

I'm going to take you all the way from the very beginning of installing it and

the very beginning of installing it and just understanding what Tableau is and

just understanding what Tableau is and how you can use it all the way to

how you can use it all the way to creating dashboards and sharing it now

creating dashboards and sharing it now personally I hate those videos that are

personally I hate those videos that are like 3 hours long and they just expect

like 3 hours long and they just expect you to go through it uh i' like to break

you to go through it uh i' like to break my videos up in chunk so if you have

my videos up in chunk so if you have ever done my sequel tutorials you'll

ever done my sequel tutorials you'll know that I like to break things up so

know that I like to break things up so it gives you time to try them out and do

it gives you time to try them out and do them yourself and then you can move on

them yourself and then you can move on to the next video so I'm going to be

to the next video so I'm going to be breaking this up into five separate

breaking this up into five separate videos but in this video I'm going to

videos but in this video I'm going to show you how to install Tableau for free

show you how to install Tableau for free I'm going to show you the user interface

I'm going to show you the user interface we're going to download a data set that

we're going to download a data set that you can find on kagle and then we will

you can find on kagle and then we will build our first visualization together

build our first visualization together with that being said let's jump over my

with that being said let's jump over my screen and we'll get started all right

screen and we'll get started all right so the very first thing that we need to

so the very first thing that we need to do is you need to actually download

do is you need to actually download Tableau so we're not going to be using

Tableau so we're not going to be using Tableau we're going to be using a free

Tableau we're going to be using a free version called Tableau public it has a

version called Tableau public it has a lot of the same features except of

lot of the same features except of course it's not uh every single feature

course it's not uh every single feature that regular Tableau has but it is

that regular Tableau has but it is absolutely perfect for learning it and

absolutely perfect for learning it and for using it and and you can even build

for using it and and you can even build um you know dashboards and share those

um you know dashboards and share those for your

for your portfolio um I'm going to put this link

portfolio um I'm going to put this link in the description so you can just go

in the description so you can just go and click on that and and all you have

and click on that and and all you have to do is input your email right here

to do is input your email right here we're going click download the app um

we're going click download the app um and then it should start to download and

and then it should start to download and then you can save that and then you're

then you can save that and then you're going to open this up now I'm going to

going to open this up now I'm going to open it up I don't know what it's going

open it up I don't know what it's going to do I already have it downloaded um

to do I already have it downloaded um but it should open up and look hopefully

but it should open up and look hopefully like what you're seeing on my screen in

like what you're seeing on my screen in just a second let see what it does um I

just a second let see what it does um I hope you can see this but it says

hope you can see this but it says Tableau public um it says I already have

Tableau public um it says I already have it set up but you're going to click

it set up but you're going to click install and go through all that um all

install and go through all that um all that setup stuff uh so I'm going to exit

that setup stuff uh so I'm going to exit out of here but I'm going to go over

out of here but I'm going to go over here and type in table of public uh and

here and type in table of public uh and it's 20 21.3 that's the current version

it's 20 21.3 that's the current version that they have out if you're doing this

that they have out if you're doing this in the future they may have you know

in the future they may have you know different versions um so you should be

different versions um so you should be able to pull this up right here now um

able to pull this up right here now um I'm going to go and get our data set

I'm going to go and get our data set that we're going to be using and I'm

that we're going to be using and I'm going to show you how to get that as

going to show you how to get that as well and then we will actually jump into

well and then we will actually jump into Tableau and start uh using it so let's

Tableau and start uh using it so let's go over here I'm going to get a data set

go over here I'm going to get a data set from kagle I wanted something pretty

from kagle I wanted something pretty generic uh to show you in future videos

generic uh to show you in future videos I'm going to show you some special or

I'm going to show you some special or not special but just different

not special but just different visualizations that you might use um and

visualizations that you might use um and we'll get different data sets for those

we'll get different data sets for those because of course not one data set

because of course not one data set covers all these other types of

covers all these other types of visualizations so um we're starting off

visualizations so um we're starting off pretty simple right here we're going to

pretty simple right here we're going to be getting one called video game sales

be getting one called video game sales um and we can take a really quick look

um and we can take a really quick look at it um here are some of the fields

at it um here are some of the fields that you're going to be having uh like

that you're going to be having uh like rank name platform the year genre and

rank name platform the year genre and then some sales data and this is what it

then some sales data and this is what it actually looks like it's called VG sales

actually looks like it's called VG sales so video game sales it's then a

so video game sales it's then a CSV and um you know here are the fields

CSV and um you know here are the fields and we have our data and all we are

and we have our data and all we are going to do is we're going to download

going to do is we're going to download that and I will save it now when you

that and I will save it now when you download it it's going to be saved into

download it it's going to be saved into a zip file so we need to go to our

a zip file so we need to go to our downloads uh let's refresh this here's

downloads uh let's refresh this here's our archive we need to go in here you

our archive we need to go in here you can just copy it and paste paste it

can just copy it and paste paste it right back into here um and just so you

right back into here um and just so you know that is a uh a CSV so be aware of

know that is a uh a CSV so be aware of that so what we want to do is we want to

that so what we want to do is we want to come in here now since it is a CSV this

come in here now since it is a CSV this is not we're not going to be using

is not we're not going to be using Microsoft Excel we're going to be using

Microsoft Excel we're going to be using the text file so we'll come in here

the text file so we'll come in here we'll take VG sales now uh one thing I

we'll take VG sales now uh one thing I want to do before I do that is I'm going

want to do before I do that is I'm going to rename mine uh VGC

to rename mine uh VGC sales1 um I've already prepared for this

sales1 um I've already prepared for this and so I already have that in there um

and so I already have that in there um but so I want to make a distinct one for

but so I want to make a distinct one for myself you do not have to do that so

myself you do not have to do that so we'll come back here um and then we're

we'll come back here um and then we're going to do text file and VG sales we're

going to do text file and VG sales we're going to open that

going to open that up

up and when it pulls up right here um you

and when it pulls up right here um you can bring in other tables and then you

can bring in other tables and then you can start to join them together and

can start to join them together and create those relationships we are not

create those relationships we are not going to be doing that in this video

going to be doing that in this video we'll do that in a separate one um as

we'll do that in a separate one um as for you know just getting started you

for you know just getting started you know we're not going to be using that

know we're not going to be using that but you can see some of these things or

but you can see some of these things or some of these fields and if you notice

some of these fields and if you notice they they um they're either ABC or

they they um they're either ABC or they're a number so it starts to

they're a number so it starts to categorize what this field type is so is

categorize what this field type is so is it a string is it numeric it starts to

it a string is it numeric it starts to automatically do that and that's all

automatically do that and that's all done within

done within Tableau and so it just kind of reads it

Tableau and so it just kind of reads it and that's what it does um what we going

and that's what it does um what we going to do is I'm going to click right down

to do is I'm going to click right down here it's called go to worksheet um the

here it's called go to worksheet um the worksheets are where you're going to

worksheets are where you're going to actually start being able to build your

actually start being able to build your visualizations your charts your graphs

visualizations your charts your graphs all these things um and so you know we

all these things um and so you know we have this in here now and so we're just

have this in here now and so we're just going to click right here on go to

going to click right here on go to worksheet as you can see here is VG

worksheet as you can see here is VG sales1 you will not have the underscore

sales1 you will not have the underscore one if you did not add that like I did

one if you did not add that like I did uh but right down here you can see all

uh but right down here you can see all the fields that we just imported from

the fields that we just imported from that data set and they even created one

that data set and they even created one right here for us uh they just generated

right here for us uh they just generated that field u based on the file so it's a

that field u based on the file so it's a count of all the rows really so what I'm

count of all the rows really so what I'm going to do is I'm just going to walk

going to do is I'm just going to walk you through uh basically what we're

you through uh basically what we're looking at some of the things that we're

looking at some of the things that we're going to be using today there will be

going to be using today there will be things that I don't talk about but I'm

things that I don't talk about but I'm going to highlight those in in in future

going to highlight those in in in future videos when we start using those or

videos when we start using those or going over them um and so let's just

going over them um and so let's just start with the most obvious one it's way

start with the most obvious one it's way over here I'm sure you saw it when we uh

over here I'm sure you saw it when we uh this first came up on the screen because

this first came up on the screen because it has all these different charts and

it has all these different charts and visualizations and graphs and uh these

visualizations and graphs and uh these will become available as you start

will become available as you start dragging and dropping our data into this

dragging and dropping our data into this sheet and so if I go right here it says

sheet and so if I go right here it says for Scatter Plots try zero or more

for Scatter Plots try zero or more Dimensions two to four measures so what

Dimensions two to four measures so what our dimensions are are right here what

our dimensions are are right here what our measures are are right down here and

our measures are are right down here and so typically uh things like like you say

so typically uh things like like you say genre or names or or strings like that

genre or names or or strings like that are going to be these uh dimensions and

are going to be these uh dimensions and then a lot of lot of times the numerical

then a lot of lot of times the numerical is going to be our going to be measures

is going to be our going to be measures next what I want to show you is right

next what I want to show you is right here so you can take something like

here so you can take something like Global sales and you can drag it right

Global sales and you can drag it right here into your rows and then it takes

here into your rows and then it takes your rows and so it automatically

your rows and so it automatically created a sum of global sales now if we

created a sum of global sales now if we take that away and let's say we drag it

take that away and let's say we drag it right here it's going to give us a

right here it's going to give us a column

column now you can also do it right up here you

now you can also do it right up here you don't have to um drag it on screen you

don't have to um drag it on screen you can

can also just add it to the column or the

also just add it to the column or the row that's typically what I do I it's

row that's typically what I do I it's just more intuitive to me um or you can

just more intuitive to me um or you can drop it in this section right here and

drop it in this section right here and it does its best to assign it some type

it does its best to assign it some type of um some type of visualization and so

of um some type of visualization and so that's what it always is trying to do it

that's what it always is trying to do it is trying to say okay this is what

is trying to say okay this is what you're trying to do let me try to to get

you're trying to do let me try to to get the best visualization for the data that

the best visualization for the data that you're giving me now while we are here

you're giving me now while we are here um it went down here into marks and

um it went down here into marks and marks is a very important area it's

marks is a very important area it's where you can add color size text detail

where you can add color size text detail and Tool tip and I'm not going to go

and Tool tip and I'm not going to go into what all those are cuz I'm just

into what all those are cuz I'm just going to show you so let's start pulling

going to show you so let's start pulling some fields in here and creating a

some fields in here and creating a visualization and then I'm going to show

visualization and then I'm going to show you how all of that works including

you how all of that works including filters as well so the first thing that

filters as well so the first thing that we are going to look at is global save

we are going to look at is global save and let's put that in the rows and then

and let's put that in the rows and then I'm going to take year and I'm going to

I'm going to take year and I'm going to make that the column and this is

make that the column and this is basically exactly what uh I wanted to do

basically exactly what uh I wanted to do now as of right now it has only the year

now as of right now it has only the year and it's looking at Global sales for

and it's looking at Global sales for everything but we want to break that out

everything but we want to break that out a little bit better I Want to Break It

a little bit better I Want to Break It Out by let's do genre so different genre

Out by let's do genre so different genre of games now if I add that right here to

of games now if I add that right here to this column s it is going to break it up

this column s it is going to break it up by year and genre if I add it right here

by year and genre if I add it right here is going to break it out by the year of

is going to break it out by the year of course but then in each individual row

course but then in each individual row has the different genre that's not what

has the different genre that's not what we want we want to keep this type of

we want we want to keep this type of line graph uh and what we're going to do

line graph uh and what we're going to do is we're going to add it to

is we're going to add it to Marks and you can't really see it based

Marks and you can't really see it based off of these colors but they're all

off of these colors but they're all different so we have action J genre we

different so we have action J genre we have the sports genre racing uh role

have the sports genre racing uh role playing all these different genres

playing all these different genres within it now we can get rid of that cuz

within it now we can get rid of that cuz we don't need it

we don't need it anymore uh and this is where these U

anymore uh and this is where these U these marks really come in handy because

these marks really come in handy because you can start basically doing what you

you can start basically doing what you want with them so for the genre I want

want with them so for the genre I want to be able to see all these different

to be able to see all these different genres with different colors to me that

genres with different colors to me that just makes the most sense so I'm going

just makes the most sense so I'm going to put color right here and

to put color right here and automatically it assigns every single

automatically it assigns every single genre its own own color and gives us

genre its own own color and gives us this Legend right over here and so it's

this Legend right over here and so it's really easy to see well when you have

really easy to see well when you have smaller numbers is much easier but I

smaller numbers is much easier but I know that red is sports and I can go

know that red is sports and I can go right here and find red and that is

right here and find red and that is sports so it makes it a lot easier than

sports so it makes it a lot easier than when it is all the same color blue so

when it is all the same color blue so what you can do after that is you can

what you can do after that is you can also add things like uh a label to it so

also add things like uh a label to it so if we take label and we or we take genre

if we take label and we or we take genre put label you can click right here and

put label you can click right here and you can get rid of the labels that you

you can get rid of the labels that you have and you can see them right down

have and you can see them right down here or you can also change uh the font

here or you can also change uh the font so if you want to make it orange or or

so if you want to make it orange or or whatever color you can do all those same

whatever color you can do all those same things and you can also do things like

things and you can also do things like changing where you see these things so

changing where you see these things so for Action you're going to see it a ton

for Action you're going to see it a ton because for each year action is is at

because for each year action is is at the is on the higher end and so you're

the is on the higher end and so you're seeing those in those mins and Maxes you

seeing those in those mins and Maxes you can also do it for a selected area so if

can also do it for a selected area so if I come in here and I select it it's then

I come in here and I select it it's then going to show me what those are so label

going to show me what those are so label is really really uh useful really

is really really uh useful really helpful let me get rid of that really

helpful let me get rid of that really quick uh you can also do it where the

quick uh you can also do it where the lines end so line ends is at the

lines end so line ends is at the beginning and the end and you can also

beginning and the end and you can also take that away or put that back on so

take that away or put that back on so labels are really important labels

labels are really important labels aren't very helpful when you're doing at

aren't very helpful when you're doing at least I don't find that it's super

least I don't find that it's super helpful when you're doing things like

helpful when you're doing things like genre so when you're doing your

genre so when you're doing your Dimensions so I'm going to get rid of

Dimensions so I'm going to get rid of that and I'm actually going to bring

that and I'm actually going to bring our Global sales over here and let's

our Global sales over here and let's label

label that and right now I think it's labeling

that and right now I think it's labeling the uh line ends we want to do the Min

the uh line ends we want to do the Min and Max now if we do Min and Max on the

and Max now if we do Min and Max on the table it's just going to give us the Max

table it's just going to give us the Max and the men which is zero and then

and the men which is zero and then 139.0 it's a little bit more useful if

139.0 it's a little bit more useful if we do it for each line uh this at least

we do it for each line uh this at least gives us some context I probably

gives us some context I probably wouldn't do this in an actual visual

wouldn't do this in an actual visual visualization but to give you some um

visualization but to give you some um understanding just how it works so now I

understanding just how it works so now I know that um right over here the men and

know that um right over here the men and the max or the men sorry the max for

the max or the men sorry the max for these for action and for sports is right

these for action and for sports is right around 138 139 so it's pretty easy to

around 138 139 so it's pretty easy to see um and you can again go in here and

see um and you can again go in here and you can remove the max or remove the

you can remove the max or remove the mins whichever one you feel is best uh

mins whichever one you feel is best uh you'll probably keep the maximums in

you'll probably keep the maximums in there for each category and so this is a

there for each category and so this is a really quickly becoming uh a pretty

really quickly becoming uh a pretty usable visualization and that's not the

usable visualization and that's not the only label that you can add we still are

only label that you can add we still are using year over here so we can always

using year over here so we can always drop year in there as well we'll create

drop year in there as well we'll create a label and so now we have let's see for

a label and so now we have let's see for this one is a puzzle genre so we also

this one is a puzzle genre so we also have the year that it had the maximum uh

have the year that it had the maximum uh sales and so you know just some things

sales and so you know just some things that you can do you don't have to add

that you can do you don't have to add that now let's go up here and we're

that now let's go up here and we're going to take a look at filters because

going to take a look at filters because filters are really important you know if

filters are really important you know if you are making this for a client or you

you are making this for a client or you making this for somebody you want them

making this for somebody you want them to be able to filter down uh to very

to be able to filter down uh to very specific information that they want to

specific information that they want to see so let's take uh the platform lots

see so let's take uh the platform lots of different

of different platforms um as you can see you know PS4

platforms um as you can see you know PS4 Xbox um if you're familiar with these

Xbox um if you're familiar with these we'll click all of these um and we'll

we'll click all of these um and we'll click okay so now this is an option as a

click okay so now this is an option as a filter and all we're going to do is

filter and all we're going to do is we're going to click on this Arrow right

we're going to click on this Arrow right here and we're going to say show

here and we're going to say show filter now right now all of them are

filter now right now all of them are selected so every single one is being

selected so every single one is being taken into account for this

taken into account for this visualization but let's say we come down

visualization but let's say we come down here and we say okay I don't want to see

here and we say okay I don't want to see sales for any of these PS the original

sales for any of these PS the original PlayStation 2 three or four so I'm going

PlayStation 2 three or four so I'm going to get rid of this one this one this one

to get rid of this one this one this one and this one and you could immediately

and this one and you could immediately see the the changes that were happening

see the the changes that were happening so now none of the numbers none of those

so now none of the numbers none of those sales are being accounted for and and

sales are being accounted for and and being added to the sum of global sales

being added to the sum of global sales right here at

right here at all so that is just how a filter uh can

all so that is just how a filter uh can work and you can also do that and you

work and you can also do that and you can get rid of all of them and you can

can get rid of all of them and you can go in and actually just pick very

go in and actually just pick very specific sales so if you only want to

specific sales so if you only want to see the PlayStation sales you can go in

see the PlayStation sales you can go in there and do that as well so really

there and do that as well so really really handy filter are things that you

really handy filter are things that you at least want to have as an option for

at least want to have as an option for most of your your visualizations at

most of your your visualizations at least that's what I found especially

least that's what I found especially when you're doing client facing work

when you're doing client facing work they like to uh get in there and mess

they like to uh get in there and mess around and look at different look at it

around and look at different look at it in different ways and so that's one that

in different ways and so that's one that I I think is is really useful to to

I I think is is really useful to to have the very last thing that we want to

have the very last thing that we want to do is we want to actually add this to a

do is we want to actually add this to a dashboard now let's say we add come

dashboard now let's say we add come right down down here and we add a new

right down down here and we add a new worksheet and actually we might change

worksheet and actually we might change one more thing on that last one but

one more thing on that last one but we'll just make a really simple one um

we'll just make a really simple one um we'll just give it genre and we'll give

we'll just give it genre and we'll give it Global sales as the

it Global sales as the rows um and this Nifty button right up

rows um and this Nifty button right up here which is a sorting button so I'm

here which is a sorting button so I'm going to sort like that I'm going to add

going to sort like that I'm going to add the genre in just as we did I'll give it

the genre in just as we did I'll give it different colors perfect now we have two

different colors perfect now we have two really quick different visualizations

really quick different visualizations right what I want to do is just show you

right what I want to do is just show you how to combine those because what you

how to combine those because what you are going to do is you're going to

are going to do is you're going to actually come in here and you're going

actually come in here and you're going to do new dashboard that's what this

to do new dashboard that's what this button is right here now when we come in

button is right here now when we come in here the size is extremely small it's

here the size is extremely small it's very easy to fix that all we're going to

very easy to fix that all we're going to do is Click right here we're going to go

do is Click right here we're going to go to this range or this dropdown and we're

to this range or this dropdown and we're going to click automatic so now it is a

going to click automatic so now it is a much larger size for us to actually drop

much larger size for us to actually drop our visualizations

our visualizations into uh and let's put sheet sheet one

into uh and let's put sheet sheet one and we'll put uh let's put it up top so

and we'll put uh let's put it up top so now it looks a little bit like this uh

now it looks a little bit like this uh not perfect but again if I wanted to

not perfect but again if I wanted to make this look a lot better I definitely

make this look a lot better I definitely would and then you can go over here and

would and then you can go over here and you can rename these things you can also

you can rename these things you can also do that back when we were in our actual

do that back when we were in our actual worksheets but you can also do it here

worksheets but you can also do it here as well and then start um you know

as well and then start um you know customizing it and building it out

customizing it and building it out that's not what this video is for that

that's not what this video is for that is the last video we're going to build

is the last video we're going to build an entire dashboard it'll be kind of

an entire dashboard it'll be kind of like a small project you put that in

like a small project you put that in your portfolio um if you have gotten

your portfolio um if you have gotten this far and you want to jump straight

this far and you want to jump straight into it and you don't want to wait for

into it and you don't want to wait for these other videos to come out or you

these other videos to come out or you don't you just want to jump straight

don't you just want to jump straight into creating an entire portfolio

into creating an entire portfolio project I have an entire portfolio

project I have an entire portfolio project series that covers SQL Python

project series that covers SQL Python and Tableau and so go check out that

and Tableau and so go check out that series I have one video dedicated to

series I have one video dedicated to Tableau it's like 45 minutes or an hour

Tableau it's like 45 minutes or an hour long and it covers a lot of the things

long and it covers a lot of the things that we're going to hear in here as well

that we're going to hear in here as well as a few other things but I appreciate

as a few other things but I appreciate you checking out this video in future

you checking out this video in future videos we're going be going over things

videos we're going be going over things like creating bins calculated Fields

like creating bins calculated Fields doing joins and then creating a final

doing joins and then creating a final project and putting it all together so

project and putting it all together so thank you so much for joining me I

thank you so much for joining me I really appreciate it if you like this

really appreciate it if you like this video be sure to like And subscribe

video be sure to like And subscribe below and I will see you in the next

below and I will see you in the next [Music]

[Music] video

video [Music]

what's going on everybody welcome back to the Tableau tutorial Series in this

to the Tableau tutorial Series in this video we're going to be going over bins

video we're going to be going over bins and calculated

[Music] Fields all right so let's jump right

Fields all right so let's jump right into it the first thing that we're going

into it the first thing that we're going to look at are bins and bins are

to look at are bins and bins are basically just groupings or ranges of

basically just groupings or ranges of numerical values so we cannot create

numerical values so we cannot create bins uh for genre name platform or

bins uh for genre name platform or anything like that we have to do

anything like that we have to do something with this sign right here

something with this sign right here which means that it is a numeric so year

which means that it is a numeric so year or all this sales data or this ranking

or all this sales data or this ranking data and we're going to use what we

data and we're going to use what we worked on in our very first tutorial and

worked on in our very first tutorial and so what we're going to be using to kind

so what we're going to be using to kind of demonstrate how bins work is this

of demonstrate how bins work is this year right down here so right now we

year right down here so right now we have a range of 1993 all the way up to

have a range of 1993 all the way up to 2018 and we're going to create some bins

2018 and we're going to create some bins to group and create ranges for these

to group and create ranges for these years and it's pretty simple all we're

years and it's pretty simple all we're going to do is I'm going to come right

going to do is I'm going to come right over here to year and this little drop

over here to year and this little drop down on the side and we're going to go

down on the side and we're going to go down to create and go down to

down to create and go down to bins now it's going to say the size of

bins now it's going to say the size of Bin and it's going to give you a

Bin and it's going to give you a recommendation based off of the

recommendation based off of the information that is already provided the

information that is already provided the Min and the max the ranges of these

Min and the max the ranges of these values you know you don't have to do

values you know you don't have to do this but usually um it it does give some

this but usually um it it does give some good estimation on what you might be

good estimation on what you might be considering if you were thinking hey

considering if you were thinking hey maybe do a bit of like 20 and they're

maybe do a bit of like 20 and they're recommending two think about why they

recommending two think about why they might be doing that we're going to

might be doing that we're going to change ours to five and you can always

change ours to five and you can always change what this field is going to be

change what this field is going to be I'm just going to give it an old

I'm just going to give it an old exclamation point just to um really

exclamation point just to um really spice things up here so we're going to

spice things up here so we're going to click okay and as you can see it adds it

click okay and as you can see it adds it right up here is no longer um it is no

right up here is no longer um it is no longer a numeric now it is a categorical

longer a numeric now it is a categorical so it now it's this is no longer just uh

so it now it's this is no longer just uh 1 2 3 4 five its ranges its groups and

1 2 3 4 five its ranges its groups and we're going to get rid of this year

we're going to get rid of this year really quick actually let's keep it up

really quick actually let's keep it up there for a second uh see what happens

there for a second uh see what happens but we're going to bring this up and

but we're going to bring this up and we'll get rid of this year and this is

we'll get rid of this year and this is is what kind of it spits out for us now

is what kind of it spits out for us now I did look at the data um when I was

I did look at the data um when I was prepping for this there are some nulls

prepping for this there are some nulls in the Years um and so all we're going

in the Years um and so all we're going to do for this is we're just going to go

to do for this is we're just going to go like this and we're going to exclude the

like this and we're going to exclude the nulls uh probably not something you

nulls uh probably not something you should be doing uh if you're doing this

should be doing uh if you're doing this for work but this is for demonstration

for work but this is for demonstration purposes so we can do it ever we want

purposes so we can do it ever we want but as you can see we now have these

but as you can see we now have these ranges so this range starts at

ranges so this range starts at 1990 and it includes 1990 all the way up

1990 and it includes 1990 all the way up to 1994 and then it's 1995 to

to 1994 and then it's 1995 to 1999 and so just really quickly we can

1999 and so just really quickly we can tell that the years 2000 to 2004 were a

tell that the years 2000 to 2004 were a huge huge huge uh season or group of of

huge huge huge uh season or group of of years for game sales so these are the

years for game sales so these are the global sales for for these video games

global sales for for these video games and so it is really helpful it's very

and so it is really helpful it's very useful um you can do this on a lot of

useful um you can do this on a lot of different information we could do this

different information we could do this on the sales data you can do this on age

on the sales data you can do this on age you can do it on years like we did and

you can do it on years like we did and it can be very very useful and so uh

it can be very very useful and so uh really quickly that is how bins work I

really quickly that is how bins work I would say it's pretty straightforward

would say it's pretty straightforward now this is a perfect time to segue into

now this is a perfect time to segue into the next part of the video which is

the next part of the video which is calculated Fields uh right over here on

calculated Fields uh right over here on this left hand side we see that the

this left hand side we see that the global sales which are in millions goes

global sales which are in millions goes all the way up to 900 million and

all the way up to 900 million and created these beautiful bins right down

created these beautiful bins right down here but let's look at Within These from

here but let's look at Within These from 1999 to 2015 let's see which of these

1999 to 2015 let's see which of these has the highest percentage of course

has the highest percentage of course it's going to be this one but we can do

it's going to be this one but we can do something called a quick table

something called a quick table calculation uh we'll create a our own

calculation uh we'll create a our own calculation later I'll show you how to

calculation later I'll show you how to do that but we're going to do a quick

do that but we're going to do a quick table calculation and we're going to do

table calculation and we're going to do the percent of total and so now we have

the percent of total and so now we have these bins and instead of just seeing

these bins and instead of just seeing the total amount of sales that they had

the total amount of sales that they had we see the actual percentages based off

we see the actual percentages based off these year ranges which is really useful

these year ranges which is really useful something that you could absolutely put

something that you could absolutely put uh in some real work that you do for a

uh in some real work that you do for a client now really quick just to show you

client now really quick just to show you something that you can do if you click

something that you can do if you click control and you drag this over here you

control and you drag this over here you can actually save that calculation so we

can actually save that calculation so we can say

can say percentage of global sales and that

percentage of global sales and that actually saves it as uh you know a

actually saves it as uh you know a measure for us so that was a quick

measure for us so that was a quick calculation but let's look how to

calculation but let's look how to actually create a calculated field so if

actually create a calculated field so if we do this right here what is going to

we do this right here what is going to come up is just the global sales and you

come up is just the global sales and you can do a lot of what you would basically

can do a lot of what you would basically do in Excel multiplication division

do in Excel multiplication division subtraction a few other things but we're

subtraction a few other things but we're going to keep it super super simple

going to keep it super super simple today all I'm going to do is I'm going

today all I'm going to do is I'm going to take Global sales and I'm going to

to take Global sales and I'm going to subtract I'm going to do an open bracket

subtract I'm going to do an open bracket and I'm going to say EU sales and it

and I'm going to say EU sales and it auto completes for me I'm going to click

auto completes for me I'm going to click okay and created calculation 2 I'm going

okay and created calculation 2 I'm going to come in here and I'm just going to

to come in here and I'm just going to say Global sales

say Global sales minus EU

minus EU sales and let's drag this over these are

sales and let's drag this over these are different um one's percentage one is in

different um one's percentage one is in terms of sum and so I'm just going to

terms of sum and so I'm just going to bring this in right here and so now we

bring this in right here and so now we are comparing against the same thing and

are comparing against the same thing and if we look at the global sales we have

if we look at the global sales we have probably right around 9 50 million-ish

probably right around 9 50 million-ish in this 2000 to 2004 bin and for Global

in this 2000 to 2004 bin and for Global sales minus the EU sales we're looking

sales minus the EU sales we're looking at you know 650 million so there is a

at you know 650 million so there is a noticeable difference and this is just

noticeable difference and this is just one of the ways that you can use

one of the ways that you can use calculated fields to actually just show

calculated fields to actually just show the difference between two numbers or

the difference between two numbers or you can do more advanced calculations

you can do more advanced calculations depending on the data that you actually

depending on the data that you actually have so that's it for this video I hope

have so that's it for this video I hope you learned a little bit more about bins

you learned a little bit more about bins and calculated fields in the next video

and calculated fields in the next video we're going to looking at a ton of

we're going to looking at a ton of different visualizations and graphs and

different visualizations and graphs and charts and just exploring what options

charts and just exploring what options really are out there for visualizing our

really are out there for visualizing our data thank you guys so much for joining

data thank you guys so much for joining me I really appreciate it if you like

me I really appreciate it if you like this video be sure to like And subscribe

this video be sure to like And subscribe below and I will see you in the next

below and I will see you in the next [Music]

[Music] video what's going on everybody welcome

video what's going on everybody welcome back to the Tableau tutorial Series in

back to the Tableau tutorial Series in this video we're going to be looking at

this video we're going to be looking at lots of different visualizations

lots of different visualizations including the scatter plot and density

[Music] Maps now before we jump into the

Maps now before we jump into the tutorial I have some very exciting news

tutorial I have some very exciting news in just two days on October 7th I going

in just two days on October 7th I going to be partnering with alter X to host a

to be partnering with alter X to host a webinar this webinar is completely for

webinar this webinar is completely for data analysts who are wanting to change

data analysts who are wanting to change careers to become a data analyst now you

careers to become a data analyst now you did hear that right I will be the host

did hear that right I will be the host of the event but but we will be bringing

of the event but but we will be bringing on guests as well who are industry

on guests as well who are industry experts who actually change careers to

experts who actually change careers to become data analyst much like myself

become data analyst much like myself they'll be sharing their stories of how

they'll be sharing their stories of how they actually transition careers along

they actually transition careers along with the tools that they found extremely

with the tools that they found extremely useful and helpful to make that switch

useful and helpful to make that switch and they'll be giving lots of advice

and they'll be giving lots of advice along the way so if you are somebody who

along the way so if you are somebody who is wanting to change careers to become a

is wanting to change careers to become a data analyst or just wanting to learn

data analyst or just wanting to learn about data analytics this is an absolute

about data analytics this is an absolute fantastic place to learn a lot more

fantastic place to learn a lot more about that I will leave a link in the

about that I will leave a link in the description so be sure to go and sign up

description so be sure to go and sign up for that again I'm going to be there so

for that again I'm going to be there so so it should be really fun without

so it should be really fun without further Ado let's jump onto my screen

further Ado let's jump onto my screen and start the tutorial now we are about

and start the tutorial now we are about to look at a ton of different

to look at a ton of different visualizations uh over here you can see

visualizations uh over here you can see just an array of them but not all of

just an array of them but not all of them are ones that I actually think are

them are ones that I actually think are useful or ones that I would actually

useful or ones that I would actually recommend using and so I'm going to take

recommend using and so I'm going to take you through some of the ones that I

you through some of the ones that I absolutely think are worth learning and

absolutely think are worth learning and using and trying out uh and I'm just

using and trying out uh and I'm just going to kind of just show you how I

going to kind of just show you how I might use them how they might look how

might use them how they might look how you can navigate them a little bit now

you can navigate them a little bit now before we do that we do need to go

before we do that we do need to go download one data set it's this

download one data set it's this Starbucks location worldwide yes we're

Starbucks location worldwide yes we're going to do a little bit of longitude

going to do a little bit of longitude latitude here and all we have to do is

latitude here and all we have to do is click this downloads button and it will

click this downloads button and it will download we're going to do that into

download we're going to do that into downloads we'll save that uh yeah I've

downloads we'll save that uh yeah I've already done that but you know I'm doing

already done that but you know I'm doing this with you guys I'm doing it for you

this with you guys I'm doing it for you so let's go to our

so let's go to our downloads now we have have here we want

downloads now we have have here we want to come in here we're going to copy it

to come in here we're going to copy it or um you can cut it and then we're

or um you can cut it and then we're going to paste it here yeah replace it

going to paste it here yeah replace it perfect and now we have it ready to go

perfect and now we have it ready to go we'll come in here let's do a new sheet

we'll come in here let's do a new sheet and I already have it in there but uh

and I already have it in there but uh I'm just going to show you what I would

I'm just going to show you what I would do do new data source we'll do text file

do do new data source we'll do text file we'll do directory and we will open

it and let's see what data we have in here before we actually begin uh just

here before we actually begin uh just super quickly we have the brand so um

super quickly we have the brand so um whatever company has it and then a bunch

whatever company has it and then a bunch of um location information street

of um location information street address City the state this is all in

address City the state this is all in the United States so that's basically it

the United States so that's basically it and what we are going to do is we're

and what we are going to do is we're going to go over to this sheet

going to go over to this sheet three and we have this directory 2

three and we have this directory 2 that's the one I just pulled in exact

that's the one I just pulled in exact same thing as directory but so the first

same thing as directory but so the first VIs visualization that we are going to

VIs visualization that we are going to look at is a bar and line graph so what

look at is a bar and line graph so what we're going to take is the year right

we're going to take is the year right here take these Global sales and these

here take these Global sales and these na

na sales and we're going to be doing this

sales and we're going to be doing this one right here so this has a combination

one right here so this has a combination of two separate uh types of

of two separate uh types of visualizations so sometimes you just

visualizations so sometimes you just have lines sometimes you just have these

have lines sometimes you just have these uh these bar graphs or the bar charts

uh these bar graphs or the bar charts and we're combining the two and it's

and we're combining the two and it's very nice I like how this looks now if

very nice I like how this looks now if you notice if I put this na sales behind

you notice if I put this na sales behind it now it kind of cuts off so now this

it now it kind of cuts off so now this Global sales is in front we're going to

Global sales is in front we're going to you know put that back I just wanted to

you know put that back I just wanted to show you that uh right here there's all

show you that uh right here there's all some of global sales some of Na sales so

some of global sales some of Na sales so if we go into this all we click this

if we go into this all we click this drop down we can change it to a line um

drop down we can change it to a line um we can change it basically whatever we

we can change it basically whatever we want I just hit contrl Z to reverse that

want I just hit contrl Z to reverse that but what we can do is we can go in here

but what we can do is we can go in here and we can change this color and let's

and we can change this color and let's see if we can just make it red is that

see if we can just make it red is that [Music]

[Music] possible see what I did I made it orange

possible see what I did I made it orange that works for me um just something to

that works for me um just something to stick out a little bit more choose

stick out a little bit more choose whatever color you want and this is a

whatever color you want and this is a really nice visualization this is one

really nice visualization this is one that I have used in the past we're

that I have used in the past we're looking at Global sales versus the na

looking at Global sales versus the na sales and so it's very easy to see the

sales and so it's very easy to see the distinction between the two and how one

distinction between the two and how one was doing a specific year versus how the

was doing a specific year versus how the other one was doing in that same year so

other one was doing in that same year so I really like this if you want to do

I really like this if you want to do something uh like keeping it consistent

something uh like keeping it consistent you can do two bars I don't really like

you can do two bars I don't really like this one as much um and you can again

this one as much um and you can again you can really change it up um there's

you can really change it up um there's lots of different ones that you can do

lots of different ones that you can do again I prefer the line but you know do

again I prefer the line but you know do whatever you think is best I'm going to

whatever you think is best I'm going to change it back because this is not how I

change it back because this is not how I want to keep it but there you go so that

want to keep it but there you go so that is the first one that we are going to

is the first one that we are going to look at let's move on to the second one

look at let's move on to the second one and we actually will be using our our

and we actually will be using our our Starbucks data here

Starbucks data here now when you bring in data that has um

now when you bring in data that has um any type of map or or um address or

any type of map or or um address or postal code or things like that or or

postal code or things like that or or country it's typically going to create

country it's typically going to create this latitude and longitude it's going

this latitude and longitude it's going to generate that now what we want to do

to generate that now what we want to do is bring this longitude right up here

is bring this longitude right up here and this latitude right

and this latitude right there and if you do the show me right

there and if you do the show me right now it's giving us this but what we want

now it's giving us this but what we want to do is add what we're looking for so

to do is add what we're looking for so what will we actually be trying to

what will we actually be trying to search for on this map you can do

search for on this map you can do anything from like a postal code um and

anything from like a postal code um and it will drag us right here let's come

it will drag us right here let's come over to this this allows us to kind of

over to this this allows us to kind of scroll around a little bit um we're

scroll around a little bit um we're going to mess around with this one for

going to mess around with this one for just a little bit and me see if I

just a little bit and me see if I can that's nice that might be too big

can that's nice that might be too big let me back up one so at least in the

let me back up one so at least in the Continental us a little bit down here

Continental us a little bit down here this these are the postal codes so right

this these are the postal codes so right now we're looking at post codes uh

now we're looking at post codes uh and there are a lot that you can do with

and there are a lot that you can do with this um really color will make almost no

this um really color will make almost no difference it just becomes this mess so

difference it just becomes this mess so you don't typically want to do something

you don't typically want to do something like that at least not for this let's go

like that at least not for this let's go to size and if we make it really small

to size and if we make it really small you can kind of see these groupings

you can kind of see these groupings these pairings um typically of like

these pairings um typically of like larger cities or major major

larger cities or major major metropolitan areas and so you can do

metropolitan areas and so you can do this and it's and it's really really

this and it's and it's really really easy I don't recommend uh labeling this

easy I don't recommend uh labeling this I don't even know if it'll do it um it

I don't even know if it'll do it um it would be an absolute mess to try to

would be an absolute mess to try to label all these

label all these postcodes well let's bring this out and

postcodes well let's bring this out and let's bring these State and provinces in

let's bring these State and provinces in now right now we have these little tiny

now right now we have these little tiny tiny uh dots on here and I think what we

tiny uh dots on here and I think what we want to do is not increase the size size

want to do is not increase the size size but over here we want to actually do

but over here we want to actually do this and make it a map so now it's going

this and make it a map so now it's going to fill in all the states we can you

to fill in all the states we can you know why not we'll add some color here

know why not we'll add some color here um but we

um but we can it hasn't numbered I didn't think

can it hasn't numbered I didn't think they were numbered

they were numbered um oh that's interesting I haven't seen

um oh that's interesting I haven't seen that I didn't look at that before I was

that I didn't look at that before I was just found that interesting but now we

just found that interesting but now we can see what uh what states Starbucks is

can see what uh what states Starbucks is in and as you can see they're in all 50

in and as you can see they're in all 50 states but it's something interesting to

states but it's something interesting to um look at to think about now if we go

um look at to think about now if we go right up here we can again choose a

right up here we can again choose a different type and we're going to go to

different type and we're going to go to the density now right now it's just

the density now right now it's just doing a density on the uh the state

doing a density on the uh the state we're going get rid of that we're going

we're going get rid of that we're going to bring back postal code I'm just

to bring back postal code I'm just switching it up on you a little bit and

switching it up on you a little bit and you can do it as small or as big as

you can do it as small or as big as you'd like um you know I like to do

you'd like um you know I like to do somewhere in the middle um probably

somewhere in the middle um probably right right about there is fine um I

right right about there is fine um I don't think it's going to make sense to

don't think it's going to make sense to really add any color here again all

really add any color here again all these poster codes are different so it's

these poster codes are different so it's just going to be complete mish mash but

just going to be complete mish mash but this is kind of how you can use a

this is kind of how you can use a density map and you can do this with uh

density map and you can do this with uh countries you can do this with postal

countries you can do this with postal codes you can do this with any type of

codes you can do this with any type of kind of like address or location based

kind of like address or location based data so that is how you can use a map

data so that is how you can use a map again there's lots of different ways to

again there's lots of different ways to use a map and so I'm not going to show

use a map and so I'm not going to show you every single way but in a really

you every single way but in a really brief way this is how you can use a map

brief way this is how you can use a map to actually visualize your data that

to actually visualize your data that does have location uh based information

does have location uh based information in it so let's go over to sheet three uh

in it so let's go over to sheet three uh and this data that we have over here it

and this data that we have over here it just allows for a lot of different types

just allows for a lot of different types of visualizations so we're going to use

of visualizations so we're going to use this one um and there are lots of other

this one um and there are lots of other ones that you might see out there like

ones that you might see out there like this one right here uh we obviously

this one right here uh we obviously wouldn't be using this we might do

wouldn't be using this we might do something like this change the

something like this change the label um and maybe add why have both of

label um and maybe add why have both of these in here um let's get rid of this

these in here um let's get rid of this oops that's not what I meant let's

oops that's not what I meant let's actually add that let's do the sum of

actually add that let's do the sum of global sales and we'll just make that

global sales and we'll just make that into a label as well

into a label as well so what you can do with these and and

so what you can do with these and and how you're able to use them and

how you're able to use them and visualize them again these are not

visualize them again these are not you'll see these often but these are not

you'll see these often but these are not often ones that I would recommend you

often ones that I would recommend you use that's very similar to these packed

use that's very similar to these packed bubbles um you can as these Global sales

bubbles um you can as these Global sales in here again add the label it just uh

in here again add the label it just uh it sometimes is not as straightforward

it sometimes is not as straightforward the information that it's trying to tell

the information that it's trying to tell you right you kind of have to search for

you right you kind of have to search for it a little bit you kind of have to look

it a little bit you kind of have to look around um but you can find some good

around um but you can find some good visualizations in here for very specific

visualizations in here for very specific types of data and so these are just ones

types of data and so these are just ones to consider uh one that you'll see all

to consider uh one that you'll see all the time is uh this guy right here and

the time is uh this guy right here and uh let me see if I can expand this a

uh let me see if I can expand this a little bit because this

little bit because this is very small um let's see we have the I

is very small um let's see we have the I just want Global

just want Global sales and let's label

sales and let's label that the

that the size I how do I expand this haven't done

size I how do I expand this haven't done this in a while let me just expand this

this in a while let me just expand this I don't use pie charts what is

I don't use pie charts what is happening this is a incredibly large pie

happening this is a incredibly large pie chart oh my gosh I am making this um

chart oh my gosh I am making this um this is becoming a problem there we go

this is becoming a problem there we go uh and what I actually wanted to do was

uh and what I actually wanted to do was label the uh genre as well as I've been

label the uh genre as well as I've been doing in all the other

doing in all the other ones and we'll label this now look

ones and we'll label this now look whether you are a fan of pie charts or

whether you are a fan of pie charts or not you have to understand that people

not you have to understand that people use them uh some people just like how

use them uh some people just like how they look and for certain data it can do

they look and for certain data it can do well for things that have a lot of

well for things that have a lot of different um groupings or categories it

different um groupings or categories it usually isn't super great but it does

usually isn't super great but it does give you some type of order of things

give you some type of order of things give you a quick glance and people use

give you a quick glance and people use them right so let's not pretend like

them right so let's not pretend like it's like the the the Hideous stepchild

it's like the the the Hideous stepchild all right people use it people have it

all right people use it people have it in their dashboards and their

in their dashboards and their visualizations all over so it's best to

visualizations all over so it's best to just know what they look like know how

just know what they look like know how to do them know um how to use them best

to do them know um how to use them best again I'm not a super huge huge fan of

again I'm not a super huge huge fan of it myself I've used it once or twice but

it myself I've used it once or twice but one to look out for and again you can

one to look out for and again you can come over to here and use is called a

come over to here and use is called a box and a whisker plot um it's good for

box and a whisker plot um it's good for these large um distributions you know

these large um distributions you know this is

this is like the median upper upper lower lower

like the median upper upper lower lower I don't use these a lot but I know a lot

I don't use these a lot but I know a lot of people who love them something to

of people who love them something to just look at and or mess around with it

just look at and or mess around with it a little bit it's pretty I think

a little bit it's pretty I think straightforward and it does give you

straightforward and it does give you some good insight into your data if you

some good insight into your data if you know how to use it now there is one last

know how to use it now there is one last one that I want to show you I'm just

one that I want to show you I'm just going to create it on a new sheet make

going to create it on a new sheet make it easy uh we'll do year here we'll do

it easy uh we'll do year here we'll do some of let's do na sales why

some of let's do na sales why not and we are going to make this like

not and we are going to make this like this now it's very similar to a line

this now it's very similar to a line chart but when we break it out by the

chart but when we break it out by the genre and we add some color you know

genre and we add some color you know it's just a different way to visualize

it's just a different way to visualize this information you can uh you know

this information you can uh you know potentially add some stuff in here like

potentially add some stuff in here like some labels if you uh want to depending

some labels if you uh want to depending on how it looks for you but this is just

on how it looks for you but this is just another way to visualize the data so

another way to visualize the data so wanting to give you guys some options

wanting to give you guys some options wanting to give you some things that you

wanting to give you some things that you might want to look at if you haven't

might want to look at if you haven't already used these before four these are

already used these before four these are ones all every single one that I've

ones all every single one that I've showed you are ones that I've at least

showed you are ones that I've at least used once um this one I maybe have

used once um this one I maybe have literally only used once but the first

literally only used once but the first ones that I showed you the ones I

ones that I showed you the ones I pointed out as the ones that I really

pointed out as the ones that I really wanted you to know are great

wanted you to know are great visualizations to learn how to use and

visualizations to learn how to use and learn how to make useful for the data

learn how to make useful for the data that you have with that being said that

that you have with that being said that is all that we are looking at in this

is all that we are looking at in this video again I tried to keep it super

video again I tried to keep it super easy just wanted to show you some

easy just wanted to show you some different visualizations the data that

different visualizations the data that you can use to get those visualizations

you can use to get those visualizations and just some other options in case you

and just some other options in case you wanted to get a little bit uh

wanted to get a little bit uh spontaneous a little bit out there a

spontaneous a little bit out there a little bit funky uh to show your boss or

little bit funky uh to show your boss or something like that thank you guys so

something like that thank you guys so much for watching I really appreciate it

much for watching I really appreciate it if you like this video be sure to like

if you like this video be sure to like And subscribe below and I will see you

And subscribe below and I will see you in the next

video [Music]

what's going on everybody welcome back to another video today we're looking at

to another video today we're looking at joins in

[Music] Tableau now before we get into the

Tableau now before we get into the tutorial I want to give a huge shout out

tutorial I want to give a huge shout out to today's sponsor and that is udem me

to today's sponsor and that is udem me they were having a massive Black Friday

they were having a massive Black Friday sale and so everything is about 85% off

sale and so everything is about 85% off so if you've been looking at a course

so if you've been looking at a course now is the time to buy it if you are

now is the time to buy it if you are looking at learning and taking an actual

looking at learning and taking an actual full Tableau course there are fantastic

full Tableau course there are fantastic ones on UD me that I have taken myself

ones on UD me that I have taken myself so be sure to go and check out UD me

so be sure to go and check out UD me while they're having this huge sale I

while they're having this huge sale I will include a link in the description

will include a link in the description if you want to check them out now let's

if you want to check them out now let's get into the tutorial all right let's

get into the tutorial all right let's get started and first we're going to

get started and first we're going to start off in Excel I'm going to kind of

start off in Excel I'm going to kind of walk you through the data that we're

walk you through the data that we're working with and then we're going to put

working with and then we're going to put it into Tableau and I'm going to show

it into Tableau and I'm going to show you how to do all those joins in Tableau

you how to do all those joins in Tableau so the first table that we have is this

so the first table that we have is this demographics table we have employee ID

demographics table we have employee ID name of employee employee age and

name of employee employee age and employee gender now look right here

employee gender now look right here because this will be important uh going

because this will be important uh going forward in the demographics table we

forward in the demographics table we have 10 uh individuals and they each

have 10 uh individuals and they each have an employee ID now when we go to

have an employee ID now when we go to the job title we have our employee ID

the job title we have our employee ID employee name and the job title but this

employee name and the job title but this one is missing Ryan Howard is missing

one is missing Ryan Howard is missing his employee ID and then the very last

his employee ID and then the very last one there are only seven employee IDs

one there are only seven employee IDs and no names um and so we're going to

and no names um and so we're going to use all of that and I'm going to show

use all of that and I'm going to show you how to actually do the joins into

you how to actually do the joins into Tableau Tableau does a really fantastic

Tableau Tableau does a really fantastic job of visualizing for you so it takes a

job of visualizing for you so it takes a lot of the guesswork out um I am going

lot of the guesswork out um I am going to include a link to my joins video in

to include a link to my joins video in SQL because these two are very closely

SQL because these two are very closely connected and and if you understand how

connected and and if you understand how the joins work in in SQL you'll

the joins work in in SQL you'll understand how the joins work in Tableau

understand how the joins work in Tableau it's almost the exact same thing so with

it's almost the exact same thing so with that being said let's jump over to

that being said let's jump over to Tableau so I'm going to pull this up

Tableau so I'm going to pull this up going go right over here and now we have

going go right over here and now we have uh where where we can connect to our

uh where where we can connect to our data and so we're going to click

data and so we're going to click Microsoft Excel I'm going to scroll down

Microsoft Excel I'm going to scroll down here to Tableau joins file I'm going to

here to Tableau joins file I'm going to open this up and I have it open so I

open this up and I have it open so I can't use it so let me get rid of that

can't use it so let me get rid of that and let's open it again perfect so now

and let's open it again perfect so now what we're going to do and I'm going to

what we're going to do and I'm going to show you how to actually open up the

show you how to actually open up the joins um in a second but what you need

joins um in a second but what you need to understand is when you first come

to understand is when you first come here Tableau doesn't automatically allow

here Tableau doesn't automatically allow you to to use the joins they use

you to to use the joins they use something called relationships and there

something called relationships and there are joins on the back end but they call

are joins on the back end but they call it relationships because they are

it relationships because they are inferring all of these things they're

inferring all of these things they're trying to go in and make that inference

trying to go in and make that inference for you so it takes a lot of the work

for you so it takes a lot of the work off of you and most of the time that

off of you and most of the time that works and and you know you just plug

works and and you know you just plug these two things in here like a

these two things in here like a demographics and the job title and it is

demographics and the job title and it is going to you know help you build those

going to you know help you build those what they call relationships and you can

what they call relationships and you can click on this and learn how the

click on this and learn how the relationships differ from joins again

relationships differ from joins again there's not a huge difference but it's

there's not a huge difference but it's not as custom customizable and you can't

not as custom customizable and you can't as easily do left joins or full joins or

as easily do left joins or full joins or all these things that we're about to

all these things that we're about to look at so uh I'm going to take this one

look at so uh I'm going to take this one off and what we're going to do to

off and what we're going to do to actually be able to look at the joins

actually be able to look at the joins and and choose what joins we want to use

and and choose what joins we want to use is we're going to do this dropdown we're

is we're going to do this dropdown we're going to click open and so now we are in

going to click open and so now we are in a place where we can actually create the

a place where we can actually create the joins uh and again it's just much more

joins uh and again it's just much more customizable and so um back when I was

customizable and so um back when I was using

using regularly I would use the relationships

regularly I would use the relationships when it was pretty simple and

when it was pretty simple and straightforward cuz almost they almost

straightforward cuz almost they almost always got it right but uh you know the

always got it right but uh you know the joins it it just makes more sense in the

joins it it just makes more sense in the way it visualizes it for me so most of

way it visualizes it for me so most of the time I'd be using the joins so let's

the time I'd be using the joins so let's pull over this job title right here and

pull over this job title right here and it's going to make this connection now

it's going to make this connection now before if you remember just about you

before if you remember just about you know 30 seconds ago when it connected

know 30 seconds ago when it connected them it was just a line and and so it

them it was just a line and and so it gave us the this option down here to

gave us the this option down here to kind of edit the relationship but now

kind of edit the relationship but now it's giving us this visualization and so

it's giving us this visualization and so let's click on it really quick and what

let's click on it really quick and what is going to come up is the different

is going to come up is the different types of joins that you can do you can

types of joins that you can do you can do an inner join a left join a right

do an inner join a left join a right join and a full outer join and then you

join and a full outer join and then you can actually choose the different uh

can actually choose the different uh data sources and how you're connecting

data sources and how you're connecting them so again um I'm going to walk

them so again um I'm going to walk through a little bit of this but I think

through a little bit of this but I think the sequel video that I did on this

the sequel video that I did on this shows it so well um I would highly

shows it so well um I would highly recommend using that um and I recommend

recommend using that um and I recommend learning SQL too so you know two birds

learning SQL too so you know two birds one stem so I'm going to get into each

one stem so I'm going to get into each of the joins how they work what data is

of the joins how they work what data is going to be displayed um and these

going to be displayed um and these visualizations are really going to be

visualizations are really going to be helpful and I think that it's it's just

helpful and I think that it's it's just nice that they have it because it's a

nice that they have it because it's a little reminder okay um you know this is

little reminder okay um you know this is what this joint is or this is what that

what this joint is or this is what that joint is so super super simple so right

joint is so super super simple so right now we have the demographics table and

now we have the demographics table and we have the job title table and so what

we have the job title table and so what it's doing right now and let's get rid

it's doing right now and let's get rid of this what it's doing right now is

of this what it's doing right now is it's doing an inner join and so it's

it's doing an inner join and so it's pulling everything that overlaps if it

pulling everything that overlaps if it matches on the employee ID and the

matches on the employee ID and the employee ID and so right now you only

employee ID and so right now you only see one through n but if you remember in

see one through n but if you remember in the demographics table we had uh 1,000

the demographics table we had uh 1,000 all the way through 10 so where's that

all the way through 10 so where's that 10th one well the 10th one is not there

10th one well the 10th one is not there and that is because in this job title

and that is because in this job title employee ID it only went up

employee ID it only went up to9 and then Ryan Howard just didn't

to9 and then Ryan Howard just didn't have an employee ID in there for

have an employee ID in there for whatever reason so that data is going to

whatever reason so that data is going to be missing now when you are using actual

be missing now when you are using actual data sets very large data sets which we

data sets very large data sets which we will use in the next video when we walk

will use in the next video when we walk through an entire

through an entire project um when you use large data sets

project um when you use large data sets this can be the difference between clean

this can be the difference between clean data and very wrong data and and

data and very wrong data and and visualizing it correctly and showing

visualizing it correctly and showing completely wrong numbers and so you

completely wrong numbers and so you really need to be sure you understand

really need to be sure you understand how your data works together when you're

how your data works together when you're doing these joins so how can we fix this

doing these joins so how can we fix this how can we um make it to where we can

how can we um make it to where we can see all of the data well right now we're

see all of the data well right now we're only making it to where if the employee

only making it to where if the employee ID is equal to the employee ID so we

ID is equal to the employee ID so we only are going to see through 109 and

only are going to see through 109 and through 109 we're never going to see

through 109 we're never going to see Ryan so there are two different types of

Ryan so there are two different types of joins that we could do to make it see it

joins that we could do to make it see it and then there's something else that we

and then there's something else that we can join on to where we can see that

can join on to where we can see that data the first that we can look at is

data the first that we can look at is the right uh join and what this does is

the right uh join and what this does is it's going to take everything that is

it's going to take everything that is the same but also everything from this

the same but also everything from this job title table regardless of if it has

job title table regardless of if it has a match in the demographics table so

a match in the demographics table so it's pretty you know this visualization

it's pretty you know this visualization does it all it's going to show

does it all it's going to show everything in the right table regardless

everything in the right table regardless and it's only going to show things from

and it's only going to show things from this table if there's a match so let's

this table if there's a match so let's try this one and we should see Ryan

try this one and we should see Ryan Howard in the job title table so let's

Howard in the job title table so let's click on it and if we scroll down there

click on it and if we scroll down there going to be n n n n n until we get to

going to be n n n n n until we get to over here where we now have the data

over here where we now have the data that we had in that actual table but

that we had in that actual table but again this wasn't a match and so we

again this wasn't a match and so we weren't able to see that data so this

weren't able to see that data so this gives us a way to where we can see all

gives us a way to where we can see all of it um all everything from that right

of it um all everything from that right table this job title table and now we're

table this job title table and now we're going to click on the full outer now the

going to click on the full outer now the full outer is going to take everything

full outer is going to take everything from both regardless of if there is a

from both regardless of if there is a match at all and so right here you're

match at all and so right here you're going to see Ryan Howard and Ryan Howard

going to see Ryan Howard and Ryan Howard now why are there two different rows for

now why are there two different rows for it well because in the demographics

it well because in the demographics table there was an employee ID so we're

table there was an employee ID so we're seeing the employee ID Ryan Howard his

seeing the employee ID Ryan Howard his age and his gender and over here there

age and his gender and over here there was no match right but in the job title

was no match right but in the job title table again this one didn't have an

table again this one didn't have an employee ID and so we we are going to be

employee ID and so we we are going to be able to see this data but over here it

able to see this data but over here it has no match and so that's why showing

has no match and so that's why showing us two different rows is because there

us two different rows is because there was no connection there was no match

was no connection there was no match there that's what a full outer joint is

there that's what a full outer joint is going to do now just for uh the purposes

going to do now just for uh the purposes of seeing what this one does as well we

of seeing what this one does as well we have the leftand table um and now we are

have the leftand table um and now we are able to see the 110 or or 1010 that we

able to see the 110 or or 1010 that we didn't see before um and it's putting in

didn't see before um and it's putting in nulles over here because there's no

nulles over here because there's no match so that's that is um what we have

match so that's that is um what we have so far now like I said just a second

so far now like I said just a second going to go there is a way that we can

going to go there is a way that we can do this without using the employee IDs

do this without using the employee IDs we're allowed to use a different join

we're allowed to use a different join Clause now there is the name of the

Clause now there is the name of the employee in both of them this one is

employee in both of them this one is called name of employee and in the job

called name of employee and in the job title it's called employee name they

title it's called employee name they don't have to have the same column name

don't have to have the same column name in order to join it you can do whatever

in order to join it you can do whatever you want so I'm going to get rid of this

you want so I'm going to get rid of this one and now we are only tying it on the

one and now we are only tying it on the employee name and let's do an inter

employee name and let's do an inter join and it should be basically

join and it should be basically everything um except the only piece of

everything um except the only piece of data that wasn't filled in which is that

data that wasn't filled in which is that 110 over on the job title table and so

110 over on the job title table and so this way was a slightly different maybe

this way was a slightly different maybe uh less thought of way because normally

uh less thought of way because normally you do it if there's an ID you go on the

you do it if there's an ID you go on the IDS but because we had a lack of data

IDS but because we had a lack of data for in in one of the tables in the job

for in in one of the tables in the job title table we decided to use a

title table we decided to use a different column to to join on and now

different column to to join on and now we're able to look at all the data

we're able to look at all the data together so super quickly that is an

together so super quickly that is an inner join a left join a right join and

inner join a left join a right join and a full outer join and it's pretty easily

a full outer join and it's pretty easily visualized here and you're able to uh

visualized here and you're able to uh change what you're joining on right here

change what you're joining on right here but you're also you can do multiple so

but you're also you can do multiple so if we want to do the employee ID and the

if we want to do the employee ID and the employee ID you can do that as well and

employee ID you can do that as well and you can keep going as as many as you'd

you can keep going as as many as you'd like um and

like um and right here or you can change some of

right here or you can change some of these things uh I don't there aren't a

these things uh I don't there aren't a lot of use cases for this um but you

lot of use cases for this um but you know you can absolutely do this um and

know you can absolutely do this um and mess around with this as seen I'm not

mess around with this as seen I'm not going to go through it in the tutorial

going to go through it in the tutorial because again 95 plus perc of the joins

because again 95 plus perc of the joins you're doing you're going to want to do

you're doing you're going to want to do it to where this equals this um and if

it to where this equals this um and if you want to get into where it doesn't

you want to get into where it doesn't equal or or all these other things which

equal or or all these other things which is more complicated I think it's much

is more complicated I think it's much better to learn that in SQL uh that's my

better to learn that in SQL uh that's my personal preference and so um again all

personal preference and so um again all in the SQL tutorial if you want to check

in the SQL tutorial if you want to check that one out so you're able to join on

that one out so you're able to join on multiple things now let's get rid of

multiple things now let's get rid of that one because we can actually bring

that one because we can actually bring in this salary one as well and what

in this salary one as well and what you'll see right down

you'll see right down here is that we have our employee ID and

here is that we have our employee ID and this is all coming from the demographics

this is all coming from the demographics so employee ID name of employer employee

so employee ID name of employer employee age employee gender then right over here

age employee gender then right over here we have the job title table so employee

we have the job title table so employee ID job title employee name job title and

ID job title employee name job title and then right over here was or is our

then right over here was or is our salary table and so we have employee ID

salary table and so we have employee ID salary and employee salary so again this

salary and employee salary so again this is a way that you can put all of this

is a way that you can put all of this data into one place and and just a

data into one place and and just a second we'll go into the

second we'll go into the worksheet right down here I'm going to

worksheet right down here I'm going to show you kind of how it looks because it

show you kind of how it looks because it looks a little bit different um than

looks a little bit different um than previous tutorials and so I want to show

previous tutorials and so I want to show you how that actually all works together

you how that actually all works together um but again you can create these joins

um but again you can create these joins um as well and do the exact same thing

um as well and do the exact same thing that we just looked at and customize the

that we just looked at and customize the joins customize what you're what you're

joins customize what you're what you're um uh joining on and then you have your

um uh joining on and then you have your finished product and so right now we

finished product and so right now we have our demographics plus Tableau joins

have our demographics plus Tableau joins file and we can rename that if we want

file and we can rename that if we want I'm going to call this um demographics

I'm going to call this um demographics plus joins

plus joins demo and click enter and so now that is

demo and click enter and so now that is saved so so now let's go down to the go

saved so so now let's go down to the go to worksheet we're going to click on

to worksheet we're going to click on that and so up here on our left side

that and so up here on our left side this may look a little bit different

this may look a little bit different than it normally does um because it's

than it normally does um because it's broken out um on the measure names and

broken out um on the measure names and the measure values it's broken out by

the measure values it's broken out by the tables that they were joined on so

the tables that they were joined on so we can pull in the employee gender now

we can pull in the employee gender now and we can pull in the employee name now

and we can pull in the employee name now um and we can pull in the employee ID

um and we can pull in the employee ID again if we want to from the job title

again if we want to from the job title table and we can pull in the employee ID

table and we can pull in the employee ID from the salary table we could do that

from the salary table we could do that if we wanted to it makes no sense uh uh

if we wanted to it makes no sense uh uh for actually creating any visualizations

for actually creating any visualizations but you know you can do that and so you

but you know you can do that and so you probably you wouldn't be able to do that

probably you wouldn't be able to do that if you hadn't joined these together and

if you hadn't joined these together and so down here in the measure values the

so down here in the measure values the values that we have are from the

values that we have are from the demographics table and the salary table

demographics table and the salary table all of the um all of the stuff from the

all of the um all of the stuff from the employee title none of those things were

employee title none of those things were um values and so we can't use there are

um values and so we can't use there are going to be no values down here and so

going to be no values down here and so really quick let's take the name of the

really quick let's take the name of the employee let's take their salary sure

employee let's take their salary sure why not um let's order

why not um let's order that let's take the employee

that let's take the employee salary we'll do

salary we'll do color and uh expan this out a little

color and uh expan this out a little bit maybe one more time oops just like

bit maybe one more time oops just like that and there you go so that is how you

that and there you go so that is how you do joins in Tableau and I think Tableau

do joins in Tableau and I think Tableau does a really fantastic job of making it

does a really fantastic job of making it pretty simple they have the different

pretty simple they have the different types of joins when you click on that

types of joins when you click on that that join button and it shows you the

that join button and it shows you the inner and the left and the right and the

inner and the left and the right and the full outer and they make it pretty

full outer and they make it pretty simple um and and and it's just really

simple um and and and it's just really useful to be able to see that while

useful to be able to see that while you're creating it and see the output

you're creating it and see the output below like we just did a second ago it

below like we just did a second ago it it just makes it so simple to create

it just makes it so simple to create those joins and then just keep going

those joins and then just keep going because you already know what your

because you already know what your output is going to be and you can kind

output is going to be and you can kind of mess around with it and make sure

of mess around with it and make sure you're getting the data that you need in

you're getting the data that you need in the very next video we're going to be

the very next video we're going to be doing an entire project in tap we're

doing an entire project in tap we're going to be using a lot more data and

going to be using a lot more data and it's going to be a a complete project

it's going to be a a complete project that you can add to your portfolio and

that you can add to your portfolio and it's going to be a really good time so I

it's going to be a really good time so I hope that you joined me for that one I

hope that you joined me for that one I appreciate your time I hope that this

appreciate your time I hope that this was helpful thank you guys so much for

was helpful thank you guys so much for watching I really appreciate it if you

watching I really appreciate it if you like this video be sure to like And

like this video be sure to like And subscribe below and I'll see you in the

subscribe below and I'll see you in the next

next [Music]

[Music] video

video what's going on everybody welcome back

what's going on everybody welcome back to the Tableau tutorial Series this is

to the Tableau tutorial Series this is our very last video in the series and

our very last video in the series and today we'll be doing an entire

[Music] project now if you're watching this

project now if you're watching this video I hope that you watch the other

video I hope that you watch the other four videos in this series just so you

four videos in this series just so you can get the basics down you kind of know

can get the basics down you kind of know what you're doing uh this won't be a

what you're doing uh this won't be a crazy hard project this is a beginner

crazy hard project this is a beginner tutorial Series so I'm trying to make

tutorial Series so I'm trying to make this super easy so you can follow along

this super easy so you can follow along nothing super comp complicated I promise

nothing super comp complicated I promise and if you were wanting to go above and

and if you were wanting to go above and beyond and just make a lot of different

beyond and just make a lot of different dashboards or try a lot of different

dashboards or try a lot of different things there's a ton of data in here and

things there's a ton of data in here and so I'll show you some of the things that

so I'll show you some of the things that I would do you know as we go through it

I would do you know as we go through it of the things that I would be looking at

of the things that I would be looking at and some of the different visualizations

and some of the different visualizations that I might do as well but again in

that I might do as well but again in this video we're going to be singing to

this video we're going to be singing to a lot of the basics but I'll switch over

a lot of the basics but I'll switch over my screen in just a second I will show

my screen in just a second I will show you the final product and then we will

you the final product and then we will actually walk through step by step of

actually walk through step by step of how to do the entire dashboard and at

how to do the entire dashboard and at the end you should have a completed

the end you should have a completed project that you can add to your

project that you can add to your portfolio or you know just share on

portfolio or you know just share on LinkedIn if you want to do that as well

LinkedIn if you want to do that as well with that being said let's jump over to

with that being said let's jump over to my screen and let's get started all

my screen and let's get started all right so let's get me off screen and

right so let's get me off screen and show you what we're going to be working

show you what we're going to be working on today this is the final dashboard

on today this is the final dashboard that we're actually going to be building

that we're actually going to be building and so it's nothing crazy right I'm sure

and so it's nothing crazy right I'm sure you have seen all of these things before

you have seen all of these things before um and I'm just going to help you kind

um and I'm just going to help you kind of build it out show you what to do the

of build it out show you what to do the buttons to click um and it's really

buttons to click um and it's really going to be a simple walk through by the

going to be a simple walk through by the end of this you should be able to do all

end of this you should be able to do all these things very easily and I highly

these things very easily and I highly encourage looking at at the data and

encourage looking at at the data and looking at these visualizations and

looking at these visualizations and seeing what else you can do with it

seeing what else you can do with it there's a lot of different colors a lot

there's a lot of different colors a lot of different visualizations um that you

of different visualizations um that you can do with this data I'm just showing

can do with this data I'm just showing you this today and so the more you go

you this today and so the more you go out there and the more you do this on

out there and the more you do this on your own and you mess around with stuff

your own and you mess around with stuff and and choose different things and see

and and choose different things and see how it all works the better you're going

how it all works the better you're going to get and so I highly highly encourage

to get and so I highly highly encourage doing that uh so what we are going to be

doing that uh so what we are going to be working with today is an Airbnb data set

working with today is an Airbnb data set I'm going to show you that in just a

I'm going to show you that in just a second and I'm going to show you the

second and I'm going to show you the data and we're going to just jump right

data and we're going to just jump right into it all right so this is the data

into it all right so this is the data set that we are going to be using this

set that we are going to be using this is the Seattle Airbnb open data set and

is the Seattle Airbnb open data set and let's scroll down really quick um

let's scroll down really quick um there's three different csvs in here and

there's three different csvs in here and so this is some of the data that we're

so this is some of the data that we're going to be working with um some date on

going to be working with um some date on listings and some pricing and then

listings and some pricing and then there's the actual listing that shows um

there's the actual listing that shows um the actual street address the location

the actual street address the location the price the bedrooms all of these good

the price the bedrooms all of these good stuff stuff and then there's a

stuff stuff and then there's a reviews um and it has you know some

reviews um and it has you know some comments and you know talks about some

comments and you know talks about some of the reviews so this is what we're

of the reviews so this is what we're going to be working with but you don't

going to be working with but you don't have to go in here and download it I

have to go in here and download it I have already combined all these csvs

have already combined all these csvs into one I've put it on the GitHub so

into one I've put it on the GitHub so I'll have a link below so you can just

I'll have a link below so you can just click on that and you don't have to do

click on that and you don't have to do all the stuff that I did to get this set

all the stuff that I did to get this set up um just so you know this is from 2016

up um just so you know this is from 2016 so this data set is a little bit old if

so this data set is a little bit old if you want to you can come right here and

you want to you can come right here and I will leave this link as well and you

I will leave this link as well and you can get the data set from you know what

can get the data set from you know what is this a couple weeks ago uh this is

is this a couple weeks ago uh this is they they are continuing to update this

they they are continuing to update this this is always updated and so you can go

this is always updated and so you can go ahead and download these but some of

ahead and download these but some of these are the CSV Dogz um so you may

these are the CSV Dogz um so you may need to like convert it I don't want to

need to like convert it I don't want to go through that process um on you know

go through that process um on you know in the video and so I am just going to

in the video and so I am just going to go with what is literally in kaggle um

go with what is literally in kaggle um and use that but if you want want to

and use that but if you want want to have an updated one for your project I

have an updated one for your project I just advise you to go in here and grab

just advise you to go in here and grab it yourself and that should be perfectly

it yourself and that should be perfectly good so go ahead and download the data

good so go ahead and download the data set from the GitHub and we should be

set from the GitHub and we should be good to go so this is the Excel that I

good to go so this is the Excel that I was just talking about this has all of

was just talking about this has all of our csvs in one place this is you know

our csvs in one place this is you know an Excel workbook so in this reviews

an Excel workbook so in this reviews actually let's start with the listings

actually let's start with the listings because that's kind of where it all

because that's kind of where it all stems from uh we have our listing and

stems from uh we have our listing and the DAT or the data in here is um you

the DAT or the data in here is um you you know really extensive there's a lot

you know really extensive there's a lot of data in here so let's get over really

of data in here so let's get over really quick um the listing refers to the

quick um the listing refers to the actual home that they're renting out the

actual home that they're renting out the Airbnb so it shows their

Airbnb so it shows their location um and there's a lot more

location um and there's a lot more location information over here I'm

location information over here I'm getting into it in in just a second so

getting into it in in just a second so there's the neighborhood the city state

there's the neighborhood the city state um zip code all stuff that you know may

um zip code all stuff that you know may be useful there's a latitude and

be useful there's a latitude and longitude it shows what type of property

longitude it shows what type of property it is so that's really really good um

it is so that's really really good um right over here it has you know how many

right over here it has you know how many bathrooms bedrooms and beds um you know

bathrooms bedrooms and beds um you know sometimes if it's a five bedroom house

sometimes if it's a five bedroom house it has seven beds so that's why there's

it has seven beds so that's why there's those two different um Fields I don't

those two different um Fields I don't know if you're familiar with Airbnb and

know if you're familiar with Airbnb and and you know what they have on there but

and you know what they have on there but just something to note uh they have the

just something to note uh they have the price this is the price per day this is

price this is the price per day this is a weekly price a monthly price and if

a weekly price a monthly price and if there's a deposit needed uh and then a

there's a deposit needed uh and then a cleaning fee as well so a bunch of

cleaning fee as well so a bunch of financial data that's you know super

financial data that's you know super useful we go into it a little bit but

useful we go into it a little bit but there's so much you can do with that um

there's so much you can do with that um you know if you want to dig into that

you know if you want to dig into that and that's kind of it the rest of it's

and that's kind of it the rest of it's pretty uh pretty useless um and there's

pretty uh pretty useless um and there's a lot so there's so much data in here

a lot so there's so much data in here almost you know more than half by far is

almost you know more than half by far is nothing you would put in any type of

nothing you would put in any type of visualization um and this is pretty

visualization um and this is pretty common uh you're not going to

common uh you're not going to get data every column where you're going

get data every column where you're going to be able to use it a lot of times it's

to be able to use it a lot of times it's just a lot of useless junk and so you

just a lot of useless junk and so you have to know what you're looking for and

have to know what you're looking for and know uh you know what's actually useful

know uh you know what's actually useful so that's the listing then we have

so that's the listing then we have reviews

reviews now what's really a little bit confusing

now what's really a little bit confusing in here and something that you just need

in here and something that you just need to kind of understand about the data um

to kind of understand about the data um and something that if you're if you get

and something that if you're if you get a data analyst job you need to

a data analyst job you need to understand your data because it's very

understand your data because it's very easy to come in here and say okay

easy to come in here and say okay there's an ID ID field and here's an ID

there's an ID ID field and here's an ID field so that means that those are the

field so that means that those are the same well not in this case um this ID

same well not in this case um this ID field is actually the review reviews ID

field is actually the review reviews ID not the reviewer ID that refers to like

not the reviewer ID that refers to like the person this is the reviews ID this

the person this is the reviews ID this listing ID is the actual ID right there

listing ID is the actual ID right there so really important to

so really important to note um and then the L and so then they

note um and then the L and so then they just have their comment there what they

just have their comment there what they left as a review and then on the

left as a review and then on the calendar um I don't know why I'm

calendar um I don't know why I'm scrolled down uh we have this listing

scrolled down uh we have this listing idea again so again that listing ID is

idea again so again that listing ID is equal to the ID in this listing table

equal to the ID in this listing table and we have a date in a price so this

and we have a date in a price so this refers to a specific location and on

refers to a specific location and on this day they got $85 for it somebody

this day they got $85 for it somebody rented it out um and so then there's

rented it out um and so then there's these like T's and Fs um let's try to

these like T's and Fs um let's try to find a blank one really quick here's a

find a blank one really quick here's a blank one so there's these T's and Fs uh

blank one so there's these T's and Fs uh the t means that it was taken um the f

the t means that it was taken um the f means that it's vacant I don't know

means that it's vacant I don't know exactly what it means uh what a TF means

exactly what it means uh what a TF means but that we can deduce that much from

but that we can deduce that much from this and so you can see when and how

this and so you can see when and how much this person was making or this

much this person was making or this homeade uh in that time so really really

homeade uh in that time so really really good data in here there's a lot to work

good data in here there's a lot to work with um and and so we're just going to

with um and and so we're just going to be kind of I'll give you a little bit of

be kind of I'll give you a little bit of a use case for it in a second and then

a use case for it in a second and then we're going to start trying to answer

we're going to start trying to answer some of those the building out some of

some of those the building out some of the visualizations for that use case uh

the visualizations for that use case uh again you could have 20 different use

again you could have 20 different use cases for this data or more um honestly

cases for this data or more um honestly for this data where you can build out

for this data where you can build out different dashboards and different

different dashboards and different reports literally with just this data

reports literally with just this data but you know we're doing a pretty

but you know we're doing a pretty General broad project and so it's hard

General broad project and so it's hard to answer all of them so let's jump over

to answer all of them so let's jump over to Tableau we're going to get started on

to Tableau we're going to get started on this and we are going to build out

this and we are going to build out everything all right so let's come right

everything all right so let's come right here uh this is a Microsoft Excel we'll

here uh this is a Microsoft Excel we'll open that up do this one we will open

open that up do this one we will open it and give it just a second says it's

it and give it just a second says it's executing the query it's pulling the

executing the query it's pulling the data in all right so we have our

data in all right so we have our calendar our listing and our reviews

calendar our listing and our reviews those are the different tabs at the

those are the different tabs at the bottom we're going to start with the

bottom we're going to start with the listing this is the the kind of the main

listing this is the the kind of the main one has um you know the there's I didn't

one has um you know the there's I didn't show you but there's about

show you but there's about 3,600

3,600 locations that they had in

locations that they had in there uh let's just have it update

there uh let's just have it update automatically I don't know why we need

automatically I don't know why we need to click on that but um so we have this

to click on that but um so we have this list listings we have our calendar and

list listings we have our calendar and our

our reviews what we're going to do is going

reviews what we're going to do is going to come in here and we're going to open

to come in here and we're going to open it as we did in our very last video uh

it as we did in our very last video uh for the joins so now that we've opened

for the joins so now that we've opened it we can kind of go in here and we can

it we can kind of go in here and we can do the joins as um as needed and so

do the joins as um as needed and so let's go over here and we're going to uh

let's go over here and we're going to uh let's start with

let's start with calendar put it right there that was

calendar put it right there that was super slow I

super slow I apologize all right let's wait for it

to get the data start setting everything up did not think it would take this long

up did not think it would take this long I

apologize no take your time so let's click on here and right now it has the

click on here and right now it has the uh the join based on the price which

uh the join based on the price which obviously is not going to work um and if

obviously is not going to work um and if you remember there is no ID in this

you remember there is no ID in this calendar it's just just the listing ID

calendar it's just just the listing ID um we can actually look right here

um we can actually look right here there's just the listing ID so we're

there's just the listing ID so we're actually going to put listing ID is

actually going to put listing ID is equal to

equal to ID and right down here we can see that

ID and right down here we can see that we have a lot of of well you can't see

we have a lot of of well you can't see it um but we show that there is a lot of

it um but we show that there is a lot of data um and so we know that that is

data um and so we know that that is correct we know that that is now pulling

correct we know that that is now pulling in data correctly because it's showing

in data correctly because it's showing up down here so that's a good thing now

up down here so that's a good thing now in this listings there there are about

in this listings there there are about 3600 um about 3600 listings and

3600 um about 3600 listings and so that all the data that's in listings

so that all the data that's in listings is going to be in there but on the

is going to be in there but on the calendar because we converted from a CSV

calendar because we converted from a CSV to an Excel workbook it isn't able to

to an Excel workbook it isn't able to store as much information so some of the

store as much information so some of the ones in calendar may have gotten cut off

ones in calendar may have gotten cut off so we can just keep at this inj join

so we can just keep at this inj join because we know that if it's in listings

because we know that if it's in listings it is going to be in calendar we know

it is going to be in calendar we know that it if it um there may be some in

that it if it um there may be some in calar Cal that aren't in listings so if

calar Cal that aren't in listings so if we really um you know if we really

we really um you know if we really really wanted to we could do a full

really wanted to we could do a full outer or something like that I I haven't

outer or something like that I I haven't really thought through this as I'm

really thought through this as I'm talking through it in my head but we

talking through it in my head but we know that uh everything that's in

know that uh everything that's in listing is going to be in calendar and

listing is going to be in calendar and so you know we don't really need to do

so you know we don't really need to do anything other than an inner

anything other than an inner join and we can also pull in these

join and we can also pull in these reviews and it's going to do the same

reviews and it's going to do the same thing as before where just kind of

thing as before where just kind of pulling in the data and it defaults to

pulling in the data and it defaults to ID equals ID now we know that that is

ID equals ID now we know that that is not correct um because the ID in here is

not correct um because the ID in here is referring to the review ID we need to go

referring to the review ID we need to go to the listings ID so we need the ID be

to the listings ID so we need the ID be able to you know be part of that

able to you know be part of that listings ID if we do the

listings ID if we do the ID it goes down to

ID it goes down to 2,555 rows if we do how it's supposed

2,555 rows if we do how it's supposed and because that's just you know it's

and because that's just you know it's random luck there happen to be some

random luck there happen to be some numbers that are in both fields um that

numbers that are in both fields um that tie together if we do the correct one

tie together if we do the correct one where we hit the listing ID it bumps it

where we hit the listing ID it bumps it up to I think 2, 373,000 oh maybe more

up to I think 2, 373,000 oh maybe more than that uh 23 million rows right a lot

than that uh 23 million rows right a lot lot lot more and so it's super important

lot lot more and so it's super important to get these joins right to tie them

to get these joins right to tie them together on the right Fields if you just

together on the right Fields if you just do it based off what Tableau tells you

do it based off what Tableau tells you because it has that automated um you

because it has that automated um you know it goes into these fields and says

know it goes into these fields and says okay these are the same exact column

okay these are the same exact column name so they're most likely going to be

name so they're most likely going to be what you're looking for well it was

what you're looking for well it was incorrect in this point so it's really

incorrect in this point so it's really important to check those things and make

important to check those things and make sure you're pulling in the right data

sure you're pulling in the right data again we're going to keep it that inner

again we're going to keep it that inner join um you know if you wanted to you

join um you know if you wanted to you know try to see if there's any other

know try to see if there's any other data that correlate we're keeping it

data that correlate we're keeping it simple today but sometimes you need to

simple today but sometimes you need to join on multiple things uh so just uh a

join on multiple things uh so just uh a you know a tip so let's get out of here

you know a tip so let's get out of here um and we are good to go so this is our

um and we are good to go so this is our listings plus Tableau full project

listings plus Tableau full project that's what we'll that's what we'll be

that's what we'll that's what we'll be working with um and we we were able to

working with um and we we were able to tie all three of these um you know as

tie all three of these um you know as you call them tables or sheets or

you call them tables or sheets or whatever you want to call them we were

whatever you want to call them we were able to tie them together so let's go

able to tie them together so let's go over here to our first

over here to our first worksheet uh let's

worksheet uh let's see all right so this says Tableau

see all right so this says Tableau public only works with less than 15

public only works with less than 15 million rows of data we have 23 million

million rows of data we have 23 million rows of data that is uh that's a problem

rows of data that is uh that's a problem um and when I did this before it didn't

um and when I did this before it didn't do that so I you know we're going to

do that so I you know we're going to work through this together so this is

work through this together so this is date reviews I believe this is date for

date reviews I believe this is date for um this is date for the calendar which

um this is date for the calendar which is going to be a lot of rows of data and

is going to be a lot of rows of data and so I'm sure that's part of it let's

so I'm sure that's part of it let's see let's do

see let's do years we only want 2016 oops we only

years we only want 2016 oops we only want

2016 let's do okay let's see what that does let's see if

let's see what that does let's see if that gets us under what we need um we

that gets us under what we need um we only want 2016 data

only want 2016 data anyways so if it's in 2017 we were going

anyways so if it's in 2017 we were going to take it out um anyway so we'll see if

to take it out um anyway so we'll see if that gets us underneath I have

that gets us underneath I have absolutely if this T ends up taking like

absolutely if this T ends up taking like 20 minutes I will just cut it and you

20 minutes I will just cut it and you know you won't have to wait as long as

know you won't have to wait as long as I'm waiting so let's see how long it

takes all right so it took about 20 minutes and it did absolutely nothing

minutes and it did absolutely nothing um one thing I do know is that we don't

um one thing I do know is that we don't actually use this review tables at all

actually use this review tables at all um just for demonstration purposes so

um just for demonstration purposes so we're going to remove that and let's see

we're going to remove that and let's see if that helps us in any

way if it does we're just going to keep it as is um you know the reviews table

it as is um you know the reviews table is really just for demonstrating how to

is really just for demonstrating how to do the joint

do the joint but we weren't actually using any of the

but we weren't actually using any of the data for any of the

data for any of the visualizations although you

visualizations although you could again I'm going to see how long

could again I'm going to see how long this takes uh and I'll cut

ahead all right so that worked uh perfectly it apparently took out all the

perfectly it apparently took out all the data that we needed all the rows that we

data that we needed all the rows that we needed to get under that level again I

needed to get under that level again I was just doing that to show you the that

was just doing that to show you the that that joins how you needed to change the

that joins how you needed to change the columns to make sure that it joined

columns to make sure that it joined properly we don't actually use for any

properly we don't actually use for any of the visualization so their end

of the visualization so their end product is going to be totally fine I

product is going to be totally fine I don't know why uh this didn't happen to

don't know why uh this didn't happen to me when I when I created this whole

me when I when I created this whole thing already um so just going to move

thing already um so just going to move forward because uh I make mistakes so uh

forward because uh I make mistakes so uh let's keep moving the first one that we

let's keep moving the first one that we are going to make is that uh is that

are going to make is that uh is that colorful one I'll probably pop it up on

colorful one I'll probably pop it up on screen so you can see it uh well if I

screen so you can see it uh well if I remember I'm going to pop it up on

remember I'm going to pop it up on screen um it's the colorful one it's the

screen um it's the colorful one it's the price by ZIP code so we're going to be

price by ZIP code so we're going to be looking at these zip codes and kind of

looking at these zip codes and kind of see

see um you know how

um you know how expensive is each zip code um and before

expensive is each zip code um and before we actually start I just remembered I

we actually start I just remembered I want to talk to you about the use case

want to talk to you about the use case for this

for this data I want to imagine you to imagine

data I want to imagine you to imagine that you're working for somebody they're

that you're working for somebody they're like hey where you know I want to start

like hey where you know I want to start an Airbnb business I want to know where

an Airbnb business I want to know where I should go where should I buy up buy a

I should go where should I buy up buy a home put it up on Airbnb and start

home put it up on Airbnb and start renting it out where's the best place

renting it out where's the best place you know what are some of the fact fact

you know what are some of the fact fact that I should be looking at uh and so

that I should be looking at uh and so that's kind of what our use case is so

that's kind of what our use case is so we're going to some of the things that

we're going to some of the things that he cares about are things like bedrooms

he cares about are things like bedrooms um location which is really important

um location which is really important and how much price he's actually going

and how much price he's actually going to get how much money can he charge and

to get how much money can he charge and so he's trying to optimize that to make

so he's trying to optimize that to make sure that whatever rental he gets he can

sure that whatever rental he gets he can make a the most profit from instead of

make a the most profit from instead of choosing something that you know he

choosing something that you know he thinks would work but you know in the

thinks would work but you know in the end he's actually not making that much

end he's actually not making that much money so those things are important so

money so those things are important so that's our use case we're trying to help

that's our use case we're trying to help this guy out help him find a really good

this guy out help him find a really good Airbnb um so let's take a look at these

Airbnb um so let's take a look at these zip codes real quick we have uh quite a

zip codes real quick we have uh quite a few of them and there's one that's null

few of them and there's one that's null uh we'll exclude that or if if it

uh we'll exclude that or if if it doesn't have a zip code we'll just

doesn't have a zip code we'll just exclude those because they're not going

exclude those because they're not going to show up on the these visualizations

to show up on the these visualizations anyways um and so we want to look at the

anyways um and so we want to look at the price so we just want to find uh the

price so we just want to find uh the price which should actually be down

price which should actually be down here and not the sum

here and not the sum uh no we want to look at the average

uh no we want to look at the average price and let's order that this is great

price and let's order that this is great um so this is the most expensive one uh

um so this is the most expensive one uh ZIP code 98134 at

ZIP code 98134 at $26 uh

$26 uh per for the average price uh but let's

per for the average price uh but let's give that some color really quick Let's

give that some color really quick Let's uh where's the ZIP code it's up here so

uh where's the ZIP code it's up here so let's take that zip code we're going to

let's take that zip code we're going to put it right over here we're going to do

put it right over here we're going to do color and it's going to give it some uh

color and it's going to give it some uh assorted colors now these colors are

assorted colors now these colors are going to um when we do the map in just a

going to um when we do the map in just a little bit these colors will um match

little bit these colors will um match what we're doing in there and so you

what we're doing in there and so you know I I like to try to color coordinate

know I I like to try to color coordinate things um we're not doing going too

things um we're not doing going too crazy with the colors today so this is

crazy with the colors today so this is our very first visualization

our very first visualization congratulations it is uh it is complete

congratulations it is uh it is complete so uh we can label this one and we can

so uh we can label this one and we can just

just do price by zip code and I'll make that

do price by zip code and I'll make that bold I don't know I usually like it bold

bold I don't know I usually like it bold we'll apply we'll do like that and boom

we'll apply we'll do like that and boom first one is done uh and this is our

first one is done uh and this is our starting place to say uh Hey person

starting place to say uh Hey person who's looking to buy this Airbnb here

who's looking to buy this Airbnb here are the zip codes where they are able to

are the zip codes where they are able to charge the most um for for their Airbnb

charge the most um for for their Airbnb so let's go over to the second sheet and

so let's go over to the second sheet and we are going to be doing the map and so

we are going to be doing the map and so um map is pretty easy

um map is pretty easy but it it's pretty easy Once you

but it it's pretty easy Once you actually get the data that you need

actually get the data that you need although there's a lot of different data

although there's a lot of different data that you can use for the actual U map

that you can use for the actual U map right here you need something that shows

right here you need something that shows um the location and there's a lot of

um the location and there's a lot of things that show location in here in

things that show location in here in fact they already um provide a latitude

fact they already um provide a latitude and longitude and then at the bottom

and longitude and then at the bottom they generated a latitude and longitude

they generated a latitude and longitude from from some different um fields and

from from some different um fields and then there's just a bunch of different

then there's just a bunch of different um State there's um States there's zip

um State there's um States there's zip codes there are uh I think another one I

codes there are uh I think another one I yeah like country there's a lot of

yeah like country there's a lot of location data in here so which one do we

location data in here so which one do we want to use we want to stay consistent

want to use we want to stay consistent we don't want to deviate from that and

we don't want to deviate from that and start using different um L long

start using different um L long longitude and latitudinal uh coordinates

longitude and latitudinal uh coordinates because that could throw off our our

because that could throw off our our results completely we want to stay

results completely we want to stay consistent with what we're using so we

consistent with what we're using so we actually want to use this ZIP code but

actually want to use this ZIP code but when we pull it up here it's going to

when we pull it up here it's going to give us uh basically the same um you

give us uh basically the same um you know it's going to show these zip codes

know it's going to show these zip codes but we were going to right over here

but we were going to right over here we're going to click on this one and now

we're going to click on this one and now it's going to separate them out so now

it's going to separate them out so now we have all of these um you know kind of

we have all of these um you know kind of separated out what you might get when

separated out what you might get when you first do this um is it might look

you first do this um is it might look like this you may have to zoom in um I

like this you may have to zoom in um I know that that happened to me the other

know that that happened to me the other time excuse me go to here that's what

time excuse me go to here that's what happened to me uh just when I first did

happened to me uh just when I first did it so uh know that that may happen

it so uh know that that may happen and we want to change the colors the

and we want to change the colors the exact same way that we did them before

exact same way that we did them before so we're just going over here we're

so we're just going over here we're doing color and these colors do um they

doing color and these colors do um they do should match up with the um with the

do should match up with the um with the other ones let me um exclude this let me

other ones let me um exclude this let me see if it does 98134 that's the

see if it does 98134 that's the blue and right over here 98134 that's a

blue and right over here 98134 that's a blue I I I believe believe they are

blue I I I believe believe they are going to be the same yep and so just

going to be the same yep and so just scrolling back if you look at the ZIP

scrolling back if you look at the ZIP code on the far right uh they are the

code on the far right uh they are the same so if you're looking like this

same so if you're looking like this section right over here I I'm just

section right over here I I'm just wanting to make sure I'm not going crazy

wanting to make sure I'm not going crazy uh before I get into this and realize

uh before I get into this and realize I'm not correct at all so uh now what we

I'm not correct at all so uh now what we want is you know this doesn't really

want is you know this doesn't really give us any information if I was just to

give us any information if I was just to glance at this map I would have no idea

glance at this map I would have no idea what you're trying to show me um any

what you're trying to show me um any information off this so we want to show

information off this so we want to show some actual

some actual information so first thing that we're

information so first thing that we're going to do is we're going to actually

going to do is we're going to actually add the label to this so that you can

add the label to this so that you can see it you know when you're going over

see it you know when you're going over here and you see okay here's this um zip

here and you see okay here's this um zip code um in the dashboard when we create

code um in the dashboard when we create it you can click on this but if you just

it you can click on this but if you just want to do it visually without having to

want to do it visually without having to click anywhere you'll be able to see

click anywhere you'll be able to see okay 98134 that's right here so this

okay 98134 that's right here so this location right here is you know able to

location right here is you know able to charge a lot of money it's probably a

charge a lot of money it's probably a really nice neighborhood so um and we

really nice neighborhood so um and we can back that up by putting the average

can back that up by putting the average price so these these two visualizations

price so these these two visualizations are really they really go hand in hand

are really they really go hand in hand we're going to add oops not the

we're going to add oops not the sum this one needs to be the average so

sum this one needs to be the average so you go to this measure the sum go to

you go to this measure the sum go to average and there you go and these

average and there you go and these should match so this should be

206.125 206.000 so this all matches um and we

206.000 so this all matches um and we can uh we can actually change that size

can uh we can actually change that size a little bit if you want to actually get

a little bit if you want to actually get it in um get it within each of these

it in um get it within each of these things you know adjust it as you see

things you know adjust it as you see fits I think that's fine right there um

fits I think that's fine right there um no need

no need to mess with it

to mess with it anymore all right so let me see I think

anymore all right so let me see I think that is everything for this one I don't

that is everything for this one I don't know if I want to add anything else uh

know if I want to add anything else uh no I'm going to keep it how it is so

no I'm going to keep it how it is so that is our second visualization again

that is our second visualization again these ones are directly uh correlated

these ones are directly uh correlated and and you know this there's just

and and you know this there's just different ways to visualize it this one

different ways to visualize it this one you can see actually on the map where it

you can see actually on the map where it is and the average price this one you

is and the average price this one you can see from highest to lowest so again

can see from highest to lowest so again you know sometimes when you're doing

you know sometimes when you're doing these visualizations you're going to

these visualizations you're going to have these accompanying um uh these

have these accompanying um uh these accompanying visualizations in your

accompanying visualizations in your dashboard that's very normal so let's

dashboard that's very normal so let's move over to the third one and for this

move over to the third one and for this third one um you know something that our

third one um you know something that our guy was looking at is he's like okay

guy was looking at is he's like okay well you know I'm thinking about listing

well you know I'm thinking about listing it on Airbnb but I also want to live in

it on Airbnb but I also want to live in it so I want to know the best times to

it so I want to know the best times to actually um you know put it on the

actually um you know put it on the market for people to be able to use and

market for people to be able to use and so I was like okay man no problem uh

so I was like okay man no problem uh let's let's take a look at when when are

let's let's take a look at when when are people spending the most money in

people spending the most money in airbnbs and we actually had that

airbnbs and we actually had that calendar um if you remember let's look

calendar um if you remember let's look let's see this calendar so we have this

let's see this calendar so we have this available the date the listing all of

available the date the listing all of that stuff um and let's look at the date

that stuff um and let's look at the date in

in here uh and we obviously don't want it

here uh and we obviously don't want it like this we want it to be more uh more

like this we want it to be more uh more of a Time series and we're going to do

of a Time series and we're going to do be doing that based off of uh the price

be doing that based off of uh the price for the calendar so let's go see if we

for the calendar so let's go see if we can find that really

can find that really quick okay here's the price

where is that calendar one let me see okay there's the calendar

one let me see okay there's the calendar oh

oh here I totally forgot where that was

here I totally forgot where that was supposed to be o that looks

supposed to be o that looks terrible okay um let's see let's let's

terrible okay um let's see let's let's start working on this because this needs

start working on this because this needs some work obviously uh this is the worst

some work obviously uh this is the worst visualization I have ever seen um so we

visualization I have ever seen um so we need to work on this a little bit what

need to work on this a little bit what we need to do is we need to change oh

we need to do is we need to change oh whoops we need to change some the way

whoops we need to change some the way that these dates are are seen so right

that these dates are are seen so right here is a these are two separate things

here is a these are two separate things so if I go right here and I Do by

so if I go right here and I Do by quarter it's just going to change the

quarter it's just going to change the quarters here right that's that isn't

quarters here right that's that isn't really helpful we actually want to keep

really helpful we actually want to keep the year here what we want to do it is

the year here what we want to do it is by year we want to separate it by year

by year we want to separate it by year um but we want to separate it let's just

um but we want to separate it let's just do I don't know let's try weak and see

do I don't know let's try weak and see what it looks like okay this is great

what it looks like okay this is great this is this is what we're looking at

this is this is what we're looking at again um if we went back and Chang this

again um if we went back and Chang this like quarter it uh changed it quarter

like quarter it uh changed it quarter and then change it to week it would show

and then change it to week it would show the

the quarters but it wouldn't

quarters but it wouldn't show everything right this isn't all the

show everything right this isn't all the data that we need and so you know you

data that we need and so you know you really need to make sure that you're

really need to make sure that you're doing this correct I by default it's

doing this correct I by default it's almost always year but if you're looking

almost always year but if you're looking at it via quarter so like let's say

at it via quarter so like let's say somebody comes in you say hey what

somebody comes in you say hey what quarters I Want to Break these out by

quarters I Want to Break these out by quarters um and not year-over-year

quarters um and not year-over-year that's how you would do this but in the

that's how you would do this but in the year we want to break it out by uh the

year we want to break it out by uh the week and you see this huge drop off um

week and you see this huge drop off um at the end well that is actually because

at the end well that is actually because the data doesn't go past that um there's

the data doesn't go past that um there's just like one day of data or one one um

just like one day of data or one one um week of data in here with actual um with

week of data in here with actual um with January of 2017 data so it just drops

January of 2017 data so it just drops off because this is an this is the sum

off because this is an this is the sum so it only adds up to like um 591 th000

so it only adds up to like um 591 th000 compared to like the 2 million so we

compared to like the 2 million so we want to get rid of that um and how do we

want to get rid of that um and how do we do that uh let's see I think it's

filter how's it format no it's not format what am I thinking bear with me

format what am I thinking bear with me uh let's a filter well I was looking for

uh let's a filter well I was looking for it I just couldn't find

it I just couldn't find it uh let's bring it back to the 31st

it uh let's bring it back to the 31st let's see if that fixes what we need

let's see if that fixes what we need perfect uh that's all you had to do um

perfect uh that's all you had to do um and the reason that this is helpful and

and the reason that this is helpful and often times you'd have several years

often times you'd have several years worth of data in here um and then you

worth of data in here um and then you could have you could do even do

could have you could do even do something like this um like this one

something like this um like this one where it has multiple

where it has multiple lines the reason that this is helpful is

lines the reason that this is helpful is because if I'm telling my friend let's I

because if I'm telling my friend let's I mean just I'm going to say it's a friend

mean just I'm going to say it's a friend or business partner whatever you

or business partner whatever you whatever you want to use this use case

whatever you want to use this use case for I'm GNA tell him hey the beginning

for I'm GNA tell him hey the beginning of January all the way until like you

of January all the way until like you know even February it's like really low

know even February it's like really low it's half so there's not a lot of people

it's half so there's not a lot of people traveling because everyone travels when

traveling because everyone travels when at the end of the year so in November

at the end of the year so in November December for the holidays to visit

December for the holidays to visit family um and then in the summer for

family um and then in the summer for vacations I would tell him just based

vacations I would tell him just based off this one thing I would say hey over

off this one thing I would say hey over the summer and then at the end of the

the summer and then at the end of the year and during the holidays that's when

year and during the holidays that's when I would be renting out your air BNB okay

I would be renting out your air BNB okay so just this one very simple

so just this one very simple visualization can help him understand

visualization can help him understand the best times um to do that that may be

the best times um to do that that may be an intuitive you may have already known

an intuitive you may have already known that but you can prove it with the data

that but you can prove it with the data which is always really helpful um and

which is always really helpful um and let's see is there anything else that we

let's see is there anything else that we need to do with

need to do with this uh I'm just going to label it and

this uh I'm just going to label it and I'm going to say

I'm going to say um

um revenue for

revenue for year

year let's do bold do apply there we go do I

let's do bold do apply there we go do I label this last one I didn't let's label

label this last one I didn't let's label that last

that last [Music]

[Music] one and we'll

one and we'll do price per zip

do price per zip code price per zip code we'll just keep

code price per zip code we'll just keep it at that keep it

it at that keep it simple um and let's do that all right I

simple um and let's do that all right I believe we have two more so we have done

believe we have two more so we have done um we've done three of them um we got

um we've done three of them um we got the zip codes we've got the um you know

the zip codes we've got the um you know the time of the year now something else

the time of the year now something else that he was wanting to know is um you

that he was wanting to know is um you know just how things affect it and

know just how things affect it and something that's going to affect the

something that's going to affect the price of the actual Airbnb is going to

price of the actual Airbnb is going to be the amount of bedrooms so the the

be the amount of bedrooms so the the larger the house the more bedrooms the

larger the house the more bedrooms the more it's going to cost typically so we

more it's going to cost typically so we can take a look at that let's pull in

can take a look at that let's pull in these bedrooms

these bedrooms um and that will be our

um and that will be our columns uh no it won't what we need to

columns uh no it won't what we need to do um and so I I knew this was going to

do um and so I I knew this was going to happen I just forgot it until right uh

happen I just forgot it until right uh until right now what we this right now

until right now what we this right now is actually a um it's a a value right so

is actually a um it's a a value right so it's a number and that's totally um

it's a number and that's totally um reasonable because if we go right here

reasonable because if we go right here we do count distinct that's because

we do count distinct that's because there's only seven values right it goes

there's only seven values right it goes there's zero bedrooms 1 2 3 4 5 5 six 7

there's zero bedrooms 1 2 3 4 5 5 six 7 all the way up to seven bedrooms right

all the way up to seven bedrooms right now it has it as a numerical value we

now it has it as a numerical value we want to um change that to create it as

want to um change that to create it as um these measure names not a value so

um these measure names not a value so we're going to um we're going to remove

we're going to um we're going to remove this we're going to go right down here

this we're going to go right down here we're going click this drop down and

we're going click this drop down and we're going to say convert to

we're going to say convert to Dimension and so now we're going to add

Dimension and so now we're going to add it as a dimension so there that looks um

it as a dimension so there that looks um much more normal I really quick I'm

much more normal I really quick I'm going to I'm going to keep these in here

going to I'm going to keep these in here for a second but we're going to get rid

for a second but we're going to get rid of these nulls and zeros because if a

of these nulls and zeros because if a home has zero bedrooms that's a

home has zero bedrooms that's a problem um and so we want to look at the

problem um and so we want to look at the price again let's go down here in the

price again let's go down here in the listings it should be the price now this

listings it should be the price now this is the price for the location per day um

is the price for the location per day um if you want to look at monthly or or you

if you want to look at monthly or or you know stuff like that they have that data

know stuff like that they have that data um but we're just going to do the price

um but we're just going to do the price the average price not the

the average price not the sum um although this is is helpful so

sum um although this is is helpful so just really quick before we change it

just really quick before we change it this is going to show you which ones

this is going to show you which ones make the which ones are bringing in the

make the which ones are bringing in the most money it also may show you which

most money it also may show you which ones are the most common um those are

ones are the most common um those are all different visualizations that we can

all different visualizations that we can do but the one that brings in the most

do but the one that brings in the most money uh that brought in 63 or that has

money uh that brought in 63 or that has $63 Million worth of um worth of

$63 Million worth of um worth of listings so they all add up those one

listings so they all add up those one bedrooms are doing phenomenal half of

bedrooms are doing phenomenal half of that are two bedrooms at 30 million

that are two bedrooms at 30 million three bedrooms at 18 million and so on

three bedrooms at 18 million and so on and so forth so there's a ton of

and so forth so there's a ton of one-bedroom ones we may even keep we

one-bedroom ones we may even keep we could even keep that in there um you

could even keep that in there um you know if we wanted

know if we wanted to um and then we do something similar

to um and then we do something similar later but you can keep something like

later but you can keep something like this in there what we will do really

this in there what we will do really quick though is we're going to do the

quick though is we're going to do the same thing that we've been doing is

same thing that we've been doing is keeping

keeping average um and we are going to get rid

average um and we are going to get rid of this cuz if it doesn't have the

of this cuz if it doesn't have the bedrooms you know that's not helpful to

bedrooms you know that's not helpful to us and if it has zero bedrooms that's

us and if it has zero bedrooms that's that's genuinely a problem I will not be

that's genuinely a problem I will not be renting an Airbnb with my family uh that

renting an Airbnb with my family uh that has zero bedrooms in it so now we have

has zero bedrooms in it so now we have this and would be really helpful to be

this and would be really helpful to be able to see that in the visualization I

able to see that in the visualization I mean it's just kind

mean it's just kind of hard to see it as is I

of hard to see it as is I mean it just does not hurt to add that

mean it just does not hurt to add that right here do a label um why is it

right here do a label um why is it angled like that maybe I just need to

angled like that maybe I just need to move it out

move it out more that looks much better um that's

more that looks much better um that's the average price that cannot be right

the average price that cannot be right that's the sum that's why so let's go

that's the sum that's why so let's go over here let's make that average as

over here let's make that average as well much better because uh if the price

well much better because uh if the price was $3

was $3 million for a three-bedroom I would not

million for a three-bedroom I would not be going there so this is really really

be going there so this is really really useful information for our friend right

useful information for our friend right if um he wants start you know get into

if um he wants start you know get into those one that one bedroom area you know

those one that one bedroom area you know you're not going to be making a lot of

you're not going to be making a lot of money it may be low cost UPF front but

money it may be low cost UPF front but he's not going to be making a lot of

he's not going to be making a lot of money it significantly goes up when you

money it significantly goes up when you reach these five and six bedroom homes

reach these five and six bedroom homes which makes sense I mean if it has five

which makes sense I mean if it has five or six bedrooms in it it's probably a

or six bedrooms in it it's probably a really large really nice home and you

really large really nice home and you can charge a lot more money and our

can charge a lot more money and our friend is uh extremely wealthy he can

friend is uh extremely wealthy he can buy whatever he wants and so he may be

buy whatever he wants and so he may be looking at these um larger on seeing

looking at these um larger on seeing that there's a much higher return um on

that there's a much higher return um on his investment the higher and the more

his investment the higher and the more bedrooms he goes so we're going to keep

bedrooms he goes so we're going to keep it just as it

it just as it is um and let me see is there's anything

is um and let me see is there's anything else that we want to do with this no

else that we want to do with this no we're going to keep it just like this uh

we're going to keep it just like this uh and the last one is by far the easiest

and the last one is by far the easiest and we actually just discussed it a

and we actually just discussed it a little bit we want to know you know

little bit we want to know you know what's his competition look like so um

what's his competition look like so um for those for the bedrooms specifically

for those for the bedrooms specifically so let's go back up to the

so let's go back up to the bedrooms we want that one to be right

bedrooms we want that one to be right here in our rows so we show um these and

here in our rows so we show um these and then we just want to count of um how

then we just want to count of um how many listings there are so we can do

many listings there are so we can do that via the listings ID so here's our

that via the listings ID so here's our listings each ID represents one location

listings each ID represents one location or one home so we're going to do that

or one home so we're going to do that right here uh that looks absolutely

right here uh that looks absolutely terrible that looks terrible what am I

terrible that looks terrible what am I doing wrong here um let me see

doing wrong here um let me see uh one thing we need to do is we want to

uh one thing we need to do is we want to get rid of these nulls and

get rid of these nulls and zeros do that really

zeros do that really quick um and then we don't want to do

quick um and then we don't want to do just the ID because I I'm realizing now

just the ID because I I'm realizing now uh what I'm doing I need to convert this

uh what I'm doing I need to convert this to a numeric so we can do a count on it

to a numeric so we can do a count on it so let's um oops let me see what what is

so let's um oops let me see what what is happening this is terrible all right

happening this is terrible all right let's put this back let's make let me

let's put this back let's make let me see if I can just um

see if I can just um do an

do an attribute let's

attribute let's do the

do the [Music]

[Music] count and let's

count and let's do

do text um no it needs to be a distinct

text um no it needs to be a distinct count because that's that's basically

count because that's that's basically like

like um a count of the numbers themselves not

um a count of the numbers themselves not each individual ID okay it took figuring

each individual ID okay it took figuring out I'm going to keep that in there

out I'm going to keep that in there because you guys need to see uh a lot of

because you guys need to see uh a lot of you guys like seeing when I make

you guys like seeing when I make mistakes so you know makes it feel like

mistakes so you know makes it feel like when you make mistakes it's okay um and

when you make mistakes it's okay um and I'm all about that so I'm leaving that

I'm all about that so I'm leaving that in there you guys can see me fail a

in there you guys can see me fail a little bit um I just forgot how to do

little bit um I just forgot how to do that for a second and this is exactly

that for a second and this is exactly what we're looking for right we want we

what we're looking for right we want we now it showed us in that visualization

now it showed us in that visualization that we were looking at earlier before

that we were looking at earlier before we um switched it to the average price

we um switched it to the average price this is showing us that there are for

this is showing us that there are for one bedrooms there's 1,800 one bedroom

one bedrooms there's 1,800 one bedroom two that 483 3 that have 206 four that

two that 483 3 that have 206 four that have 55 only five that have 20 and six

have 55 only five that have 20 and six that have five so the more you go up the

that have five so the more you go up the less and less it is or the less and less

less and less it is or the less and less competition there's going to be now is

competition there's going to be now is there a lot of demand for four-bedroom

there a lot of demand for four-bedroom five-bedroom six-bedroom uh that's for

five-bedroom six-bedroom uh that's for our friend to figure out um well maybe

our friend to figure out um well maybe we'll help them out with that later um

we'll help them out with that later um in the with the data you know we could

in the with the data you know we could look at the reviews that we had um

look at the reviews that we had um there's so much data in here and we

there's so much data in here and we could absolutely figure that out but for

could absolutely figure that out but for what it's worth giving him this initial

what it's worth giving him this initial stuff and he'll have follow-up questions

stuff and he'll have follow-up questions for us later that's how it always works

for us later that's how it always works I promise um so now we're good with this

I promise um so now we're good with this one let's label this one did I label the

one let's label this one did I label the last one I will go back and look um

last one I will go back and look um distinct I I'm going to butcher this one

distinct I I'm going to butcher this one I'm going do a distinct count

I'm going do a distinct count of of bedroom listings I don't that may

of of bedroom listings I don't that may not make sense at all but we're keeping

not make sense at all but we're keeping it so we're going to do bedroom apply

it so we're going to do bedroom apply okay let me see if I added the label on

okay let me see if I added the label on this one I didn't let me do that real

this one I didn't let me do that real quick we do

quick we do average price per

average price per bedroom again I'm

bedroom again I'm oops you didn't see that I'm just going

oops you didn't see that I'm just going with whatever is coming to my head this

with whatever is coming to my head this probably wouldn't be what I would keep

probably wouldn't be what I would keep if I this or like an actual project but

if I this or like an actual project but it works for now so we have our five

it works for now so we have our five visualizations 1 2 three four and five

visualizations 1 2 three four and five and let's create our dashboard that's

and let's create our dashboard that's going to be this button right here so

going to be this button right here so we're going to click that we are going

we're going to click that we are going to uh go right here and we're going to

to uh go right here and we're going to say automatic because we want to use

say automatic because we want to use this entire area and so now we're just

this entire area and so now we're just going to start um you know pulling them

going to start um you know pulling them over and I'm just going to start from

over and I'm just going to start from the very first one and go to the very

the very first one and go to the very last one keep it really simple so this

last one keep it really simple so this very first one we'll pull it over it you

very first one we'll pull it over it you know it's going to take up the entire

know it's going to take up the entire space until you start adding all the

space until you start adding all the other ones we'll include this one right

other ones we'll include this one right here um and well let's leave it as it is

here um and well let's leave it as it is you know we'll adjust it once it gets to

you know we'll adjust it once it gets to its final place now we have number three

its final place now we have number three We'll add this one on this side it looks

We'll add this one on this side it looks terrible right now but give it a second

terrible right now but give it a second uh then we have number four we're going

uh then we have number four we're going to add that across the top okay it's

to add that across the top okay it's already starting to look a little

already starting to look a little better and um maybe I I you don't have

better and um maybe I I you don't have to keep this in here

to keep this in here um but you definitely

um but you definitely can uh let's start to adjust things a

can uh let's start to adjust things a little

bit oops okay

oops okay let's see if I can zoom in one more NOP

let's see if I can zoom in one more NOP I'm going to do it just like that

I'm going to do it just like that actually let me

[Music] see if I can make it even just a little

see if I can make it even just a little bit closer perfect uh that's the the

bit closer perfect uh that's the the best you're going to get um if you

best you're going to get um if you didn't see I use this um magnifying and

didn't see I use this um magnifying and then I could click on the area that I

then I could click on the area that I wanted to see so we're going to keep

wanted to see so we're going to keep that just like

that just like that we're going to move this over

that we're going to move this over because that is um definitely not as

because that is um definitely not as important um and then we're going to

important um and then we're going to move this way over as well so keep it

move this way over as well so keep it just like that again this is something

just like that again this is something where if you want to you can click on

where if you want to you can click on this um it didn't I don't know why uh I

this um it didn't I don't know why uh I can't remember how to get those

can't remember how to get those connected but it's you definitely can um

connected but it's you definitely can um but okay I was just clicking on the

but okay I was just clicking on the wrong one that's

wrong one that's why that is why but you can click over

why that is why but you can click over here and you you know it'll filter um

here and you you know it'll filter um based on so if I go to this one oops

based on so if I go to this one oops [Music]

[Music] dang oh jeez what am I doing oh this is

dang oh jeez what am I doing oh this is a

a travesty okay let's try to get this

travesty okay let's try to get this back all right I'm not touching it guys

back all right I'm not touching it guys you get the gist you can mess around

you get the gist you can mess around with it yourself I'm not messing this up

with it yourself I'm not messing this up okay so the next thing we need to add is

okay so the next thing we need to add is the very last one that's going to go

the very last one that's going to go right up here and then we're just going

right up here and then we're just going to kind of move it off to the

to kind of move it off to the side

side and let's

and let's see going

add yeah have this caption um if you've never seen something like this

never seen something like this before um and I actually want to make

before um and I actually want to make this bigger as

this bigger as well oh jeez give me a second it's it's

well oh jeez give me a second it's it's kind of lagging a little

kind of lagging a little [Music]

bit and make this a little bit tall maybe I don't want it as wide but I

maybe I don't want it as wide but I definitely want a little

definitely want a little [Music]

[Music] taller give it a second yeah let me

taller give it a second yeah let me scooch this

scooch this [Music]

[Music] back just like that that's fine uh we

back just like that that's fine uh we can keep it like that in my original one

can keep it like that in my original one I didn't have this um um you can get rid

I didn't have this um um you can get rid of this if you want you know you can um

of this if you want you know you can um you know just exit out right here if you

you know just exit out right here if you want to do that but there you have it uh

want to do that but there you have it uh this is the entire thing so we started

this is the entire thing so we started from the very start um we started with

from the very start um we started with this one then this one uh did some um

this one then this one uh did some um and this is you know all the zip all of

and this is you know all the zip all of our ZIP code work then we took a look at

our ZIP code work then we took a look at the calendar where we looked at the

the calendar where we looked at the price and did some time series

price and did some time series visualization and then we're looking at

visualization and then we're looking at the bedrooms and and the count of

the bedrooms and and the count of bedrooms and so this should be really

bedrooms and so this should be really helpful for a friend it should be an

helpful for a friend it should be an initial dashboard to get him going and

initial dashboard to get him going and once he sees us he's going to have a

once he sees us he's going to have a million other questions and he's going

million other questions and he's going to want another dashboard for different

to want another dashboard for different data that's in there he's going to ask

data that's in there he's going to ask about okay well what if I want to do it

about okay well what if I want to do it weekly or you know I want to rent it out

weekly or you know I want to rent it out for the month or you know how many um

for the month or you know how many um reviews are people five star reviews are

reviews are people five star reviews are people giving on you know W bedroom two

people giving on you know W bedroom two bedroom three bedroom these are all

bedroom three bedroom these are all things that you know he may ask and then

things that you know he may ask and then we'd have to build out in the real world

we'd have to build out in the real world this is what happens all the time you

this is what happens all the time you know they make a request and then

know they make a request and then they're like oh this is great but I also

they're like oh this is great but I also want this so um you know your friend is

want this so um you know your friend is is going to be right in line with just

is going to be right in line with just about everyone else um that has ever

about everyone else um that has ever gotten a dashboard uh for work or for

gotten a dashboard uh for work or for personal use with that being said this

personal use with that being said this is it um we have done the entire thing

is it um we have done the entire thing now if you want to share this it is

now if you want to share this it is super super easy to share um and I'm

super super easy to share um and I'm going to try to remember how to share it

going to try to remember how to share it uh so we're going to do save to tap

uh so we're going to do save to tap public

public As and we're going to do this and we're

As and we're going to do this and we're going to make it um let's do Air BnB is

going to make it um let's do Air BnB is it like is it a capital B is it like

it like is it a capital B is it like that no that doesn't look right

that no that doesn't look right Airbnb uh we'll do full project and

Airbnb uh we'll do full project and we'll

we'll save and that is being created right now

save and that is being created right now um and I will save this so if you guys

um and I will save this so if you guys want to go look at this you can um and

want to go look at this you can um and I'll provide a link in the description

I'll provide a link in the description as well for that and see if yours looks

as well for that and see if yours looks um similar to mine or better than

um similar to mine or better than mine give it a second CU it's

thinking all right so here it is so here's our final our final project um

here's our final our final project um and if you followed step by step then

and if you followed step by step then you should get this exact or very very

you should get this exact or very very similar to this one again I encourage

similar to this one again I encourage you to if you want to have the upto-date

you to if you want to have the upto-date data to go to that um Link in the

data to go to that um Link in the description that has um the the most

description that has um the the most recent data and they update that I

recent data and they update that I believe monthly so you can go there get

believe monthly so you can go there get the most recent data and then you can do

the most recent data and then you can do stuff and you can create a beautiful

stuff and you can create a beautiful project just like this um but with the

project just like this um but with the you know the most recent data again I

you know the most recent data again I use the kaggle data just so you guys can

use the kaggle data just so you guys can remember and I encourage you to look at

remember and I encourage you to look at the different data points that are in

the different data points that are in the Excel there is so much in there and

the Excel there is so much in there and you can use uh honestly like there's

you can use uh honestly like there's probably 30 or 40 other fields that you

probably 30 or 40 other fields that you could be using in there that we never

could be using in there that we never even touched um but for this project

even touched um but for this project we're keeping it pretty simple and so so

we're keeping it pretty simple and so so go do that make completely unique

go do that make completely unique dashboards and and visualizations and

dashboards and and visualizations and create projects and add it to your

create projects and add it to your portfolios so that you can create uh a

portfolios so that you can create uh a fantastic portfolio website and get a

fantastic portfolio website and get a job and that's what this is all about um

job and that's what this is all about um it's about upskilling and and getting

it's about upskilling and and getting these skills that you can you know get a

these skills that you can you know get a job or or do better in your job so I

job or or do better in your job so I hope this has been helpful I really

hope this has been helpful I really appreciate you guys joining me and and

appreciate you guys joining me and and doing this entire project with me I have

doing this entire project with me I have no idea how long this is this probably

no idea how long this is this probably this could be like an hour for all I

this could be like an hour for all I know um so thank you so much for

know um so thank you so much for sticking with me this entire time if you

sticking with me this entire time if you like this video be sure to like And

like this video be sure to like And subscribe below and I will see you in

subscribe below and I will see you in the next

[Music] video what's going on everybody welcome

video what's going on everybody welcome back to another video today we're going

back to another video today we're going to be starting our powerbi tutorial

series now I am super excited to start this

now I am super excited to start this series with you guys we are going to be

series with you guys we are going to be breaking this up in about six or seven

breaking this up in about six or seven videos I don't really like those super

videos I don't really like those super long videos where it's like four hours

long videos where it's like four hours long I like breaking mine up into chunks

long I like breaking mine up into chunks so that's what we're going to do this is

so that's what we're going to do this is the beginner series and so we're going

the beginner series and so we're going to start with the very Basics and we're

to start with the very Basics and we're just going to work our way up and I'm

just going to work our way up and I'm going to walk you through every single

going to walk you through every single step of the way it'll be very easy to

step of the way it'll be very easy to follow everything will be provided for

follow everything will be provided for you so that all you have to do is really

you so that all you have to do is really follow along and by the end of it you

follow along and by the end of it you should know powerbi a lot better you

should know powerbi a lot better you should have a lot more com using it now

should have a lot more com using it now before we actually jump onto my screen I

before we actually jump onto my screen I want to give a huge shout out to the

want to give a huge shout out to the sponsor of this video and that is udemy

sponsor of this video and that is udemy you guys know that I absolutely love

you guys know that I absolutely love udemy I've been using them for years and

udemy I've been using them for years and that is no exception when it comes to

that is no exception when it comes to powerbi I have taken some of the best

powerbi I have taken some of the best powerbi courses ever on udemy so I

powerbi courses ever on udemy so I highly recommend you checking out the

highly recommend you checking out the ones that I have in the description

ones that I have in the description these are ones that I actually took and

these are ones that I actually took and I loved the most so if you're looking

I loved the most so if you're looking for a full powerbi course I highly

for a full powerbi course I highly recommend checking out you to me thank

recommend checking out you to me thank you so much again to our sponsor and now

you so much again to our sponsor and now without further Ado let's jump onto my

without further Ado let's jump onto my screen and get started with a tutorial

screen and get started with a tutorial all right so the first thing I'm going

all right so the first thing I'm going to do is download powerbi desktop I will

to do is download powerbi desktop I will leave this link in the description so

leave this link in the description so you can just click on it go to it and

you can just click on it go to it and download it we're going to click this

download it we're going to click this download free button and once we click

download free button and once we click it you can go to the Microsoft store and

it you can go to the Microsoft store and I already have it downloaded so when you

I already have it downloaded so when you see it uh it'll already say downloaded

see it uh it'll already say downloaded but um for you you can go in here you

but um for you you can go in here you can click download and it will download

can click download and it will download it for you I'm on Microsoft uh but it

it for you I'm on Microsoft uh but it may look a little bit different for you

may look a little bit different for you if you're on a different system but once

if you're on a different system but once that is done we are going to open up

that is done we are going to open up powerbi so let's go right down here to

powerbi so let's go right down here to our search let's go to

powerbi and it is going to open up for us all right so right away this is what

us all right so right away this is what it's going to look like when you open it

it's going to look like when you open it and we're going to go right over here to

and we're going to go right over here to get data and let's click on that it's

get data and let's click on that it's going to open up this window and it's

going to open up this window and it's going to give us a lot of different

going to give us a lot of different options for where we can get data from

options for where we can get data from now some of these are free and some you

now some of these are free and some you need to upgrade from but you just taking

need to upgrade from but you just taking a quick glance through here you have a

a quick glance through here you have a ton of options there's databases there's

ton of options there's databases there's um you know blob storages there's post

um you know blob storages there's post create SQL or different SQL databases um

create SQL or different SQL databases um there's Google analytics there's a lot

there's Google analytics there's a lot of places and you can go through the

of places and you can go through the process to connect to that data and you

process to connect to that data and you can pull that data in from those data

can pull that data in from those data sources now for what we are doing we're

sources now for what we are doing we're just going to be using an Excel I'm

just going to be using an Excel I'm going to leave the Excel that I'm going

going to leave the Excel that I'm going to be using in the description you can

to be using in the description you can go and download it and walk through this

go and download it and walk through this with me so what we're going to do is

with me so what we're going to do is click on Excel workbook and we're going

click on Excel workbook and we're going to click connect so we're going to go

to click connect so we're going to go right here in our powerbi tutorials

right here in our powerbi tutorials folder and we're going to click on

folder and we're going to click on apocalypse food prep so let's click on

apocalypse food prep so let's click on that and it is going to connect and pull

that and it is going to connect and pull that data in now right here we have our

that data in now right here we have our Navigator and so if you had a lot of

Navigator and so if you had a lot of different sheets you can click on that

different sheets you can click on that and choose which ones to pull in I just

and choose which ones to pull in I just clicked on it right over here and we're

clicked on it right over here and we're able to preview the data but I can't

able to preview the data but I can't load or transform it yet I need to

load or transform it yet I need to select which sheets I'm bringing in so

select which sheets I'm bringing in so we only have ones that's the only one

we only have ones that's the only one we're going to bring in so you can go

we're going to bring in so you can go ahead and load the data or you can click

ahead and load the data or you can click on transform data it's going to take us

on transform data it's going to take us to powerbi power query which is going to

to powerbi power query which is going to allow us to transform our data so I'm

allow us to transform our data so I'm going to have an entire video on how to

going to have an entire video on how to transform the data but I'm going to give

transform the data but I'm going to give you a really quick glance at it to kind

you a really quick glance at it to kind of show you what it is so right up here

of show you what it is so right up here it says our power query editor this is a

it says our power query editor this is a the window to basically transform your

the window to basically transform your data and get it ready for your

data and get it ready for your visualizations now you can do this in

visualizations now you can do this in Excel if you want to and do that before

Excel if you want to and do that before forand or you can do it here and there

forand or you can do it here and there are lots of things that we can do in

are lots of things that we can do in here as you can see at the top again

here as you can see at the top again I'll have an entire video dedicated to

I'll have an entire video dedicated to just power query but let's take a quick

just power query but let's take a quick look at the data and see if there's

look at the data and see if there's anything we want to transform quickly

anything we want to transform quickly before we actually go and start building

before we actually go and start building our

our visualizations so over here we have the

visualizations so over here we have the store where we purchased it we have the

store where we purchased it we have the product that we purchased the price that

product that we purchased the price that we paid and the date that we bought it

we paid and the date that we bought it now the first thing that jumps out to me

now the first thing that jumps out to me is that this just says date on it um we

is that this just says date on it um we might want to say date

might want to say date uncore purchased and we're going to hit

uncore purchased and we're going to hit enter and if you noticed right over here

enter and if you noticed right over here on these applied steps it says renamed

on these applied steps it says renamed columns everything that you do every

columns everything that you do every single step that you apply to transform

single step that you apply to transform this data is going to be right over here

this data is going to be right over here and if I want to if I go back and I say

and if I want to if I go back and I say you know I really didn't want to rename

you know I really didn't want to rename that column I can just click X and it is

that column I can just click X and it is going to get rid of that and take it

going to get rid of that and take it back to its original state so again I'm

back to its original state so again I'm just going to say purchase

just going to say purchase and we're going to enter that now this

and we're going to enter that now this is our apocalypse food prep so this is

is our apocalypse food prep so this is food that we are buying for the

food that we are buying for the apocalypse um for this example and if we

apocalypse um for this example and if we look at our products we have bottled

look at our products we have bottled water canned vegetables dried beans milk

water canned vegetables dried beans milk and rice and all of that stuff makes

and rice and all of that stuff makes sense except for the milk U milk will

sense except for the milk U milk will not stay or last long in the apocalypse

not stay or last long in the apocalypse so I think what we're going to do is

so I think what we're going to do is we're going to filter that out really

we're going to filter that out really quickly and we're GNA click okay and

quickly and we're GNA click okay and right over here again says filtered rows

right over here again says filtered rows and so now if we scroll down there's no

and so now if we scroll down there's no milk so what we are going to do is we

milk so what we are going to do is we are going to go over here to close and

are going to go over here to close and apply and it is going to actually load

apply and it is going to actually load the data into powerbi

the data into powerbi desktop so on this left- hand side it

desktop so on this left- hand side it immediately takes us to the report Tab

immediately takes us to the report Tab and what we want to do is go right here

and what we want to do is go right here to the data

to the data Tab and take a look at our data so again

Tab and take a look at our data so again there's our date purchased and as you

there's our date purchased and as you can see the milk is not in there another

can see the milk is not in there another tab that we're going to take a look at

tab that we're going to take a look at um and again in this report tab this is

um and again in this report tab this is where we actually build our

where we actually build our visualizations the data is where we can

visualizations the data is where we can see the data and and change it up a

see the data and and change it up a little bit and change some small things

little bit and change some small things about it like sorting The Columns or

about it like sorting The Columns or even creating a new column and over here

even creating a new column and over here we have this other Tab and is called

we have this other Tab and is called model and this is especially useful when

model and this is especially useful when you have multiple tables or multiple

you have multiple tables or multiple excels and you need to join them to kind

excels and you need to join them to kind of connect them together we don't have

of connect them together we don't have that but in a future video I'm going to

that but in a future video I'm going to walk through how to use this entire

walk through how to use this entire higher tab so now let's go back to the

higher tab so now let's go back to the data Tab and I want to just look at the

data Tab and I want to just look at the data really quickly before we go over to

data really quickly before we go over to the report Tab and we start building our

the report Tab and we start building our first visualization as you can see I've

first visualization as you can see I've been buying these different products in

been buying these different products in different months so this rice I've been

different months so this rice I've been purchasing in January February March and

purchasing in January February March and April and I've been buying it from three

April and I've been buying it from three different locations because I wanted to

different locations because I wanted to see if I was spending less money at one

see if I was spending less money at one location on all of the products so then

location on all of the products so then I would just shop there in the future

I would just shop there in the future and save a lot of money or if there were

and save a lot of money or if there were specific products that were really cheap

specific products that were really cheap at one location but others they were

at one location but others they were cheaper at a different location so I

cheaper at a different location so I should just buy like the dried beans at

should just buy like the dried beans at Costco but everything else I should be

Costco but everything else I should be buying at Walmart and so that's what

buying at Walmart and so that's what we're going to look at in just a little

we're going to look at in just a little bit so let's go over to the report tab

bit so let's go over to the report tab right up here at the top there's this

right up here at the top there's this data section so you can kind of choose

data section so you can kind of choose if you want to add any more data now

if you want to add any more data now that we are here we can also write

that we are here we can also write queries or transform the data like we

queries or transform the data like we were looking at in the power query

were looking at in the power query editor window over here in the insert we

editor window over here in the insert we can add a new visualization or a text

can add a new visualization or a text box and then in the calculation section

box and then in the calculation section we we can create a new measure or a

we we can create a new measure or a quick measure and then over here we have

quick measure and then over here we have share where you can actually publish

share where you can actually publish your report or your dashboard online now

your report or your dashboard online now over on the visualization section on

over on the visualization section on this far right this is a very important

this far right this is a very important area this is where a lot of the actual

area this is where a lot of the actual creating of the dashboards happen so

creating of the dashboards happen so let's take a look really quick and we'll

let's take a look really quick and we'll get into a lot of these things as we're

get into a lot of these things as we're actually building our dashboard so we're

actually building our dashboard so we're not just sitting here looking and

not just sitting here looking and talking we're going to be actually

talking we're going to be actually building and doing all right so we're

building and doing all right so we're going to click right here on this drop

going to click right here on this drop down on sheet one it's going to show us

down on sheet one it's going to show us all of our columns now two of the things

all of our columns now two of the things that we wanted to look at were where are

that we wanted to look at were where are we spending the least amount of money

we spending the least amount of money buying the exact same product that'll

buying the exact same product that'll help us determine where we want to shop

help us determine where we want to shop and the second thing was should I be

and the second thing was should I be buying all my products at the same place

buying all my products at the same place or are there certain products that

or are there certain products that they're going to be cheaper at a

they're going to be cheaper at a specific store and I should buy it there

specific store and I should buy it there so let's start out with the first one

so let's start out with the first one which we're just going to see uh with

which we're just going to see uh with the store and the

the store and the price uh where we're spending the least

price uh where we're spending the least amount of money and just at a quick

amount of money and just at a quick glance we can see we're spending the

glance we can see we're spending the least amount of money at Costco at $210

least amount of money at Costco at $210 versus Target 219 and Walmart at 225 and

versus Target 219 and Walmart at 225 and that really answers our question but we

that really answers our question but we want to visualize it better be able to

want to visualize it better be able to see it in an easier way so we're going

see it in an easier way so we're going to go right over here and we can click

to go right over here and we can click on a lot of these but the one that

on a lot of these but the one that probably makes the most sense is the

probably makes the most sense is the stocked column

stocked column chart and it's going to show Walmart

chart and it's going to show Walmart Target and Costco now they're all the

Target and Costco now they're all the same color let's add a legend so we're

same color let's add a legend so we're just going to drag store over here down

just going to drag store over here down to this Legend and let's make this

to this Legend and let's make this larger while we're working on it so now

larger while we're working on it so now we can see we're spending the most

we can see we're spending the most amount of money at Walmart right in

amount of money at Walmart right in between at Target and then at Costco is

between at Target and then at Costco is the lowest and so right there we know

the lowest and so right there we know that Costco is the place to go for our

that Costco is the place to go for our apocalypse food prep but is it going to

apocalypse food prep but is it going to be that way for every product I don't

be that way for every product I don't know let's take a look let's put this up

know let's take a look let's put this up in this corner and let's start a new one

in this corner and let's start a new one we're going to need to select the

we're going to need to select the product for sure and the price and

product for sure and the price and probably Additionally the store as well

probably Additionally the store as well and let's click

and let's click on let's not do this one we need a

on let's not do this one we need a clustered column chart that's what we

clustered column chart that's what we need let's bring this over here let's

need let's bring this over here let's expand this quite a bit and so really at

expand this quite a bit and so really at a glance this is giving us everything

a glance this is giving us everything that we need we can see each product

that we need we can see each product right here and we can see how much we're

right here and we can see how much we're paying per store and so for Rice we're

paying per store and so for Rice we're paying it looks like a lot more for our

paying it looks like a lot more for our rice at Walmart while at Target is

rice at Walmart while at Target is actually where we are paying the least

actually where we are paying the least now if we look at all of these it looks

now if we look at all of these it looks like for Costco the only one that we're

like for Costco the only one that we're really paying a lot more on is on our

really paying a lot more on is on our rice but for our dried beans our bottled

rice but for our dried beans our bottled water we're paying quite a bit less and

water we're paying quite a bit less and really it's pretty negligible for these

really it's pretty negligible for these canned vegetables we're paying maybe

canned vegetables we're paying maybe what 60 cents 50 60 cents more per can

what 60 cents 50 60 cents more per can so that's pretty negligible but for the

so that's pretty negligible but for the big ticket items um we're really

big ticket items um we're really spending a lot less at Costco if we

spending a lot less at Costco if we wanted to SP to save just a little bit

wanted to SP to save just a little bit more money we could go to Target for our

more money we could go to Target for our rice now if I want to make this more

rice now if I want to make this more like a dashboard and we're only keeping

like a dashboard and we're only keeping these two things I'm going to kind of

these two things I'm going to kind of size them kind of like this whoops going

size them kind of like this whoops going to show you that in a little bit I'm

to show you that in a little bit I'm going to size them a little bit like

going to size them a little bit like this so now that we have that looking

this so now that we have that looking good we want to change the title of both

good we want to change the title of both of these so what we're going to do is go

of these so what we're going to do is go over here in our visualizations and

over here in our visualizations and format your visual uh and we are going

format your visual uh and we are going to go to this General go to Ty TI and

to go to this General go to Ty TI and now we can name it anything we really

now we can name it anything we really want for this we're going to say best

want for this we're going to say best store for

store for product and while we're in here one

product and while we're in here one other thing that I wanted to do is I

other thing that I wanted to do is I want to go to this visual go right down

want to go to this visual go right down here to these data labels now we haven't

here to these data labels now we haven't added any data labels so I'm going to

added any data labels so I'm going to click on and you'll see exactly what it

click on and you'll see exactly what it does uh it just puts the labels and the

does uh it just puts the labels and the numbers above it so you don't have to

numbers above it so you don't have to actually like hover over it and see what

actually like hover over it and see what it is now it is actually rounding these

it is now it is actually rounding these numbers so what we're going to do is go

numbers so what we're going to do is go down here we're going to go down to

down here we're going to go down to values and we'll go down to display

values and we'll go down to display units and it's on auto so it's Auto

units and it's on auto so it's Auto rounding those numbers and we're just

rounding those numbers and we're just going to say none so we can see the

going to say none so we can see the actual value of these

actual value of these numbers and we can do the exact same

numbers and we can do the exact same thing over here it probably is a good

thing over here it probably is a good thing to do um and it just is going to

thing to do um and it just is going to visualize it a little bit differently in

visualize it a little bit differently in here but you can always change that if

here but you can always change that if you want to go over here to

you want to go over here to title and we're going to say total by

title and we're going to say total by store and now we're going to take a look

store and now we're going to take a look and so in a matter of minutes we were

and so in a matter of minutes we were able to take our data from an Excel put

able to take our data from an Excel put it into powerbi transform it a little

it into powerbi transform it a little bit then we're able to create these

bit then we're able to create these visualizations that gave us concrete

visualizations that gave us concrete answers to some very important topics we

answers to some very important topics we now know that Costco is the place to go

now know that Costco is the place to go for basically every single product

for basically every single product except if we're buying rice and if we

except if we're buying rice and if we want to save just a few dollars we're

want to save just a few dollars we're going to head over to Target and that's

going to head over to Target and that's genuinely going to change my shopping

genuinely going to change my shopping habits for the next several years until

habits for the next several years until the apocalypse happens so in future

the apocalypse happens so in future videos we're going to dive into a lot of

videos we're going to dive into a lot of the things that we looked at today but

the things that we looked at today but just in more detail and then at the very

just in more detail and then at the very end of the series we're going to have an

end of the series we're going to have an entire project where we really use every

entire project where we really use every single part of powerbi and create a

single part of powerbi and create a beautiful dashboard and so that's all we

beautiful dashboard and so that's all we have for our very first video in our

have for our very first video in our powerbi series I hope it was helpful if

powerbi series I hope it was helpful if you like this video be sure to like And

you like this video be sure to like And subscribe below and I'll see you in the

subscribe below and I'll see you in the next

next video

video [Music]

[Music] what's going on everybody today we're

what's going on everybody today we're continuing our powerbi tutorial series

continuing our powerbi tutorial series and in this video we're going to be

and in this video we're going to be looking at Power

looking at Power [Music]

[Music] query Now power query is really great

query Now power query is really great because it allows you to actually

because it allows you to actually transform the data before you actually

transform the data before you actually get it into powerbi so if you want to

get it into powerbi so if you want to make any changes like adding or deleting

make any changes like adding or deleting a column or changing the data type or a

a column or changing the data type or a ton of other things you can do all of

ton of other things you can do all of that in power query now without further

that in power query now without further Ado let's jump on my screen and get

Ado let's jump on my screen and get started with the tutorial all right so

started with the tutorial all right so before we jump over to powerbi and start

before we jump over to powerbi and start using power query I wanted to take a

using power query I wanted to take a look at the data and this is the Excel

look at the data and this is the Excel from our last video called apocalypse

from our last video called apocalypse food prep and in that video we went

food prep and in that video we went through and we bought some rice some

through and we bought some rice some beans water vegetables and milk all for

beans water vegetables and milk all for the apocalypse getting prepared for that

the apocalypse getting prepared for that now we decided to buy some additional

now we decided to buy some additional things like rope some flashlights duct

things like rope some flashlights duct tape and a water filter several water

tape and a water filter several water filters and after we purchased those uh

filters and after we purchased those uh our boss or whoever we're working with

our boss or whoever we're working with or somebody decided to go and make a

or somebody decided to go and make a pivot table now in this pivot table they

pivot table now in this pivot table they kind of broke it out by Costco Target

kind of broke it out by Costco Target and Walmart and had all the items had

and Walmart and had all the items had some subtotals as well as some Grand

some subtotals as well as some Grand totals right here and then they decided

totals right here and then they decided to kind of copy and paste that into this

to kind of copy and paste that into this and you'll see this a lot when you're

and you'll see this a lot when you're working with uh people who use Excel

working with uh people who use Excel they like to kind of make things like

they like to kind of make things like this maybe make it into like a table or

this maybe make it into like a table or or format a little bit differently but

or format a little bit differently but you'll see stuff like this a lot so this

you'll see stuff like this a lot so this is what we're going to actually pull

is what we're going to actually pull into Power query and work with now we're

into Power query and work with now we're going to imagine that this is all we

going to imagine that this is all we have this is the only thing we were

have this is the only thing we were working with and I'll kind of reference

working with and I'll kind of reference this pivot table a little bit but we're

this pivot table a little bit but we're going to pretend this is all we have and

going to pretend this is all we have and we want to transform it to make it a lot

we want to transform it to make it a lot more usable to where we can make

more usable to where we can make visualizations with it so let's hop over

visualizations with it so let's hop over to powerbi and pull this excel in so

to powerbi and pull this excel in so what we're going to do is click import

what we're going to do is click import data from Excel we're going to click

data from Excel we're going to click apocalypse food prep and click open and

apocalypse food prep and click open and then it's going to bring up this window

then it's going to bring up this window right here now this is where we can

right here now this is where we can choose what data to bring in so we can

choose what data to bring in so we can take a preview and just click on it real

take a preview and just click on it real quick and this is the pivot table that

quick and this is the pivot table that we were looking at so it does have that

we were looking at so it does have that pivot table so we are able to pull in

pivot table so we are able to pull in just a pivot table and then we have the

just a pivot table and then we have the purchase overview where it's kind of

purchase overview where it's kind of that formatted um thing that we're just

that formatted um thing that we're just looking at with all the colors we're

looking at with all the colors we're going to pull both of those in so we're

going to pull both of those in so we're going to pull in the pivot table and the

going to pull in the pivot table and the purchase overview now we could just load

purchase overview now we could just load it or we could transform it and we're

it or we could transform it and we're going to click transform and that's

going to click transform and that's going to bring us to power query so

going to bring us to power query so let's click on transform data so now

let's click on transform data so now really quick before we actually jump

really quick before we actually jump into working through this and

into working through this and transforming it I want to show you what

transforming it I want to show you what the power query editor looks like so if

the power query editor looks like so if we go right over here we have our

we go right over here we have our queries and these are the tables that we

queries and these are the tables that we actually pulled in and we can click on

actually pulled in and we can click on those and kind of go back and forth

those and kind of go back and forth between them now up top we have our

between them now up top we have our ribbon and the ribbon offers a lot of

ribbon and the ribbon offers a lot of functionality we have things like remove

functionality we have things like remove columns keep rows remove rows split

columns keep rows remove rows split columns these are all things that we're

columns these are all things that we're likely to use when using this power

likely to use when using this power query editor there's also another tab

query editor there's also another tab called transform where there's a lot of

called transform where there's a lot of functionality here as well things like

functionality here as well things like unpivoting a column or transposing

unpivoting a column or transposing columns and rows and using a first row

columns and rows and using a first row as a header some of the things that

as a header some of the things that we'll be looking at today there's also

we'll be looking at today there's also another tab called add a column and this

another tab called add a column and this one's pretty self-explanatory where you

one's pretty self-explanatory where you can add additional columns like deleting

can add additional columns like deleting a column creating an index column or a

a column creating an index column or a conditional column those are the three

conditional column those are the three main ones there's also view tools and

main ones there's also view tools and help but we're not going to really be

help but we're not going to really be looking at those today and then on the

looking at those today and then on the far right side we have our query

far right side we have our query settings you can do things like change

settings you can do things like change the name so we call it pivot table

the name so we call it pivot table 2022 and it'll update right over here on

2022 and it'll update right over here on our query side and we have our applied

our query side and we have our applied steps now our applied steps are

steps now our applied steps are extremely important and very very useful

extremely important and very very useful anytime we make any change to transform

anytime we make any change to transform this data it's going to be documented

this data it's going to be documented right here and then we can go back and

right here and then we can go back and look at it or we could even delete that

look at it or we could even delete that change in the future if we want to and

change in the future if we want to and go back to a previous version of what we

go back to a previous version of what we just did so when we loaded the data into

just did so when we loaded the data into powerbi it did a few things for us it

powerbi it did a few things for us it shows the source the navigation and it

shows the source the navigation and it promoted the headers and then it also

promoted the headers and then it also changed the data type so if we want to

changed the data type so if we want to check we can actually see those things

check we can actually see those things or change those things like this Source

or change those things like this Source right here we can click on this little

right here we can click on this little icon and it's going to bring up the

icon and it's going to bring up the actual path where we got this file so if

actual path where we got this file so if we wanted to change that or or it

we wanted to change that or or it changes in the future future we can come

changes in the future future we can come here and we can change this file path

here and we can change this file path but we're not going to do that right now

but we're not going to do that right now so let's click on cancel and let's go

so let's click on cancel and let's go back down to change type so it promoted

back down to change type so it promoted these headers and obviously these

these headers and obviously these headers are not correct we're looking at

headers are not correct we're looking at this pivot table and not the purchase

this pivot table and not the purchase overview but it changed these column

overview but it changed these column headers and so in the future if we

headers and so in the future if we wanted to we could easily change those

wanted to we could easily change those but it did that for us and it changed

but it did that for us and it changed the type as well so if you look right

the type as well so if you look right here it says

here it says abc123 all the way over here it's where

abc123 all the way over here it's where it just says ABC ABC means it's only

it just says ABC ABC means it's only going to be text where abc123 means it

going to be text where abc123 means it could be basically anything uh text or

could be basically anything uh text or it could be numeric so now let's go over

it could be numeric so now let's go over to purchase overview and this is the one

to purchase overview and this is the one that we're actually going to be working

that we're actually going to be working on the most but we might be looking at

on the most but we might be looking at pivot table just a little bit to kind of

pivot table just a little bit to kind of reference it and see some of the

reference it and see some of the differences so before we do anything

differences so before we do anything let's just take a look at how powerbi

let's just take a look at how powerbi decided to take this data in so it chose

decided to take this data in so it chose this apocalypse food prep overview as

this apocalypse food prep overview as kind of the First Column and that was

kind of the First Column and that was kind of our header or the title of what

kind of our header or the title of what we were looking at before and then all

we were looking at before and then all these other columns are basically column

these other columns are basically column 1 2 3 four fivs so that's something that

1 2 3 four fivs so that's something that we're going to want to change in just a

we're going to want to change in just a little bit there's also all these blank

little bit there's also all these blank uh columns right here at the top and

uh columns right here at the top and kind of these null values as we go along

kind of these null values as we go along and we'll take a look at those and we

and we'll take a look at those and we kind of we going to want to get rid of

kind of we going to want to get rid of some of this and just clean this up to

some of this and just clean this up to make it more usable for our powerbi

make it more usable for our powerbi visualizations this may be perfectly

visualizations this may be perfectly fine and acceptable in an Excel but when

fine and acceptable in an Excel but when you're pulling it into powerbi the real

you're pulling it into powerbi the real reason you're pulling it in is to create

reason you're pulling it in is to create visualizations not just it to look good

visualizations not just it to look good in an Excel so we're going to need to

in an Excel so we're going to need to clean this up quite a bit so let's go

clean this up quite a bit so let's go right up top the first thing that I want

right up top the first thing that I want to do is I want to get rid of these top

to do is I want to get rid of these top rows so we're going to go to this top

rows so we're going to go to this top ribbon and we're going to click remove

ribbon and we're going to click remove rows and we're going to select remove

rows and we're going to select remove top rows and we're going to select two

top rows and we're going to select two because we have one two rows of all

because we have one two rows of all nulls and those are completely useless

nulls and those are completely useless we just want to get rid of them right

we just want to get rid of them right away so let's cck Okay and it removed

away so let's cck Okay and it removed those the next thing that we want to do

those the next thing that we want to do is these this location product and all

is these this location product and all these dates these are actually the

these dates these are actually the column headers that we wanted so what we

column headers that we wanted so what we need to do now is we want to go over to

need to do now is we want to go over to transform and we want to say use first

transform and we want to say use first row as

row as headers and just like that we have

headers and just like that we have location products and these dates as our

location products and these dates as our headers exactly how we wanted them now

headers exactly how we wanted them now let's say for whatever reason you know

let's say for whatever reason you know we made a mistake and we needed to go

we made a mistake and we needed to go back we would just select remove top

back we would just select remove top rows and that would be perfectly fine

rows and that would be perfectly fine now you can see over here it promoted

now you can see over here it promoted the headers but it's also changed the

the headers but it's also changed the data type so before if we went to before

data type so before if we went to before we removed the headers these were all

we removed the headers these were all abc123 abc123 because it had a lot of

abc123 abc123 because it had a lot of different data types in there so it just

different data types in there so it just kind of made a generic data type but

kind of made a generic data type but when we promoted these headers the first

when we promoted these headers the first thing that it decided to do was also

thing that it decided to do was also change this data type for us giving us

change this data type for us giving us its best guess as to what this data type

its best guess as to what this data type is and it decided to do this decimal so

is and it decided to do this decimal so this one two is a decimal but we're

this one two is a decimal but we're actually going to change that and all

actually going to change that and all you have to do is click on This 1.2 uh

you have to do is click on This 1.2 uh or or the data type that it has right

or or the data type that it has right here for you and we're going to click on

here for you and we're going to click on fixed decimal number and let's do

fixed decimal number and let's do replace

replace current and now it's just a little bit

current and now it's just a little bit better so now it's 2.70 2.5 and that's

better so now it's 2.70 2.5 and that's normally how we would read uh values

normally how we would read uh values like this because this is money so we

like this because this is money so we would normally read it to the second

would normally read it to the second decimal just like that and if we have it

decimal just like that and if we have it on the second decimal for some we should

on the second decimal for some we should probably have it on the second decimal

probably have it on the second decimal for all all of them so really quickly

for all all of them so really quickly I'm going to go through and I'm just

I'm going to go through and I'm just going to change that and it should be

going to change that and it should be pretty quick so hang with me for just a

pretty quick so hang with me for just a second all right that is perfect now for

second all right that is perfect now for the purposes of what we're about to do

the purposes of what we're about to do we don't actually need these subtotals

we don't actually need these subtotals or this Costco total Target total and

or this Costco total Target total and Walmart total as well as the grand total

Walmart total as well as the grand total really we want to get rid of those and

really we want to get rid of those and so what we're going to do is we're going

so what we're going to do is we're going to go right over here we're going to

to go right over here we're going to click on this drop down and we're going

click on this drop down and we're going to try to filter this data before we

to try to filter this data before we actually load it into power VI so we're

actually load it into power VI so we're going to filter and we're going to say

going to filter and we're going to say remove empty and let's remove those and

remove empty and let's remove those and it's going to take out all of those

it's going to take out all of those nulls if we wanted to try to filter this

nulls if we wanted to try to filter this out by saying something like Costco

out by saying something like Costco total or Target total we could do that

total or Target total we could do that by going right here clicking this drop

by going right here clicking this drop town on products going to text filters

town on products going to text filters and saying does not contain and let's do

and saying does not contain and let's do insert and we're going to say does not

insert and we're going to say does not contain and we want to say total and

contain and we want to say total and let's click okay okay and again it

let's click okay okay and again it filtered out all of those things so

filtered out all of those things so there's a few different options that you

there's a few different options that you can do if you want to filter out rows

can do if you want to filter out rows that contain either null values or

that contain either null values or specific values now the next thing that

specific values now the next thing that we're going to do is actually get rid of

we're going to do is actually get rid of a column this grand total column and so

a column this grand total column and so what we're going to do is we're going to

what we're going to do is we're going to click on the very top part where it says

click on the very top part where it says grand total we're going to go back over

grand total we're going to go back over here to home and we're going to click on

here to home and we're going to click on remove columns and it says insert that's

remove columns and it says insert that's because we're on this filtered rows one

because we're on this filtered rows one right here um but what we're going to do

right here um but what we're going to do is just insert that and it'll insert

is just insert that and it'll insert right there that's totally fine we can

right there that's totally fine we can just move it to the bottom now we got

just move it to the bottom now we got rid of this column entirely now this

rid of this column entirely now this looks really good visually I like how

looks really good visually I like how this looks I like how everything is set

this looks I like how everything is set up the biggest thing about this is that

up the biggest thing about this is that when you're actually wanting to use this

when you're actually wanting to use this for visualizations these columns as

for visualizations these columns as dates doesn't really work too well and

dates doesn't really work too well and so what we're going to want to do is

so what we're going to want to do is we're going to want to transpose this or

we're going to want to transpose this or pivot this to where these dates are

pivot this to where these dates are actually rows so what we're going to do

actually rows so what we're going to do is select the first date which is

is select the first date which is January 1st all the way through April

January 1st all the way through April 1st and we're going to hit shift and

1st and we're going to hit shift and click on that April 1st right there to

click on that April 1st right there to select all of them at the same time and

select all of them at the same time and then we're going to go over here to the

then we're going to go over here to the transform Tab and we're going to click

transform Tab and we're going to click unpivot columns and let's see what this

unpivot columns and let's see what this does and so now what we've done is we've

does and so now what we've done is we've basically recreated our original Excel

basically recreated our original Excel that we had so let's go back and take a

that we had so let's go back and take a look really quickly at that so this

look really quickly at that so this looks almost identical to what we have

looks almost identical to what we have in powerbi right now and this is

in powerbi right now and this is extremely usable and very good for

extremely usable and very good for visualization

visualization and is much much better than this but

and is much much better than this but again we were pretending that this is

again we were pretending that this is what we were given at the beginning so

what we were given at the beginning so you have to imagine you know somebody

you have to imagine you know somebody just handing you this and you need to

just handing you this and you need to make it much more usable for

make it much more usable for visualizations in the future which

visualizations in the future which happens a lot and we actually wanted to

happens a lot and we actually wanted to create this we just weren't given this

create this we just weren't given this now a few last things that we might want

now a few last things that we might want to do is we want to clean this up just a

to do is we want to clean this up just a little bit we're going to select the

little bit we're going to select the data type and change this to date and

data type and change this to date and then we're going to select the value and

then we're going to select the value and I double clicked on the value and I

I double clicked on the value and I actually want to call this cost uh or

actually want to call this cost uh or product cost

product cost productor

productor cost and then for the location I

cost and then for the location I actually want this to be called

actually want this to be called store so now this looks really good but

store so now this looks really good but I want to show you one thing really

I want to show you one thing really quickly on this pivot table 2022 so

quickly on this pivot table 2022 so let's go back here this looks very

let's go back here this looks very similar to how we had it when it first

similar to how we had it when it first started one thing I wanted to show you

started one thing I wanted to show you uh really quickly and I want to click on

uh really quickly and I want to click on this first one we're going to make make

this first one we're going to make make this our column header and then we're

this our column header and then we're going to try to Pivot or unpivot this

going to try to Pivot or unpivot this January February March April so really

January February March April so really quickly let's do that so we're going to

quickly let's do that so we're going to transform use first row as

transform use first row as headers so now we have this January

headers so now we have this January February March April now if you notice

February March April now if you notice these are not dates these are actually

these are not dates these are actually texts it says January February March and

texts it says January February March and April so if we go to do this and we

April so if we go to do this and we click

click unpivot and here's the columns that are

unpivot and here's the columns that are cre cre when we unpivot it it is January

cre cre when we unpivot it it is January February March and April these are not

February March and April these are not dates so we cannot go and change this to

dates so we cannot go and change this to a date because that would error out

a date because that would error out because it's actually text so it's

because it's actually text so it's something that you want to look out for

something that you want to look out for it's something that you need to be aware

it's something that you need to be aware of and you can change that in the pivot

of and you can change that in the pivot table so you want to be aware of how it

table so you want to be aware of how it actually sits and looks in the Excel or

actually sits and looks in the Excel or whatever data source you're pulling from

whatever data source you're pulling from before you actually pull it into Power

before you actually pull it into Power query to transform and now the very last

query to transform and now the very last thing that we need to do to finalize all

thing that we need to do to finalize all of this is go over here to close and

of this is go over here to close and apply and once we click that everything

apply and once we click that everything that we've worked on is going to be

that we've worked on is going to be applied to the actual data and it's

applied to the actual data and it's going to load into powerbi to create our

going to load into powerbi to create our visualizations so let's go ahead and

visualizations so let's go ahead and click on that and so now the data has

click on that and so now the data has been pulled into powerbi let's go right

been pulled into powerbi let's go right down here to data and we can see the

down here to data and we can see the data right here if we need to transform

data right here if we need to transform this data again we can bring it back

this data again we can bring it back into the power query editor window by

into the power query editor window by just clicking the transform data button

just clicking the transform data button and it's going to bring us right back so

and it's going to bring us right back so I hope that this was helpful thank you

I hope that this was helpful thank you so much for watching if you like this

so much for watching if you like this video like And subscribe below and check

video like And subscribe below and check out all my other videos and everything

out all my other videos and everything data analyst related I'll see you in the

data analyst related I'll see you in the next

next [Music]

[Music] video what's going on everybody welcome

video what's going on everybody welcome back to the powerbi tutorial Series

back to the powerbi tutorial Series today we're going to be taking a look at

today we're going to be taking a look at building

[Music] relationships now when you import

relationships now when you import multiple tables from either the same

multiple tables from either the same data source or multiple data sources you

data source or multiple data sources you want to tie them together so that when

want to tie them together so that when you're creating your visualizations

you're creating your visualizations everything is connected so in this

everything is connected so in this tutorial we'll be walking through how to

tutorial we'll be walking through how to create those relationships to make sure

create those relationships to make sure that all of your tables are connected

that all of your tables are connected properly and without further Ado let's

properly and without further Ado let's jump onto my screen and get started with

jump onto my screen and get started with the tutorial all right so before we jump

the tutorial all right so before we jump over to powerbi and start creating our

over to powerbi and start creating our relationships and our model I want to

relationships and our model I want to take a look at the data in Excel we

take a look at the data in Excel we realized we were buying so many products

realized we were buying so many products for the apocalypse that we decided to

for the apocalypse that we decided to start our own store and we have several

start our own store and we have several customers and some client information

customers and some client information down here and so I wanted to take a look

down here and so I wanted to take a look at some of the columns and these tables

at some of the columns and these tables that we're going to be looking at first

that we're going to be looking at first thing we have is the apocalypse store

thing we have is the apocalypse store these are the things that we are selling

these are the things that we are selling I know it's a very limited inventory but

I know it's a very limited inventory but these are the really high sellers these

these are the really high sellers these are the ones that I wanted to sell so we

are the ones that I wanted to sell so we have this product ID our product name

have this product ID our product name price and production cost then we have

price and production cost then we have this apocalypse sales this is how many

this apocalypse sales this is how many sales we've actually made to our

sales we've actually made to our customers so we have this customer ID

customers so we have this customer ID our customer name product ID order ID

our customer name product ID order ID unit sold and the date it was purchased

unit sold and the date it was purchased and then we have our customer

and then we have our customer information right here here are all of

information right here here are all of our clients so we have this customer ID

our clients so we have this customer ID customer address city state and zip code

customer address city state and zip code so now that we've taken a look at our

so now that we've taken a look at our data let's go and load it into powerbi

data let's go and load it into powerbi so we're going to say import data from

so we're going to say import data from Excel we're going to choose this model

Excel we're going to choose this model right here we're going to click open and

right here we're going to click open and we are going to want all three of these

we are going to want all three of these so I'm going to click on all of them and

so I'm going to click on all of them and we're just going to load it we're not

we're just going to load it we're not going to transform the data at

all so now the data has been loaded let's go right over here on the left

let's go right over here on the left hand side to our model Tab and let's

hand side to our model Tab and let's scoot this over just a little bit and

scoot this over just a little bit and move back and we're going to move these

move back and we're going to move these tables up to where it's a little bit

tables up to where it's a little bit easier to

easier to see so right off the bat you can already

see so right off the bat you can already see that there are these lines between

see that there are these lines between these tables so there are already

these tables so there are already relationships that powerbi has

relationships that powerbi has automatically detected and created from

automatically detected and created from my experience powerbi actually does a

my experience powerbi actually does a really good job at creating these

really good job at creating these relationships automatically but we're

relationships automatically but we're going to go in and take a look at these

going to go in and take a look at these and kind of see what everything means

and kind of see what everything means and then we're going to go back and

and then we're going to go back and create these relationships from scratch

create these relationships from scratch just to make sure that we know how to do

just to make sure that we know how to do every single part so to get it started

every single part so to get it started let's double click on this line

let's double click on this line connecting the customer information

connecting the customer information table to the apocalypse sales

table to the apocalypse sales table and it's going to bring up this

table and it's going to bring up this edit relationship page right here so

edit relationship page right here so this line right here connecting these

this line right here connecting these two tables actually gives us quite a bit

two tables actually gives us quite a bit of information without actually having

of information without actually having to click into this edit relationship

to click into this edit relationship page what this is showing is that we

page what this is showing is that we have a one to many relationship and

have a one to many relationship and there's only one or a single crossfilter

there's only one or a single crossfilter direction and you can find both of those

direction and you can find both of those things right down here and I'm going to

things right down here and I'm going to walk through what those mean in just a

walk through what those mean in just a little bit on this page you can also see

little bit on this page you can also see the columns that powerbi decided to

the columns that powerbi decided to choose in order to tie these two tables

choose in order to tie these two tables together now for our example they

together now for our example they decided to use the customer and customer

decided to use the customer and customer right here from the customer information

right here from the customer information table as well as the apocal sales but I

table as well as the apocal sales but I don't really want to use those

don't really want to use those specifically because on this apocalypse

specifically because on this apocalypse sales table I might remove this customer

sales table I might remove this customer information and just keep the customer

information and just keep the customer ID it may have chosen these customer

ID it may have chosen these customer columns because they have the exact same

columns because they have the exact same name and really the same information but

name and really the same information but I want to use this customer ID anyways

I want to use this customer ID anyways so what I'm going to do is I'm going to

so what I'm going to do is I'm going to click on that column and click on this

click on that column and click on this column and then I'm going to click okay

column and then I'm going to click okay and if we go back into it by double

and if we go back into it by double clicking again we're going to see that

clicking again we're going to see that and now save that and if we did what we

and now save that and if we did what we just did before which is kind of hover

just did before which is kind of hover over it it's going to show us what those

over it it's going to show us what those two tables are joined on so opening this

two tables are joined on so opening this back up let's go down here to this

back up let's go down here to this cardinality and cross filter Direction

cardinality and cross filter Direction cardinality has several different

cardinality has several different options that you can choose from you

options that you can choose from you have one to many one to one one to many

have one to many one to one one to many and many to many now for this example

and many to many now for this example we're looking at apocalypse sales and

we're looking at apocalypse sales and we're going apocalypse sales down to

we're going apocalypse sales down to customer information now there are a lot

customer information now there are a lot of rows in the apocalypse sales but

of rows in the apocalypse sales but there's very few in this customer

there's very few in this customer information and there's only one

information and there's only one customer per row whereas in the

customer per row whereas in the apocalypse sales up here the customer

apocalypse sales up here the customer can have several rows for several

can have several rows for several different orders so that's why the

different orders so that's why the cardinality is many to one now if we

cardinality is many to one now if we flip this and we say we want the

flip this and we say we want the customer information here and we want

customer information here and we want the apocalypse sales down here we tie

the apocalypse sales down here we tie that together now it's going to flip and

that together now it's going to flip and it's going to say one to many now let's

it's going to say one to many now let's look at the cross filter Direction and

look at the cross filter Direction and there's only two options here it's

there's only two options here it's either single or both and if we choose

either single or both and if we choose both and we click okay this now goes

both and we click okay this now goes from a single arrow pointing in one

from a single arrow pointing in one direction to two arrows pointing in both

direction to two arrows pointing in both directions but what does this really

directions but what does this really mean so in order to demonstrate this I'm

mean so in order to demonstrate this I'm going to put this back to a single

going to put this back to a single Direction and what we're going to try to

Direction and what we're going to try to do is connect the data over here or the

do is connect the data over here or the columns over here to the columns in this

columns over here to the columns in this apocalypse store so let's go over here

apocalypse store so let's go over here to build a visualization and what we're

to build a visualization and what we're going to do is we're going to take this

going to do is we're going to take this customer information and let's just say

customer information and let's just say we want to look at state so I'm going to

we want to look at state so I'm going to click on state right here and I'm just

click on state right here and I'm just going to make this into a table and the

going to make this into a table and the customer information table is only tied

customer information table is only tied right now to the sales table so we're

right now to the sales table so we're actually going to go over to the

actually going to go over to the apocalypse store and we want to see how

apocalypse store and we want to see how many product IDs are being bought in

many product IDs are being bought in these different states so really quickly

these different states so really quickly we're going to come up here and create a

we're going to come up here and create a new measure and all we're going to say

new measure and all we're going to say is this measure is the count of

is this measure is the count of Apocalypse store product ID and we're

Apocalypse store product ID and we're going to create that and now we're going

going to create that and now we're going to select it so it's added to that table

to select it so it's added to that table so now what this is showing is that

so now what this is showing is that there are 10 product s which there are

there are 10 product s which there are 10 products for each of these states but

10 products for each of these states but that's not actually technically correct

that's not actually technically correct because not every state purchased these

because not every state purchased these 10 different items if we go back to our

10 different items if we go back to our model and we change both of these

model and we change both of these to a both

to a both Direction and then we're going to go

Direction and then we're going to go back and see what changed in our

back and see what changed in our numbers so now let's go back to our

numbers so now let's go back to our visualization and now we can see that

visualization and now we can see that Minnesota actually only ordered seven

Minnesota actually only ordered seven different product IDs Miss Miss 8 New

different product IDs Miss Miss 8 New York 99 and Texas 10 this is actually

York 99 and Texas 10 this is actually much more accurate than before when you

much more accurate than before when you use the both option it takes these

use the both option it takes these tables and treats them as if they are a

tables and treats them as if they are a single table but the single option is

single table but the single option is not going to do that and so for our

not going to do that and so for our example if we're trying to connect this

example if we're trying to connect this table to this table and one of the last

table to this table and one of the last things that I want to show you is this

things that I want to show you is this option right down here which says make

option right down here which says make this relationship active now if we don't

this relationship active now if we don't click list and there are other options

click list and there are other options in here that connect these things like

in here that connect these things like the customer to the customer then that

the customer to the customer then that may be the active relationship but if I

may be the active relationship but if I select this is the active relationship

select this is the active relationship that means this is going to become the

that means this is going to become the default relationship between these two

default relationship between these two tables so now let's come out of here

tables so now let's come out of here we're going to click cancel we're going

we're going to click cancel we're going to zoom in just a little bit and bring

to zoom in just a little bit and bring these tables a little bit closer so we

these tables a little bit closer so we can zoom in just a little bit more now

can zoom in just a little bit more now we are going to go ahead and delete

we are going to go ahead and delete these so we're going to say delete yes

these so we're going to say delete yes and delete yes so just for demonstration

and delete yes so just for demonstration purposes we're going to build these

purposes we're going to build these relationships from scratch so we're

relationships from scratch so we're going to come over to the customer

going to come over to the customer information table and we're going to

information table and we're going to drag it all the way over here and put it

drag it all the way over here and put it on top of this cust ID or the customer

on top of this cust ID or the customer ID in Apocalypse

ID in Apocalypse sales and it's going to automatically

sales and it's going to automatically create that relationship and we can open

create that relationship and we can open this up and as you can see it created

this up and as you can see it created the relationship between this customer

the relationship between this customer ID in the apocalypse sales and the

ID in the apocalypse sales and the customer ID in the customer information

customer ID in the customer information it also defaulted the cardinality from

it also defaulted the cardinality from many to one and the cross filter

many to one and the cross filter direction to single so we're going to go

direction to single so we're going to go ahead and change that to both and click

ahead and change that to both and click okay

okay and then we're going to come over here

and then we're going to come over here to the product ID in Apocalypse store

to the product ID in Apocalypse store and drag this over the product ID in the

and drag this over the product ID in the apocalypse

apocalypse sales and again if we open it up it

sales and again if we open it up it created that relationship for us it

created that relationship for us it created the cardinality automatically

created the cardinality automatically and we're going to change this cross

and we're going to change this cross filter direction to both and click okay

filter direction to both and click okay and so on a really small scale that is

and so on a really small scale that is how it works of course it becomes a

how it works of course it becomes a little bit more complex the more tables

little bit more complex the more tables that you add and the more relationships

that you add and the more relationships that are created but this is how you're

that are created but this is how you're going to actually create the

going to actually create the relationships in the model tab within

relationships in the model tab within powerbi I hope that this tutorial has

powerbi I hope that this tutorial has helped you understand this concept a

helped you understand this concept a little bit better thank you guys so much

little bit better thank you guys so much for watching I really appreciate it if

for watching I really appreciate it if you like this video be sure to like And

you like this video be sure to like And subscribe below and I'll see you in the

subscribe below and I'll see you in the next

next [Music]

[Music] video what's going on everybody welcome

video what's going on everybody welcome back to the powerbi tutorial Series

back to the powerbi tutorial Series today we're going to be taking a look at

today we're going to be taking a look at Dax

[Music] now DAC stands for data analysis

now DAC stands for data analysis expressions and it's basically a library

expressions and it's basically a library of functions and operators that help you

of functions and operators that help you build formulas you can use Dax to create

build formulas you can use Dax to create measures and calculated columns within

measures and calculated columns within powerbi which can really give you a lot

powerbi which can really give you a lot of insight into your data honestly it is

of insight into your data honestly it is not super complicated and hopefully by

not super complicated and hopefully by the end of this video you'll have a lot

the end of this video you'll have a lot more confidence actually using Dax and

more confidence actually using Dax and powerp so without further Ado let's jump

powerp so without further Ado let's jump onto my screen and get started with the

onto my screen and get started with the tutorial all right so let's take a look

tutorial all right so let's take a look at our tables and data before we get

at our tables and data before we get started so we have two tables the

started so we have two tables the apocalypse sales the apocalypse store

apocalypse sales the apocalypse store for this apocalypse sales table we have

for this apocalypse sales table we have the customer product ID order ID unit

the customer product ID order ID unit sold and the date it was purchased and

sold and the date it was purchased and then for the apocalypse store we have

then for the apocalypse store we have product ID product name price and

product ID product name price and production cost now these are joined

production cost now these are joined together or they do have a relationship

together or they do have a relationship together via the product ID so what

together via the product ID so what we're going to be using are these new

we're going to be using are these new measures and new columns to create our

measures and new columns to create our Dax functions so really quickly let's go

Dax functions so really quickly let's go over to this report Tab and let's drop

over to this report Tab and let's drop down our Fields over here so we can see

down our Fields over here so we can see everything and so to get us started

everything and so to get us started we're going to go right up here to

we're going to go right up here to apocalypse sales we're going to

apocalypse sales we're going to rightclick and click new measure and

rightclick and click new measure and it's going to open up this right here

it's going to open up this right here which is basically our bar where we can

which is basically our bar where we can create our functions and so right here

create our functions and so right here it's automatically given us the name

it's automatically given us the name measure but we can change that and we're

measure but we can change that and we're going to say count of sales so now we

going to say count of sales so now we can start writing our Dax function

can start writing our Dax function that's just going to be the name of it

that's just going to be the name of it and what's going to show up right over

and what's going to show up right over here once we click enter so let's go

here once we click enter so let's go over here and we're going to say count

over here and we're going to say count and as we're typing it's automatically

and as we're typing it's automatically giving us options it has something

giving us options it has something called intellisense if you've ever used

called intellisense if you've ever used other Microsoft products intellisense is

other Microsoft products intellisense is their kind of autoc completion that

their kind of autoc completion that helps you look at other options very

helps you look at other options very quickly and so we're just going to click

quickly and so we're just going to click on this count and it's prompting us to

on this count and it's prompting us to put in a column name and so we can come

put in a column name and so we can come down here and we can select one or we

down here and we can select one or we can type it out and it'll try to predict

can type it out and it'll try to predict and help us choose which column to

and help us choose which column to select so for us we're going to use this

select so for us we're going to use this order ID but let's just start typing it

order ID but let's just start typing it out we'll say order ID and then we can

out we'll say order ID and then we can click on it and we're going to close

click on it and we're going to close this parenthesis and click enter or you

this parenthesis and click enter or you can go over here and click this check

can go over here and click this check mark but we're just going to click

mark but we're just going to click enter and so over on this right side it

enter and so over on this right side it finalized that and save that and we can

finalized that and save that and we can actually look at that by clicking on

actually look at that by clicking on this box next to

this box next to it and we want to look at the this in a

it and we want to look at the this in a table so now we can see that there are

table so now we can see that there are 74 sales now for this we want to see

74 sales now for this we want to see who's buying our products we want to see

who's buying our products we want to see what our what our client name is so

what our what our client name is so we're going to go over here we're going

we're going to go over here we're going to choose customer and we're going to

to choose customer and we're going to put customer on top of sales and we're

put customer on top of sales and we're just going to take a look at it like

just going to take a look at it like this so now we can see that our number

this so now we can see that our number one customer is Uncle Joe's Prep shop he

one customer is Uncle Joe's Prep shop he has 22 orders now they have the most

has 22 orders now they have the most orders with us but it doesn't

orders with us but it doesn't necessarily mean that they're spending

necessarily mean that they're spending the most money with us but we can take a

the most money with us but we can take a look at that later the next thing that I

look at that later the next thing that I want to take a look at is how many

want to take a look at is how many products we're actually selling what are

products we're actually selling what are our big products that we're selling we

our big products that we're selling we have 10 different items but I don't know

have 10 different items but I don't know exactly which one is selling the best if

exactly which one is selling the best if if one is doing really poorly and

if one is doing really poorly and getting no orders this is something that

getting no orders this is something that I want to look into so all we're going

I want to look into so all we're going to do is go right back up here to

to do is go right back up here to apocalypse sales again right click and

apocalypse sales again right click and select new measure and for this one

select new measure and for this one we're going to call it the sum of

we're going to call it the sum of products sold

products sold and all we're going to start out with is

and all we're going to start out with is by doing sum and if this seems familiar

by doing sum and if this seems familiar to something like Excel you're 100%

to something like Excel you're 100% correct it is very similar and remember

correct it is very similar and remember these are both Microsoft products so

these are both Microsoft products so there's going to be similar

there's going to be similar functionality in both of them and so

functionality in both of them and so this Dax is going to have a lot of

this Dax is going to have a lot of similarities to exactly how it has it in

similarities to exactly how it has it in Excel so we're going to do an open

Excel so we're going to do an open bracket and now what we're going to

bracket and now what we're going to choose is this units sold we want to sum

choose is this units sold we want to sum up all of these units sold and see how

up all of these units sold and see how many we actually selling so we're going

many we actually selling so we're going to say units sold I'm going to hit tab

to say units sold I'm going to hit tab it's going to autocomplete that I'm

it's going to autocomplete that I'm going to close my parenthesis and I'm

going to close my parenthesis and I'm going to come over here and click this

going to come over here and click this checkbox so now it's created that

checkbox so now it's created that measure and we're already selected in

measure and we're already selected in this table so all we have to do is click

this table so all we have to do is click the check mark and it's going to show us

the check mark and it's going to show us that we have 3,000 total products sold

that we have 3,000 total products sold and we can go through here and see what

and we can go through here and see what the big sellers are and probably the

the big sellers are and probably the biggest one that I see right off the bat

biggest one that I see right off the bat is this multi- Tool Survival Knife so

is this multi- Tool Survival Knife so these Dax functions that you can write

these Dax functions that you can write can be very simple and lead to really

can be very simple and lead to really good insights that you can use for the

good insights that you can use for the visualizations later on now I want to

visualizations later on now I want to take a look at the difference between

take a look at the difference between something like sum which is an

something like sum which is an aggregator function and something like

aggregator function and something like sum X which is an iterator function

sum X which is an iterator function because if you add X to some of these

because if you add X to some of these aggregator functions you can create them

aggregator functions you can create them or or make them into an iterator

or or make them into an iterator function so you can have some and some X

function so you can have some and some X or average and average X adding X onto

or average and average X adding X onto the end of them can make them to an

the end of them can make them to an iterator function so let's take a look

iterator function so let's take a look and see how that actually works I'm

and see how that actually works I'm going to show you the difference and

going to show you the difference and then I'm going to talk through the

then I'm going to talk through the difference at the end so really quickly

difference at the end so really quickly let's go back to our data and let's go

let's go back to our data and let's go to the apocalypse store now what we have

to the apocalypse store now what we have right here is we have the price and we

right here is we have the price and we have the production cost and we want to

have the production cost and we want to see how much profit we're getting from

see how much profit we're getting from each of these as well as we can take a

each of these as well as we can take a look at the unit sold and see how much

look at the unit sold and see how much money we are actually making so what

money we are actually making so what we're going to do is we're going to come

we're going to do is we're going to come back over here we're going to go to

back over here we're going to go to apocalypse store we're going to right

apocalypse store we're going to right click and create a measure and in just a

click and create a measure and in just a little bit we're going to be creating a

little bit we're going to be creating a new column and that'll kind of show the

new column and that'll kind of show the difference really well so we're going to

difference really well so we're going to create this new measure and we're going

create this new measure and we're going to name it

to name it profit and we're going to come over here

profit and we're going to come over here and what we're going to do is we're

and what we're going to do is we're going to take the sum oops we're going

going to take the sum oops we're going to start with our sums we're going to

to start with our sums we're going to take the sum of the

take the sum of the price and then we're going to close that

price and then we're going to close that parenthesis and we're going to subtract

parenthesis and we're going to subtract the sum of the production cost so all

the sum of the production cost so all that does is it says if something cost

that does is it says if something cost $20 if we sold it for $20 and it only

$20 if we sold it for $20 and it only costs us $10 that's $10 in profit for

costs us $10 that's $10 in profit for that item and then what we're going to

that item and then what we're going to want to do is we're going to actually

want to do is we're going to actually want to encapsulate that really quickly

want to encapsulate that really quickly because we're about to use multiply and

because we're about to use multiply and then we're going to sum and now we're

then we're going to sum and now we're going to take the units sold so how many

going to take the units sold so how many units were actually sold at that profit

units were actually sold at that profit that we just made so let's see if that

that we just made so let's see if that works and let's click the check right

works and let's click the check right here and so we have the profit so let's

here and so we have the profit so let's click on the profit oops that's not what

click on the profit oops that's not what I wanted to do let's use a new one or

I wanted to do let's use a new one or let's create a new uh table we're going

let's create a new uh table we're going to click

to click profit let's make it a table and I'm

profit let's make it a table and I'm going to pull this right over

going to pull this right over here now we have our profit but I really

here now we have our profit but I really want to know is which customer is

want to know is which customer is spending the most money at my store so

spending the most money at my store so we're going to come right over here

we're going to come right over here we're going to click on customer and I'm

we're going to click on customer and I'm put customer at the top and just at a

put customer at the top and just at a glance we can see that Uncle Joe's Prep

glance we can see that Uncle Joe's Prep shop is spending the most money at the

shop is spending the most money at the store now now what I want to show you is

store now now what I want to show you is the difference between sum and sum X so

the difference between sum and sum X so what I'm going to do so I'm going to go

what I'm going to do so I'm going to go back to this profit and going to copy

back to this profit and going to copy this this entire thing and we're going

this this entire thing and we're going to go back here to this table now we

to go back here to this table now we just created a measure and we were able

just created a measure and we were able to break it down by each customer so

to break it down by each customer so let's go back over here now let's go up

let's go back over here now let's go up here to home and we're going to create a

here to home and we're going to create a new column and we're going to call this

new column and we're going to call this profit

profit profit underscore column and we're going

profit underscore column and we're going to literally paste the exact same thing

to literally paste the exact same thing into here and we're going to hit

into here and we're going to hit enter and each row is the exact same

enter and each row is the exact same thing so what it's doing is it is going

thing so what it's doing is it is going through the price and it's adding all of

through the price and it's adding all of it up and calculating it at the bottom

it up and calculating it at the bottom it's adding the production cost it's

it's adding the production cost it's going all the way down and calculating

going all the way down and calculating it at the bottom and then it's going

it at the bottom and then it's going over and looking at how many units it

over and looking at how many units it sold and then it's performing this

sold and then it's performing this calculation up here and then it gives us

calculation up here and then it gives us the total and it's doing it for every

the total and it's doing it for every single row but that's not really what we

single row but that's not really what we wanted to show what we wanted to show is

wanted to show what we wanted to show is the profit for each row what we wanted

the profit for each row what we wanted to say is here's the price for the Rope

to say is here's the price for the Rope the production cost for the rope and

the production cost for the rope and then how many units we actually sold and

then how many units we actually sold and then it'll calculate that and give us

then it'll calculate that and give us the actual profit for just that row but

the actual profit for just that row but we cannot do it by just using this sum

we cannot do it by just using this sum what we need to do is use something

what we need to do is use something called Su X so let's add another column

called Su X so let's add another column let's go back to home say new

let's go back to home say new column and now we're going to

column and now we're going to say profit underscore oops underscore

say profit underscore oops underscore column underscore sum

column underscore sum X and now we're going to use sum X and

X and now we're going to use sum X and hit Tab and we need to choose the table

hit Tab and we need to choose the table that we want to put this in so we're

that we want to put this in so we're going to say apocalypse sales because

going to say apocalypse sales because that's the table that we're looking at

that's the table that we're looking at right here we're going to say comma and

right here we're going to say comma and now we need to input an expression which

now we need to input an expression which it says it Returns the sum of an

it says it Returns the sum of an expression evaluated for each row in a

expression evaluated for each row in a table before when you're just using sum

table before when you're just using sum it's looking at all of these combined

it's looking at all of these combined now it's taking it row by row so what

now it's taking it row by row so what we're going to do is basically input the

we're going to do is basically input the same thing as we did before I'm going to

same thing as we did before I'm going to copy I'm going to paste that it's not

copy I'm going to paste that it's not going to be correct I need to get rid of

going to be correct I need to get rid of these

these sums but it's basically the exact same

sums but it's basically the exact same equation give me just a second and let's

equation give me just a second and let's get rid of this

get rid of this some and let's see if this works so

some and let's see if this works so let's click the check

let's click the check button and now this looks a lot better

button and now this looks a lot better so what this is now showing us is at a

so what this is now showing us is at a row level this nylon rope made us 51,000

row level this nylon rope made us 51,000 almost

almost $52,000 the waterproof matches made us

$52,000 the waterproof matches made us $115,000 and we can go down and look at

$115,000 and we can go down and look at each item and see how much that actually

each item and see how much that actually made us versus this profit column and so

made us versus this profit column and so that is the biggest difference between

that is the biggest difference between sum and sum X hopefully that made sense

sum and sum X hopefully that made sense I know that sum and sum X and and the

I know that sum and sum X and and the difference between an aggregator

difference between an aggregator function and iterator function can be a

function and iterator function can be a little bit confusing especially if

little bit confusing especially if you've never done it before but

you've never done it before but hopefully that was a good example for

hopefully that was a good example for you to understand that concept now let's

you to understand that concept now let's go back over here to apocalypse sales

go back over here to apocalypse sales right here we have a date purchase now

right here we have a date purchase now in the Dax function we have some ways

in the Dax function we have some ways that we can interact with dates and so I

that we can interact with dates and so I want to take a look at those really

want to take a look at those really quickly so we're going to go right up

quickly so we're going to go right up here and click on new column and we're

here and click on new column and we're just going to leave that as column but

just going to leave that as column but what we're going to say is day so

what we're going to say is day so there's a few different ones we have Day

there's a few different ones we have Day dates YTD next day previous day and

dates YTD next day previous day and weekday and they all are pretty

weekday and they all are pretty self-explanatory if you click on it

self-explanatory if you click on it let's click on weekday it says it's

let's click on weekday it says it's going to return a number from 1 to 7

going to return a number from 1 to 7 identifying the day of the week of a

identifying the day of the week of a date so let's use this really quickly

date so let's use this really quickly and so we're going to say date

and so we're going to say date purchased and and click tab hit

purchased and and click tab hit comma and it's going to give us a three

comma and it's going to give us a three different options basically it's a one a

different options basically it's a one a two and a three um right here if you hit

two and a three um right here if you hit this button read more you can read more

this button read more you can read more on it this is going to say Sunday is

on it this is going to say Sunday is equal to one Saturday is equal to seven

equal to one Saturday is equal to seven I like this one personally which is

I like this one personally which is Monday equals one in my brain it just

Monday equals one in my brain it just makes more sense so I'm going to click

makes more sense so I'm going to click on two I'm going to close that

on two I'm going to close that parentheses and we're going to I guess

parentheses and we're going to I guess I'll say uh let's say day of week for

I'll say uh let's say day of week for the column

the column let's click that

let's click that checkbox and now Saturdays are equal to

checkbox and now Saturdays are equal to sixes Mondays are equal to one this

sixes Mondays are equal to one this allows us to see which day of the week

allows us to see which day of the week people are buying the most products on

people are buying the most products on or or which day of the week is somebody

or or which day of the week is somebody submitting their orders on and so let's

submitting their orders on and so let's go over to our report let's get rid of

go over to our report let's get rid of this we just going to move this oh jeez

this we just going to move this oh jeez I hate moving stuff sometimes all right

I hate moving stuff sometimes all right really quickly I want to show you the

really quickly I want to show you the difference between what we just did and

difference between what we just did and what we already have so we have this um

what we already have so we have this um date purchased and let's make that into

date purchased and let's make that into a bar

a bar graph and what we're going to be taking

graph and what we're going to be taking a look at is actually the units sold so

a look at is actually the units sold so right here we have this and obviously

right here we have this and obviously for we don't want 2022 we're going to

for we don't want 2022 we're going to get rid of the year we only have one

get rid of the year we only have one quarter right here we can see January

quarter right here we can see January February March so we can tell that

February March so we can tell that January has the most sales or the most

January has the most sales or the most units sold in that month if we get rid

units sold in that month if we get rid of that we go down to day we do have

of that we go down to day we do have some information but we don't know what

some information but we don't know what day of the week it is it could change

day of the week it is it could change from month to month and it's really hard

from month to month and it's really hard to tell exactly what if there's any

to tell exactly what if there's any pattern there at all that's where what

pattern there at all that's where what we just created comes in handy so let's

we just created comes in handy so let's recreate this exact same thing but

recreate this exact same thing but instead we're going to use day of week

instead we're going to use day of week so we're going to select day of week and

so we're going to select day of week and unit sold let's drag that down and move

unit sold let's drag that down and move this over right here and this day of the

this over right here and this day of the week should be on the

week should be on the xaxis and it's really easy now to see if

xaxis and it's really easy now to see if there's a pattern here there's really

there's a pattern here there's really not at least not for this fake data that

not at least not for this fake data that we have um but just I I want these uh

we have um but just I I want these uh data labels on really

data labels on really quickly um it's not easy to see if

quickly um it's not easy to see if there's any pattern again Monday has the

there's any pattern again Monday has the most so maybe that that I mean it goes

most so maybe that that I mean it goes down a little bit and then it picks back

down a little bit and then it picks back up so maybe middle the week is our least

up so maybe middle the week is our least uh sales day our Wednesdays and

uh sales day our Wednesdays and Thursdays are a little bit lower than

Thursdays are a little bit lower than the rest and the beginning and the end

the rest and the beginning and the end of the week tend to be the highest again

of the week tend to be the highest again not a huge pattern but you know it's

not a huge pattern but you know it's much easier to see if there is a pattern

much easier to see if there is a pattern from week to week or what day of the

from week to week or what day of the week now that we use this weekday

week now that we use this weekday function and so this can be really

function and so this can be really really useful let's go back here to our

really useful let's go back here to our data now we're going to look at our last

data now we're going to look at our last Dax function for this video let's go up

Dax function for this video let's go up here and create a new column and we're

here and create a new column and we're going to be looking at something called

going to be looking at something called the if statement now if you've ever used

the if statement now if you've ever used Excel I'm sure you have heard of this

Excel I'm sure you have heard of this and you can do the exact same thing here

and you can do the exact same thing here in powerbi and so we're going to name

in powerbi and so we're going to name this one order size order undor size and

this one order size order undor size and so all we're going to say is if we're

so all we're going to say is if we're going to click on this one right here we

going to click on this one right here we need to perform our logical test and

need to perform our logical test and then we want to say if it's true what's

then we want to say if it's true what's our value and if it's false what is our

our value and if it's false what is our value so what we're going to be looking

value so what we're going to be looking at is units sold so we're looking at

at is units sold so we're looking at order size so we're going to say if unit

order size so we're going to say if unit sold is greater than

sold is greater than 25 what's going to happen if it is true

25 what's going to happen if it is true if the order is larger than 25 you want

if the order is larger than 25 you want to say it's a big order and if it's not

to say it's a big order and if it's not we want to say it's a small

we want to say it's a small order super simple we'll close that

order super simple we'll close that parenthesis we'll click okay and now

parenthesis we'll click okay and now really quickly we're able to see if this

really quickly we're able to see if this is a big order or a small order and so

is a big order or a small order and so that is all I have for you today there

that is all I have for you today there are a lot of other dox functions but the

are a lot of other dox functions but the ones that we looked at today are ones

ones that we looked at today are ones that are very common ones that you'll

that are very common ones that you'll see the most and there can be a lot of

see the most and there can be a lot of really complex and intricate Dax

really complex and intricate Dax functions that you can create and in our

functions that you can create and in our project at the end of this series I will

project at the end of this series I will be sure to include some more complex Dax

be sure to include some more complex Dax functions but hopefully this gave you a

functions but hopefully this gave you a good introduction into Dax so you know

good introduction into Dax so you know how to use it a little bit better thank

how to use it a little bit better thank you guys so much for watching I really

you guys so much for watching I really appreciate it if you like this video be

appreciate it if you like this video be sure to like And subscribe and check out

sure to like And subscribe and check out all of my other videos on everything

all of my other videos on everything data analyst related I will see you in

data analyst related I will see you in the next

video [Music]

[Music]

what's going on everybody welcome back to the powerbi tutorial Series today

to the powerbi tutorial Series today we're going to be looking at how to

we're going to be looking at how to drill down in

[Music] visualizations so when I say drill down

visualizations so when I say drill down I mean you're basically adding another

I mean you're basically adding another layer beneath the top layer of the

layer beneath the top layer of the visualization and when somebody clicks

visualization and when somebody clicks or drills down in that data they can see

or drills down in that data they can see more insights and more information on

more insights and more information on the top level of data when you drill

the top level of data when you drill down you can also drill up and I will

down you can also drill up and I will show you how to do that in this tutorial

show you how to do that in this tutorial so without further Ado let's jump on my

so without further Ado let's jump on my screen and get started with the tutorial

screen and get started with the tutorial all right so before we get started I

all right so before we get started I wanted to remind you that you can find

wanted to remind you that you can find the data that we're going to be working

the data that we're going to be working with in this tutorial in the description

with in this tutorial in the description you can go and download it from my

you can go and download it from my GitHub now the two tables I'm going to

GitHub now the two tables I'm going to be looking at are apocalypse sales and

be looking at are apocalypse sales and purchase tracker and if you've ever

purchase tracker and if you've ever created any visualizations you've

created any visualizations you've probably seen something like this where

probably seen something like this where you'll have the store and the price and

you'll have the store and the price and this is the the things that we actually

this is the the things that we actually bought so this is the total amount of

bought so this is the total amount of Apocalypse prepping uh equipment that we

Apocalypse prepping uh equipment that we bought and we'll put the store in this

bought and we'll put the store in this Legend right here and you've probably

Legend right here and you've probably seen something like this and if you're

seen something like this and if you're anything like me you're going to be in a

anything like me you're going to be in a meeting and you're going to be

meeting and you're going to be presenting this and some higher up is

presenting this and some higher up is going to be like hey Alex that looks

going to be like hey Alex that looks great but I want to you know see what

great but I want to you know see what things we actually bought in Target and

things we actually bought in Target and how much this cost can you create a

how much this cost can you create a visualization for that and you're going

visualization for that and you're going to be like well I could or I could use

to be like well I could or I could use drill down so you could have done this

drill down so you could have done this in the first place uh which you should

in the first place uh which you should have so what we're going to do is all

have so what we're going to do is all we're going to do is we're going to say

we're going to do is we're going to say we're going to say the product right

we're going to say the product right here and these are going to be the

here and these are going to be the actual things and we're going to put it

actual things and we're going to put it right under store now you can't see

right under store now you can't see these things right but there is a a

these things right but there is a a hierarchy here so once we added this

hierarchy here so once we added this these options became available let's

these options became available let's take it out and all those just

take it out and all those just disappeared and then if we add it back

disappeared and then if we add it back right here they came back and so you can

right here they came back and so you can do right here which is is click to turn

do right here which is is click to turn on drill down you can go to the next

on drill down you can go to the next level in the hierarchy or you can even

level in the hierarchy or you can even expand all down one level in the

expand all down one level in the hierarchy so let's look at each of those

hierarchy so let's look at each of those really quickly so let's click on this

really quickly so let's click on this one it's just going to turn on drill

one it's just going to turn on drill down mode so now if I go and I click on

down mode so now if I go and I click on target it's going to drill down into

target it's going to drill down into these and if we want to I can then put

these and if we want to I can then put product under this

product under this Legend and we can see all of those

Legend and we can see all of those things but of course if we go back up

things but of course if we go back up it's going to be all broken up into this

it's going to be all broken up into this clustered column chart which is more

clustered column chart which is more like

like um this which isn't exactly what we were

um this which isn't exactly what we were going for but it works now uh let me get

going for but it works now uh let me get rid of this I actually want store in the

rid of this I actually want store in the legend now if we turn that off and we

legend now if we turn that off and we click it doesn't do that anymore so what

click it doesn't do that anymore so what it does now is it just highlights

it does now is it just highlights Walmart it highlights Costco it

Walmart it highlights Costco it highlights Target so we're going to keep

highlights Target so we're going to keep that on uh but we can also do something

that on uh but we can also do something called going down in the next level of

called going down in the next level of hierarchy so let's click on that and so

hierarchy so let's click on that and so now this is going to go down to the next

now this is going to go down to the next level down to this product level because

level down to this product level because that is the next level and now it's

that is the next level and now it's going to show us each of those things

going to show us each of those things but it's going to have it broken out by

but it's going to have it broken out by the store and so it's a completely

the store and so it's a completely different visualization but all within

different visualization but all within the same Realm of the data that we're

the same Realm of the data that we're looking at and what we actually care

looking at and what we actually care about so let's go back up in the

about so let's go back up in the hierarchy and then let's use this one

hierarchy and then let's use this one right here which is expand all down one

right here which is expand all down one level in the hierarchy and so this one

level in the hierarchy and so this one is again extremely similar except it

is again extremely similar except it just visualizes it differently and now

just visualizes it differently and now what it's doing is Walmart rice Target

what it's doing is Walmart rice Target dried beans Costco rice so instead of

dried beans Costco rice so instead of having an all uh like this one where

having an all uh like this one where it's stacked on top of each other it's

it's stacked on top of each other it's breaking it down individually so this

breaking it down individually so this one column would become three separate

one column would become three separate columns now I'm going to minimize this

columns now I'm going to minimize this right here uh I'm actually going to go

right here uh I'm actually going to go back up in the hierarchy just for visual

back up in the hierarchy just for visual purposes now I'm going to show you one

purposes now I'm going to show you one more example we're going to use this

more example we're going to use this apocalypse sales up here and this is one

apocalypse sales up here and this is one that I actually use all the time so the

that I actually use all the time so the one you've seen you know you'll get

one you've seen you know you'll get stuff like that especially if you're

stuff like that especially if you're working with like sales and stuff but I

working with like sales and stuff but I work in operations right so I have a lot

work in operations right so I have a lot of order IDs product IDs stuff like that

of order IDs product IDs stuff like that now this one this one genuinely I use

now this one this one genuinely I use quite often I'll have a customer U let's

quite often I'll have a customer U let's make it we'll just go like this we have

make it we'll just go like this we have a customer and we have unit sold and

a customer and we have unit sold and let's use the customer as the legend so

let's use the customer as the legend so let's make this one quite a bit

let's make this one quite a bit larger and I'll have something like this

larger and I'll have something like this and they'll say okay well we want to see

and they'll say okay well we want to see the order ID s that go with it because

the order ID s that go with it because we want to know what orders are actually

we want to know what orders are actually happening for each of these people

happening for each of these people obviously I'm not using this exact data

obviously I'm not using this exact data but very very very similar and all you

but very very very similar and all you have to do is take these order IDs and

have to do is take these order IDs and slide it right under here under customer

slide it right under here under customer and this visualization right here is

and this visualization right here is something I've done a thousand times

something I've done a thousand times because what happens is is someone some

because what happens is is someone some stakeholder in our company is saying hey

stakeholder in our company is saying hey Alex we want this and we want to know we

Alex we want this and we want to know we want to drill down on this IP address we

want to drill down on this IP address we want to drill down on this certain

want to drill down on this certain database we want to drill down on

database we want to drill down on something and we want to see the order

something and we want to see the order IDs within them so then all you do is

IDs within them so then all you do is you turn on drill mode or drill down

you turn on drill mode or drill down mode you'll click on it and you can see

mode you'll click on it and you can see every single order ID that's in there

every single order ID that's in there and then they can go and look those up

and then they can go and look those up in their system and resolve them or

in their system and resolve them or whatever they're trying to do with it

whatever they're trying to do with it and it helps a ton and it's very very

and it helps a ton and it's very very useful this one is extremely applicable

useful this one is extremely applicable and that's really all drill down is

and that's really all drill down is again you have these different

again you have these different hierarchies as well um but for different

hierarchies as well um but for different things it's not as useful as you can see

things it's not as useful as you can see we also have this hierarchy which again

we also have this hierarchy which again is not as useful so it just depends on

is not as useful so it just depends on the data that you're using and how you

the data that you're using and how you want to use this drill down effect but I

want to use this drill down effect but I promise you that drill down is used all

promise you that drill down is used all the time especially when you're giving

the time especially when you're giving presentations where people want to know

presentations where people want to know more information than just the the

more information than just the the visualization that you're presenting so

visualization that you're presenting so I hope that this has been helpful I hope

I hope that this has been helpful I hope that you understand drill down a little

that you understand drill down a little bit better if you like this video be

bit better if you like this video be sure to like And subscribe and check out

sure to like And subscribe and check out all my other videos on powerbi thank you

all my other videos on powerbi thank you and I'll see you in the next video

and I'll see you in the next video [Music]

[Music] what's going on everybody welcome back

what's going on everybody welcome back to the powerbi tutorial Series today

to the powerbi tutorial Series today we're going to be taking a look at

we're going to be taking a look at conditional

[Music] formatting now conditional formatting

formatting now conditional formatting may sound familiar because we looked at

may sound familiar because we looked at it in the Excel series and it's very

it in the Excel series and it's very similar how you use it in Excel versus

similar how you use it in Excel versus how you use it in powerbi conditional

how you use it in powerbi conditional formatting allows you to take a table or

formatting allows you to take a table or a matrix within powerbi and use those

a matrix within powerbi and use those cells to color code them and create

cells to color code them and create gradients and different visualizations

gradients and different visualizations within the actual table or Matrix I'm

within the actual table or Matrix I'm excited to start this one so let's jump

excited to start this one so let's jump over my screen and get started with the

over my screen and get started with the tutorial all right so before we get

tutorial all right so before we get started if you want to use the data that

started if you want to use the data that we're using in this video you can find

we're using in this video you can find it in the description on my GitHub now

it in the description on my GitHub now conditional formatting is super simple

conditional formatting is super simple and you've most likely used it in Excel

and you've most likely used it in Excel before but you can also use it in

before but you can also use it in powerbi and let me show you how to do

powerbi and let me show you how to do that so the first thing we're going to

that so the first thing we're going to do is come over over to our apocalypse

do is come over over to our apocalypse store and we're going to pull up our

store and we're going to pull up our product name as well as the price and

product name as well as the price and what we can do is come over here and

what we can do is come over here and we're going to go to price and it has to

we're going to go to price and it has to be under the columns so you can't come

be under the columns so you can't come over here and do this we're going to

over here and do this we're going to come right over here to price and we're

come right over here to price and we're going to right click and let's go to

going to right click and let's go to conditional formatting and we have

conditional formatting and we have background color font color icons and

background color font color icons and web URL let's take a look at background

web URL let's take a look at background color first this is most likely the one

color first this is most likely the one that we'll look at the most so we're

that we'll look at the most so we're going to get this pop up and I'm going

going to get this pop up and I'm going to slide this over now there's a lot of

to slide this over now there's a lot of different things we can customize in

different things we can customize in here and the first thing I want to take

here and the first thing I want to take a look at is format style we have the

a look at is format style we have the gradient and what it's going to say is

gradient and what it's going to say is the lowest value will be this color

the lowest value will be this color highest value will be this color it'll

highest value will be this color it'll give us this gradient color scale and so

give us this gradient color scale and so we'll use that in just a little bit but

we'll use that in just a little bit but we can also create rules kind of like an

we can also create rules kind of like an if statement and if it is between this

if statement and if it is between this range and this range we give it a color

range and this range we give it a color and if it's between a different range

and if it's between a different range and a different range we'll give it a

and a different range we'll give it a different color so we'll also try that

different color so we'll also try that one and then we have this field value uh

one and then we have this field value uh and this one is one that uh honestly I

and this one is one that uh honestly I don't use that much I've used it maybe

don't use that much I've used it maybe once and what you can do is select a

once and what you can do is select a text field like customer and you can do

text field like customer and you can do some summarizations on the first and

some summarizations on the first and last and that is it so what we're going

last and that is it so what we're going to do is we're going to look at gradient

to do is we're going to look at gradient specifically for not the customer but

specifically for not the customer but we're going to go back to the apocalypse

we're going to go back to the apocalypse store and we're going to do it on the

store and we're going to do it on the price now what I'm going to do is keep

price now what I'm going to do is keep it as the count because this is what the

it as the count because this is what the default is and we're going to go back

default is and we're going to go back and fix it later but what we want our

and fix it later but what we want our lowest value to be is this bright green

lowest value to be is this bright green showing that this it's a cheap product

showing that this it's a cheap product it's easy to purchase the high value

it's easy to purchase the high value ones are going to be just the shade of

ones are going to be just the shade of red more expensive and we'll do it on

red more expensive and we'll do it on the count now remember the count is on

the count now remember the count is on each of these and we're not doing a

each of these and we're not doing a count of how many are sold we're doing a

count of how many are sold we're doing a count of each product so it's just one

count of each product so it's just one per row so it all should be the same

per row so it all should be the same color let's take a look so it is all the

color let's take a look so it is all the same color but what we really want to

same color but what we really want to show is the actual price not just the

show is the actual price not just the count of the price so let's go back to

count of the price so let's go back to conditional formatting we're going to

conditional formatting we're going to click the background color again and

click the background color again and this time we're going to change the

this time we're going to change the summarization now you can do sum you can

summarization now you can do sum you can do average minimum maximum it really

do average minimum maximum it really doesn't matter for this example the

doesn't matter for this example the number is the same regardless of really

number is the same regardless of really which one we choose so we can just

which one we choose so we can just choose the minimum and it's going to

choose the minimum and it's going to choose the minimum of each row which is

choose the minimum of each row which is the price so we're just going to select

the price so we're just going to select minimum for this example we'll select

minimum for this example we'll select okay and it should correct it

okay and it should correct it accordingly which means the bright green

accordingly which means the bright green is the lowest and it goes all the way up

is the lowest and it goes all the way up to the highest which is the red now

to the highest which is the red now let's go over here to apocalypse sales

let's go over here to apocalypse sales we'll add in the units

we'll add in the units sold and let's move that out a little

sold and let's move that out a little bit and I'm doing that on purpose

bit and I'm doing that on purpose because we're about to look at something

because we're about to look at something within the conditional formatting so

within the conditional formatting so let's go to unit sold and we'll look at

let's go to unit sold and we'll look at the conditional formatting for this one

the conditional formatting for this one now if you noticed we now have a new one

now if you noticed we now have a new one on here called data bars now we're able

on here called data bars now we're able to see data bar bars on unit sold and

to see data bar bars on unit sold and not price because unit sold is something

not price because unit sold is something like a sum an average something that's

like a sum an average something that's aggregated but let's take a look at

aggregated but let's take a look at datab bars because I want to show you

datab bars because I want to show you how to use this and then we'll go back

how to use this and then we'll go back to the background color so for data bars

to the background color so for data bars we are going to taking a look at the

we are going to taking a look at the lowest to the highest value again we're

lowest to the highest value again we're going to go from bright green all the

going to go from bright green all the way

way to this exact red it's going to be from

to this exact red it's going to be from left to right and what it's going to

left to right and what it's going to show you is if it is a positive number

show you is if it is a positive number which all of these are is going to be a

which all of these are is going to be a green bar basically representing the

green bar basically representing the number that you see in here along this

number that you see in here along this line so let's click

line so let's click okay and we're going to be able to see

okay and we're going to be able to see the highest numbers and let's scooch

the highest numbers and let's scooch this over quite a bit so you can kind of

this over quite a bit so you can kind of get a better understanding and we're

get a better understanding and we're going to do it from highest to lowest so

going to do it from highest to lowest so we sold the most multi-tool survival

we sold the most multi-tool survival knives at 477 and so this entire bar

knives at 477 and so this entire bar this row is entirely filled up or almost

this row is entirely filled up or almost all the way filled up while as it gets

all the way filled up while as it gets lower and as we sell only 182 solar

lower and as we sell only 182 solar battery flashlights the bar is going to

battery flashlights the bar is going to represent that and show that now I'm

represent that and show that now I'm about to completely mess up this

about to completely mess up this visualization on purpose because it's

visualization on purpose because it's about to get very messy to show you that

about to get very messy to show you that you can do a little bit too much uh it

you can do a little bit too much uh it is possible what we're going to do is

is possible what we're going to do is we're going to go right over here to

we're going to go right over here to this background color unit sold and

this background color unit sold and instead of gradient let's look at rules

instead of gradient let's look at rules now with the price we just did a

now with the price we just did a gradient scale but we can do basically

gradient scale but we can do basically groups of these and say if a number is

groups of these and say if a number is greater to or equal than this number

greater to or equal than this number then it's going to be a certain color

then it's going to be a certain color and then if it's in a different range we

and then if it's in a different range we can give it a different color so we're

can give it a different color so we're going to say if it's greater than or

going to say if it's greater than or equal to zero and we're going to say

equal to zero and we're going to say number not percent and if it's less than

number not percent and if it's less than 266 because we have 265 right here let's

266 because we have 265 right here let's make it a nice uh like gold a beautiful

make it a nice uh like gold a beautiful lovely mustard gold just just great now

lovely mustard gold just just great now we're going to say if it's greater than

we're going to say if it's greater than or equal to we'll do 260 6 6 because

or equal to we'll do 260 6 6 because this is less than 266 so it should be

this is less than 266 so it should be greater than or equal to 266 number and

greater than or equal to 266 number and if it is less than we'll say

if it is less than we'll say 500 now we want to do this one and we'll

500 now we want to do this one and we'll give it uh let's do like a peach and

give it uh let's do like a peach and we'll click okay and now we have another

we'll click okay and now we have another conditional formatting on top of that

conditional formatting on top of that that can give us more information now

that can give us more information now again you should not do this it's just

again you should not do this it's just too many now let's go one step further

too many now let's go one step further and make it even more ridiculous and

and make it even more ridiculous and show you one more thing before I show

show you one more thing before I show you how you may actually want to use

you how you may actually want to use this uh let's go back to unit sold we're

this uh let's go back to unit sold we're going to rightclick go to conditional

going to rightclick go to conditional formatting and you can do something

formatting and you can do something called icons um font color is the exact

called icons um font color is the exact same thing as background color except it

same thing as background color except it changes the the font and so I'm not

changes the the font and so I'm not really going to look into that one icons

really going to look into that one icons are very simple extremely similar to

are very simple extremely similar to Excel and how you've seen them and the

Excel and how you've seen them and the rules that you can apply to them are

rules that you can apply to them are basically the same as if you're doing

basically the same as if you're doing like a gradient and it's these if

like a gradient and it's these if statements that we saw before now it

statements that we saw before now it Auto gives us this right here which

Auto gives us this right here which basically says 0 to 33% 33 to 67 67 to

basically says 0 to 33% 33 to 67 67 to 100 if it's in the bottom 3% it gives us

100 if it's in the bottom 3% it gives us this red the middle is yellow and the

this red the middle is yellow and the top is green so we can go through and

top is green so we can go through and change all of this but honestly this

change all of this but honestly this looks pretty good so let's click on

looks pretty good so let's click on it and so the ones that are our least

it and so the ones that are our least sellers are these red ones right here

sellers are these red ones right here and the top sellers are up here now this

and the top sellers are up here now this is just based on unit sold and this

is just based on unit sold and this looks absolutely terrible so let's kind

looks absolutely terrible so let's kind of take this exact information but make

of take this exact information but make it a little bit better so we're going to

it a little bit better so we're going to create a new visualization or at least a

create a new visualization or at least a new table so let's click on product name

new table so let's click on product name and we'll take the price unit sold and

and we'll take the price unit sold and revenue and what I think makes the most

revenue and what I think makes the most sense for looking at revenue is these

sense for looking at revenue is these data bars right here but there's only

data bars right here but there's only one problem I can't do that because it's

one problem I can't do that because it's not summarized like unit sold was but

not summarized like unit sold was but what I can do is to get that those data

what I can do is to get that those data bars is I can come right down here

bars is I can come right down here instead of saying don't summarize I can

instead of saying don't summarize I can summarize it and I can just click the

summarize it and I can just click the sum so it now was summarized it's the

sum so it now was summarized it's the exact same number but if I right click

exact same number but if I right click on here as sum of Revenue I go to

on here as sum of Revenue I go to conditional formatting I can now use

conditional formatting I can now use those data bars and so we're going to

those data bars and so we're going to use those data bars and we're going to

use those data bars and we're going to say for the lowest value and the highest

say for the lowest value and the highest value and let's just make it a

value and let's just make it a nice maybe a darker green I don't want

nice maybe a darker green I don't want it to well that's that's hideous let's

it to well that's that's hideous let's make it this color right here a nice

make it this color right here a nice dark green and there's no negative so it

dark green and there's no negative so it doesn't really matter we're going to go

doesn't really matter we're going to go left to right and you can show the bar

left to right and you can show the bar only but we're going to keep it because

only but we're going to keep it because I want to see it and we're going to go

I want to see it and we're going to go just like this we're going to

just like this we're going to order and this is pretty telling um

order and this is pretty telling um honestly I did not think the

honestly I did not think the weatherproof jackets were performing so

weatherproof jackets were performing so well but I mean they are by far a number

well but I mean they are by far a number one seller so you know our weatherproof

one seller so you know our weatherproof jackets multitool survival knives and

jackets multitool survival knives and the nylon rope are perform outperforming

the nylon rope are perform outperforming all of our other products so those my

all of our other products so those my might be the ones that I focus on the

might be the ones that I focus on the most while duct tape the n95 masks and

most while duct tape the n95 masks and waterproof matches I mean those are

waterproof matches I mean those are those are garbage so I might be looking

those are garbage so I might be looking to replace those in the near future with

to replace those in the near future with some other items that might sell a

some other items that might sell a little bit better so that's how you use

little bit better so that's how you use conditional formatting and it's actually

conditional formatting and it's actually pretty useful there are a lot of times

pretty useful there are a lot of times where I've done something like this in

where I've done something like this in an actual visualization for work and it

an actual visualization for work and it looks something like this it just

looks something like this it just depends on what you're visualizing but

depends on what you're visualizing but this is very much a simple thing that

this is very much a simple thing that you can do to just add a little bit more

you can do to just add a little bit more information and and actual visual

information and and actual visual to this little chart or table that

to this little chart or table that you're going to create sometimes it's

you're going to create sometimes it's just better to have these simple

just better to have these simple visualizations on this table rather than

visualizations on this table rather than just having the numbers themselves makes

just having the numbers themselves makes it a little bit more easy to read and

it a little bit more easy to read and understand so again I hope that this was

understand so again I hope that this was helpful thank you guys so much for

helpful thank you guys so much for watching I really appreciate it if you

watching I really appreciate it if you like this video be sure to like And

like this video be sure to like And subscribe and check out all my other

subscribe and check out all my other videos on powerbi and I'll see you in

videos on powerbi and I'll see you in the next

the next [Music]

[Music] video

video [Music]

[Music] what's going on everybody welcome back

what's going on everybody welcome back to the powerbi tutorial Series today

to the powerbi tutorial Series today we're going to be taking a look at bins

we're going to be taking a look at bins and

[Music] lists now bins and list are really

lists now bins and list are really useful because they allow you to group

useful because they allow you to group things together to analyze and visualize

things together to analyze and visualize them easier so in this tutorial I'll

them easier so in this tutorial I'll show you how to create your bins and

show you how to create your bins and lists and then we'll create some

lists and then we'll create some visualizations to show you how it can be

visualizations to show you how it can be helpful so without further Ado let's

helpful so without further Ado let's jump on my screen start with a tutorial

jump on my screen start with a tutorial all right so before we get started I

all right so before we get started I wanted to let you know you can go and

wanted to let you know you can go and download the data that we're going to be

download the data that we're going to be using in this tutorial in the

using in this tutorial in the description below is on my GitHub so we

description below is on my GitHub so we are going to be looking at bins and

are going to be looking at bins and lists today um and for this we're going

lists today um and for this we're going to be going over here to this apocalypse

to be going over here to this apocalypse sales uh and let's open up our data

sales uh and let's open up our data right over here and we want to look at

right over here and we want to look at apocalypse sales really quickly I feel

apocalypse sales really quickly I feel like more people would know what a bin

like more people would know what a bin is so we'll kind of start with a list

is so we'll kind of start with a list just go a little bit backwards than we

just go a little bit backwards than we normally would uh I'm going to use this

normally would uh I'm going to use this customer or we're going to use this

customer or we're going to use this customer column right here for a list

customer column right here for a list really quickly and you can do that in

really quickly and you can do that in two ways you can come up here and you

two ways you can come up here and you can right click on the customer and go

can right click on the customer and go to new group or you can come over here

to new group or you can come over here under this uh the Field section on the

under this uh the Field section on the far right and go to customer rightclick

far right and go to customer rightclick and click new group so let's click on

and click new group so let's click on that

that now and right now is only giving us the

now and right now is only giving us the list type it's not giving us bins

list type it's not giving us bins because bins have to be numeric so we

because bins have to be numeric so we really can't do that at the moment um so

really can't do that at the moment um so we're going to call this just customer

we're going to call this just customer groups just or or we'll actually call it

groups just or or we'll actually call it list just so it's easier to recognize

list just so it's easier to recognize when we create it and so all we're going

when we create it and so all we're going to do is we're going to basically group

to do is we're going to basically group these but it's going to be called a list

these but it's going to be called a list and so what we're going to do is we're

and so what we're going to do is we're going to select and we're going to

going to select and we're going to select and we're going to say group and

select and we're going to say group and click on this group button and then it

click on this group button and then it creates this Alex the analyst apocalypse

creates this Alex the analyst apocalypse Preppers and uh this prep for anything

Preppers and uh this prep for anything prepping store so that it kind of named

prepping store so that it kind of named it for us but if we double click on it

it for us but if we double click on it then we can rename this and we can call

then we can rename this and we can call this the best prepping

this the best prepping stores and then we have these last two

stores and then we have these last two and we can we can click on one and then

and we can we can click on one and then click control and click on the other one

click control and click on the other one so we get both of them and then we can

so we get both of them and then we can click group and we can call this and

click group and we can call this and we'll double click and we'll call this

we'll double click and we'll call this the worst prepping stores

the worst prepping stores um and then that's it and that's all we

um and then that's it and that's all we have to do and what we're then going to

have to do and what we're then going to do and if you want to undo this and you

do and if you want to undo this and you want to switch it up and do whatever you

want to switch it up and do whatever you can click on group but we're not going

can click on group but we're not going to do that we're going to click

to do that we're going to click okay and here is the column that it

okay and here is the column that it created and it basically tells us what

created and it basically tells us what list we put it in if it's Uncle Joe's

list we put it in if it's Uncle Joe's Prep shop that's in the worst prepping

Prep shop that's in the worst prepping stores list and if it's the Alex the

stores list and if it's the Alex the analyst apocalypse Preppers that is in

analyst apocalypse Preppers that is in the best prepping stores so it's kind of

the best prepping stores so it's kind of like an if statement you could even

like an if statement you could even create a calculated column do it on this

create a calculated column do it on this customer create an if statement this is

customer create an if statement this is just a lot faster and a lot easier than

just a lot faster and a lot easier than doing that but it basically would do the

doing that but it basically would do the exact same thing now you can use lists

exact same thing now you can use lists as well on things like numeric so let's

as well on things like numeric so let's say we have order

say we have order ID and we'll go to new group and it's

ID and we'll go to new group and it's going to Auto go to bin because

going to Auto go to bin because typically that's what you'll use but you

typically that's what you'll use but you can do list as well and let's say you

can do list as well and let's say you know we want to say we want to call

know we want to say we want to call these like we'll group these and call

these like we'll group these and call these the

these the first um we'll call this the first

first um we'll call this the first customers or the first orders because

customers or the first orders because we're looking at order IDs look at the

we're looking at order IDs look at the first orders and then we will go back

first orders and then we will go back here we're going on the left side we're

here we're going on the left side we're going to click oops we're going to go

going to click oops we're going to go back to the top we're going to hit shift

back to the top we're going to hit shift group all of these and we'll say the

group all of these and we'll say the latest

latest orders and you absolutely can do this um

orders and you absolutely can do this um again this is kind of like an if

again this is kind of like an if statement right so you're saying if it

statement right so you're saying if it falls between this range and this range

falls between this range and this range then it's called the first orders and if

then it's called the first orders and if it's between this range and this other

it's between this range and this other range it's the latest orders um again

range it's the latest orders um again it's just a much simpler version of an

it's just a much simpler version of an if statement and so you don't have to

if statement and so you don't have to write it all out you can just have this

write it all out you can just have this user interface kind of do it for you uh

user interface kind of do it for you uh and and it's really really useful so now

and and it's really really useful so now let's talk about bins and by far the

let's talk about bins and by far the easiest way to demonstrate this and I'll

easiest way to demonstrate this and I'll show you one other way uh but by far the

show you one other way uh but by far the easiest ways to show this is by using

easiest ways to show this is by using age and so uh for absolutely no reason

age and so uh for absolutely no reason whatsoever these customer IDs uh who are

whatsoever these customer IDs uh who are right here in this customer information

right here in this customer information they decided to give us some of their

they decided to give us some of their buyer information who are actually

buyer information who are actually buying their products on their website

buying their products on their website or in their store they just decided to

or in their store they just decided to give it to us as well as some uh simple

give it to us as well as some uh simple demographic information I I don't know

demographic information I I don't know why but what we're going to use bins for

why but what we're going to use bins for is grouping these age brackets so you

is grouping these age brackets so you know you might be interested in say well

know you might be interested in say well I want to know if my core population who

I want to know if my core population who are buying my products are within a

are buying my products are within a certain range and you don't want to look

certain range and you don't want to look look at every single age because then it

look at every single age because then it just you know in your visualizations

just you know in your visualizations it's not going to look right you want to

it's not going to look right you want to kind of group them make it easier to

kind of group them make it easier to visualize so what we're going to do is

visualize so what we're going to do is we're going to go through here and we're

we're going to go through here and we're going to basically go by tens so 10 20

going to basically go by tens so 10 20 30 40 50 60 and see what age bracket

30 40 50 60 and see what age bracket these people fall in so we're going to

these people fall in so we're going to go to age we're going to right click and

go to age we're going to right click and we're going to say new group and we're

we're going to say new group and we're going to go to bin and we'll leave it as

going to go to bin and we'll leave it as a default age bins um and you can do two

a default age bins um and you can do two things you can do the size of the bins

things you can do the size of the bins which splits it uh uh which splits it by

which splits it uh uh which splits it by this number right here or you can go

this number right here or you can go based on the number of bins so if you

based on the number of bins so if you only want to do five different bins

only want to do five different bins it'll calculate that for you and it'll

it'll calculate that for you and it'll say okay if you only want five bins

say okay if you only want five bins you're going to have to do it at 12.2 if

you're going to have to do it at 12.2 if you want 10 bins it can be 6.1 but it is

you want 10 bins it can be 6.1 but it is completely up to you on how you want to

completely up to you on how you want to do that um you can do the size and we'll

do that um you can do the size and we'll just say every 10 which is what we're

just say every 10 which is what we're going to do or you can go through and

going to do or you can go through and then you can create you know the how

then you can create you know the how many many bins you actually want so

many many bins you actually want so let's go ahead and click okay and it's

let's go ahead and click okay and it's going to create those bins for us so if

going to create those bins for us so if somebody is 78 they're going to be in

somebody is 78 they're going to be in the 70s bin if somebody's 41 they'll be

the 70s bin if somebody's 41 they'll be in the 40 bin if somebody is 29 they'll

in the 40 bin if somebody is 29 they'll be in the 20 bin and so on and so forth

be in the 20 bin and so on and so forth so when we go to visualize this we don't

so when we go to visualize this we don't have you know 71 72 73 74 have a lot

have you know 71 72 73 74 have a lot more things on our visualization it'll

more things on our visualization it'll just be the 70 or it'll just be the 20

just be the 70 or it'll just be the 20 now we can also use bins on dates as

now we can also use bins on dates as well so let's go back to apocalypse

well so let's go back to apocalypse sales we have this date purchase so we

sales we have this date purchase so we can create a bin for this as well so

can create a bin for this as well so let's go to date purchased let's go new

let's go to date purchased let's go new group now you can also create a list and

group now you can also create a list and that's totally fine if you would like to

that's totally fine if you would like to do that um and it would look kind of

do that um and it would look kind of like this where you can go through and

like this where you can go through and you can select it and you can say okay

you can select it and you can say okay this group all these dates you can group

this group all these dates you can group those and say this is going to be

those and say this is going to be January uh and you can do that and

January uh and you can do that and that's totally okay um but for this one

that's totally okay um but for this one we're going to do bins I think it's a

we're going to do bins I think it's a little bit easier to do bins because

little bit easier to do bins because what we can do is go right here and we

what we can do is go right here and we can specify if we want seconds minutes

can specify if we want seconds minutes hours days months or years and so um for

hours days months or years and so um for the data that we have it goes January

the data that we have it goes January February and March so we're going to do

February and March so we're going to do months and we're going to say the bin

months and we're going to say the bin size is going to be one month so each

size is going to be one month so each month should have its own bin so it'll

month should have its own bin so it'll be three bins total so we're going to

be three bins total so we're going to select

select okay and as you can see on this right

okay and as you can see on this right side we have January of 2022 and that

side we have January of 2022 and that correlates to the January over here then

correlates to the January over here then it goes down to February and then it

it goes down to February and then it goes down to March and then when we

goes down to March and then when we visualize this uh we don't have to do

visualize this uh we don't have to do this the hierarchy stuff that we do in

this the hierarchy stuff that we do in here where we filter it down down to

here where we filter it down down to months we can just use this right here

months we can just use this right here and that will be our month's column so

and that will be our month's column so now let's go over to our visualizations

now let's go over to our visualizations and we'll see how this looks really

and we'll see how this looks really quickly we're not going to look at all

quickly we're not going to look at all of them but we will take a look at few

of them but we will take a look at few of them so the first one that we can

of them so the first one that we can look at is age so let's look at the

look at is age so let's look at the buyer ID and then we'll do age as well

buyer ID and then we'll do age as well and so let's spread this

and so let's spread this out and we can see our distribution of

out and we can see our distribution of our buyers so it looks like we have very

our buyers so it looks like we have very few uh who are in the 10 range thank

few uh who are in the 10 range thank goodness and we can even put the age

goodness and we can even put the age right under here under the age bins and

right under here under the age bins and we have this now we kind of have this

we have this now we kind of have this drill down and so if we go right here

drill down and so if we go right here and we drill down right there this will

and we drill down right there this will actually give us the breakdown so this

actually give us the breakdown so this is what it would have kind of looked

is what it would have kind of looked like our visualization would have looked

like our visualization would have looked like if we had just kept it the age cuz

like if we had just kept it the age cuz now we're drilling down into the age and

now we're drilling down into the age and so it looks like we have one 18-year-old

so it looks like we have one 18-year-old and maybe a 20-year-old as well um let's

and maybe a 20-year-old as well um let's go back up yeah so it looks like we only

go back up yeah so it looks like we only have one buyer ID yes so there's only

have one buyer ID yes so there's only one 18year old so of legal age to start

one 18year old so of legal age to start buying you know all these prepping

buying you know all these prepping equipment and probably uh buying online

equipment and probably uh buying online and stuff like that which makes sense

and stuff like that which makes sense right so uh this gives you kind of a

right so uh this gives you kind of a quick breakdown in the bins rather than

quick breakdown in the bins rather than um doing it the alternative way so now

um doing it the alternative way so now let's take a look at the customer list

let's take a look at the customer list as well as the unit sold and it looks

as well as the unit sold and it looks like the best prepping store uh is

like the best prepping store uh is actually performing much worse

actually performing much worse surprisingly uh than the worst prepping

surprisingly uh than the worst prepping store and so I hope this gave you a

store and so I hope this gave you a really good idea of how to use bins and

really good idea of how to use bins and lists within powerbi thank you so much

lists within powerbi thank you so much for watching if you like this video be

for watching if you like this video be sure to like And subscribe and check out

sure to like And subscribe and check out all my other videos on powerbi I'll see

all my other videos on powerbi I'll see you in the next

you in the next [Music]

[Music] video

video [Music]

[Music] what's going on everybody welcome back

what's going on everybody welcome back to the powerbi tutorial Series today

to the powerbi tutorial Series today we're going to be taking a look at all

we're going to be taking a look at all types of

[Music] visualizations now when you're working

visualizations now when you're working in powerbi there are a lot of different

in powerbi there are a lot of different options to create visualizations and you

options to create visualizations and you may not always be sure which one to use

may not always be sure which one to use and so that's what this video is for I'm

and so that's what this video is for I'm going to walk you through a lot of the

going to walk you through a lot of the visualizations that I like and I use a

visualizations that I like and I use a lot as well as kind of point out some of

lot as well as kind of point out some of the ones that I don't like as much so

the ones that I don't like as much so that you get kind of a feel for the ones

that you get kind of a feel for the ones that I think are really popular and that

that I think are really popular and that are used the most so without further Ado

are used the most so without further Ado let's jump into powerbi and start taking

let's jump into powerbi and start taking a look all right before we jump into it

a look all right before we jump into it there is a link in the description where

there is a link in the description where you can get the data that we're going to

you can get the data that we're going to be using for these visualizations if you

be using for these visualizations if you want to practice them yourself before we

want to practice them yourself before we actually get into it we do need to

actually get into it we do need to combine this and if you download that

combine this and if you download that Excel and you see this you'll have to do

Excel and you see this you'll have to do the same thing all we have to say is

the same thing all we have to say is that this product ID is the same as this

that this product ID is the same as this product ID purchased and now we are good

product ID purchased and now we are good to go do one to many and it's okay if

to go do one to many and it's okay if it's one way so right over here under

it's one way so right over here under this visualizations tab there are lots

this visualizations tab there are lots of different options and it can be a

of different options and it can be a little bit overwhelming you don't really

little bit overwhelming you don't really know which one to choose there are some

know which one to choose there are some in here that I have almost never used

in here that I have almost never used for my job ever so I'll Point those out

for my job ever so I'll Point those out as we go through but the main focus is

as we go through but the main focus is going to be focusing on the ones that I

going to be focusing on the ones that I do use that I have used and showing you

do use that I have used and showing you how to actually create that

how to actually create that visualization Maybe spice it up just a

visualization Maybe spice it up just a little bit but we have a lot of them to

little bit but we have a lot of them to go through so let's jump right into it

go through so let's jump right into it and the very first one that we're going

and the very first one that we're going to start with probably the easiest one

to start with probably the easiest one and the one that you'll recognize the

and the one that you'll recognize the most is a stacked bar chart and what we

most is a stacked bar chart and what we going to do is go ahead right over here

going to do is go ahead right over here to the product name and we want this

to the product name and we want this unit sold as well so we're going to

unit sold as well so we're going to click product name and it's going to go

click product name and it's going to go straight into the Y AIS for us and then

straight into the Y AIS for us and then we're going to click unit sold and that

we're going to click unit sold and that will go into the x-axis automatically it

will go into the x-axis automatically it just kind of intuitively knows but

just kind of intuitively knows but sometimes it will make a mistake and

sometimes it will make a mistake and then you can just fix it or flip it and

then you can just fix it or flip it and we do want this uh let me make this much

we do want this uh let me make this much larger we do want this to be a little

larger we do want this to be a little bit more colorcoded that is what this

bit more colorcoded that is what this Legend is down here so what we're going

Legend is down here so what we're going to do is drag this product name down to

to do is drag this product name down to the legend and now we have each product

the legend and now we have each product as its own

as its own color and in previous videos we have

color and in previous videos we have gone through and looked at some of these

gone through and looked at some of these Visual and general options that you have

Visual and general options that you have when you're actually creating these

when you're actually creating these visualizations but we're going to do

visualizations but we're going to do some of them while we're in here as well

some of them while we're in here as well so we're just going to go down here

so we're just going to go down here we're going to choose data labels and

we're going to choose data labels and we're going to shrink that and if you go

we're going to shrink that and if you go higher the higher you go the less you

higher the higher you go the less you see so if you want all of them all the

see so if you want all of them all the way down to the green we're going to go

way down to the green we're going to go right about there and we're going to

right about there and we're going to make it smaller so now we can go ahead

make it smaller so now we can go ahead and click anywhere outside of that

and click anywhere outside of that visualization and now we can create a

visualization and now we can create a new one if we had just kept it like this

new one if we had just kept it like this where we were still interacting with

where we were still interacting with this visualization and we clicked on a

this visualization and we clicked on a different one it would have then changed

different one it would have then changed our visualization completely which we

our visualization completely which we don't want so let's hit contrl Z click

don't want so let's hit contrl Z click out of it and now we can create a new

out of it and now we can create a new one let's go right over here to this

one let's go right over here to this 100% stacked column chart I'm going to

100% stacked column chart I'm going to click on it drag it over here and make

click on it drag it over here and make it much larger and we're going to come

it much larger and we're going to come right over here to this customer

right over here to this customer information and we're going to click on

information and we're going to click on customer and then we're going to go up

customer and then we're going to go up to unit sold and click on unit sold and

to unit sold and click on unit sold and we want to break these out and so

we want to break these out and so basically what this is doing is it's

basically what this is doing is it's breaking it out by each of these shops

breaking it out by each of these shops and we can see the total of what they're

and we can see the total of what they're buying the units sold but we want to see

buying the units sold but we want to see exactly what products make up this

exactly what products make up this percentage of this 100% so we're going

percentage of this 100% so we're going to go right over here to product name

to go right over here to product name we're going to drag that down to the

we're going to drag that down to the legend and as you can see now we have

legend and as you can see now we have each of these products and each of the

each of these products and each of the products is up here so this backpack we

products is up here so this backpack we can see the backpack right here backpack

can see the backpack right here backpack right here and right here and we can see

right here and right here and we can see which customer is buying what percentage

which customer is buying what percentage of their purchases so for this prep for

of their purchases so for this prep for anything prep store they have a very

anything prep store they have a very large percentage 40% is duct tape so

large percentage 40% is duct tape so they're buying a lot of duct tape so

they're buying a lot of duct tape so really quickly we're able to see what

really quickly we're able to see what clients are purchasing or which clients

clients are purchasing or which clients are purchasing what products the most so

are purchasing what products the most so just like this Alex analyst apocalypse

just like this Alex analyst apocalypse Preppers they're buying a lot of water

Preppers they're buying a lot of water purifiers we like drinking clean water

purifiers we like drinking clean water um you know that's just what my audience

um you know that's just what my audience likes and so you know we can easily get

likes and so you know we can easily get a quick glance of that again we're going

a quick glance of that again we're going to go in here I tend to like putting

to go in here I tend to like putting these data labels on here that's just

these data labels on here that's just what I preference

what I preference so you know something like this it looks

so you know something like this it looks nice it looks clean um we can always go

nice it looks clean um we can always go back and change these names which we'll

back and change these names which we'll do for this one so we're going to go

do for this one so we're going to go over here go to title we'll go down to

over here go to title we'll go down to the text and we'll do

the text and we'll do customer

customer oops customer purchase oh jeez

oops customer purchase oh jeez breakdown pretend I'm really good at

breakdown pretend I'm really good at spelling and we're going to do it just

spelling and we're going to do it just like that we'll get out of there so now

like that we'll get out of there so now we have customer purchase breakdown and

we have customer purchase breakdown and that looks really nice it's a good uh a

that looks really nice it's a good uh a good visualization and we're going to

good visualization and we're going to bring that right over here we're going

bring that right over here we're going to have a lot on the screen so I may

to have a lot on the screen so I may have to uh make them smaller or larger

have to uh make them smaller or larger to fit

to fit everything all right so let's go on to

everything all right so let's go on to our next one another really common

our next one another really common visualization is this one right here

visualization is this one right here which is the line chart and the line

which is the line chart and the line chart is great especially when you're

chart is great especially when you're using things like dates I have found

using things like dates I have found this one to be the best best and a lot

this one to be the best best and a lot of people use this as well so we're

of people use this as well so we're going to go right over here and click on

going to go right over here and click on date purchased and then units sold and

date purchased and then units sold and on the x-axis you can see it's broken up

on the x-axis you can see it's broken up by year quarter month and day so we

by year quarter month and day so we don't want to do it that high level we

don't want to do it that high level we only have three months of data in here

only have three months of data in here so we're going to get rid of the year

so we're going to get rid of the year we're going to get rid of the quarter

we're going to get rid of the quarter and then we at least have this and let's

and then we at least have this and let's break it out because right now we're

break it out because right now we're looking at all of the units sold so

looking at all of the units sold so we're going to drag the product name

we're going to drag the product name right down here to the legend and now it

right down here to the legend and now it breaks it out by the actual product and

breaks it out by the actual product and for each month in January February or

for each month in January February or March you can follow these products and

March you can follow these products and see how they did in each of those months

see how they did in each of those months and if we wanted to we can come right

and if we wanted to we can come right over here to the filter on the product

over here to the filter on the product name and we could filter it by maybe the

name and we could filter it by maybe the top three so let's do multi-tool

top three so let's do multi-tool survival knife the nylon rope and the

survival knife the nylon rope and the duct tape and we can have it just like

duct tape and we can have it just like this and you know you can do those for

this and you know you can do those for any product that you want but again we

any product that you want but again we just want to do it for those three just

just want to do it for those three just for an example and that really doesn't

for an example and that really doesn't give us a ton of information we could

give us a ton of information we could even go down to the day and you know it

even go down to the day and you know it might give us a little bit more

might give us a little bit more information and so we'll keep it like

information and so we'll keep it like that and we can go over here change the

that and we can go over here change the name as well we're not going to do this

name as well we're not going to do this for all of them again we're just looking

for all of them again we're just looking at the different types of visualizations

at the different types of visualizations I think are really good to know but

I think are really good to know but we'll change this one as well to

we'll change this one as well to products

products purchased by

purchased by date we'll keep it just like that again

date we'll keep it just like that again nothing fancy we're just trying to look

nothing fancy we're just trying to look at a bunch of different stuff so let's

at a bunch of different stuff so let's put this over here down here now let's

put this over here down here now let's click out of there and there are other

click out of there and there are other ones in here um that are definitely

ones in here um that are definitely useful and you absolutely can use um

useful and you absolutely can use um like this one is a stacked bar chart

like this one is a stacked bar chart this one is a stacked column chart it's

this one is a stacked column chart it's basically the same thing just a

basically the same thing just a different orientation like we went to

different orientation like we went to here it's just a different orientation

here it's just a different orientation it's the same thing um just like this

it's the same thing um just like this clustered bar chart custom column chart

clustered bar chart custom column chart it's just its orientation either

it's just its orientation either horizontal or

horizontal or vertical then we have things like an

vertical then we have things like an area chart uh stacked area chart not

area chart uh stacked area chart not really things that I've used too much in

really things that I've used too much in previous positions one that I have use

previous positions one that I have use though is a line and clustered column

though is a line and clustered column chart so it kind of combines a few of

chart so it kind of combines a few of these with you know you have these bar

these with you know you have these bar charts as well as line charts into one

charts as well as line charts into one visualization so let's look at this one

visualization so let's look at this one because this is one that I have used

because this is one that I have used several times in my actual job so for

several times in my actual job so for our x axis we'll use the product name

our x axis we'll use the product name then we'll look at something like the

then we'll look at something like the price and so let's make this a lot

price and so let's make this a lot larger so you can actually see it so now

larger so you can actually see it so now we have the price and now we can look at

we have the price and now we can look at something like the production cost and

something like the production cost and that can

that can be our line ya AIS so now we're looking

be our line ya AIS so now we're looking at the price of it how much someone is

at the price of it how much someone is actually paying for it and then we're

actually paying for it and then we're looking at how much it's costing us to

looking at how much it's costing us to actually produce that product and so

actually produce that product and so really quickly at a glance you can kind

really quickly at a glance you can kind of see that it's around the halfway to

of see that it's around the halfway to 2/3 point on most of these you can see

2/3 point on most of these you can see that the production cost is always lower

that the production cost is always lower than the actual price because of course

than the actual price because of course we're out here to make a profit on these

we're out here to make a profit on these products so let's minimize this one

products so let's minimize this one we're going to put this one right down

we're going to put this one right down here let's make it even smaller let's

here let's make it even smaller let's click out of that and the next one that

click out of that and the next one that we're going to take a look at is a

we're going to take a look at is a scatter chart so let's click on that and

scatter chart so let's click on that and make it much larger

make it much larger oops there we go so let's use the price

oops there we go so let's use the price and the production cost again and so our

and the production cost again and so our x axis is the price our y y AIS is the

x axis is the price our y y AIS is the production cost but now we need to fill

production cost but now we need to fill in this values right here so let's go

in this values right here so let's go over here and click on the product name

over here and click on the product name and drag that into values and so now we

and drag that into values and so now we have our values we just don't know what

have our values we just don't know what they are but we can see it so let's drag

they are but we can see it so let's drag this down to Legend as well and it

this down to Legend as well and it breaks it out and we kind of have this

breaks it out and we kind of have this scatter plot and you know for this fake

scatter plot and you know for this fake data that we're using it doesn't really

data that we're using it doesn't really show a lot U but if you're using real

show a lot U but if you're using real data you can definitely find outliers

data you can definitely find outliers and Trends and patterns using this type

and Trends and patterns using this type of visualization let's go ahead and make

of visualization let's go ahead and make that one small as well drag it right

that one small as well drag it right down into the

down into the corner now let's go right over here and

corner now let's go right over here and we have the the dreaded pie charts um

we have the the dreaded pie charts um and dut chart now look I think it's kind

and dut chart now look I think it's kind of a joke in the data analyst Community

of a joke in the data analyst Community about pie charts and doughnut charts but

about pie charts and doughnut charts but at the same time people use them and

at the same time people use them and they request them and so sometimes

they request them and so sometimes you're going to use it whether you like

you're going to use it whether you like it or not so let's click on the dut

it or not so let's click on the dut chart and let's make this one a lot

chart and let's make this one a lot larger and let's go over here and let's

larger and let's go over here and let's click on

click on State and we're also going to click on

State and we're also going to click on total purchased and that's really all

total purchased and that's really all you have to do these ones are pretty

you have to do these ones are pretty straightforward you can change a few

straightforward you can change a few different things like where these labels

different things like where these labels are if you want them inside you can also

are if you want them inside you can also do that and that would look totally fine

do that and that would look totally fine um again I'm just not a super huge fan

um again I'm just not a super huge fan but you will get this one requested

but you will get this one requested people like this and want to see it and

people like this and want to see it and the reason a lot of analysts don't like

the reason a lot of analysts don't like using this is because when you start

using this is because when you start glancing at these it's really hard to

glancing at these it's really hard to tell the difference between these sizes

tell the difference between these sizes if you look at something like this you

if you look at something like this you can easily see that this is larger like

can easily see that this is larger like if you're looking at this one the

if you're looking at this one the multi-tool survival knife is obviously

multi-tool survival knife is obviously the longest and it gets shorter shorter

the longest and it gets shorter shorter shorter shorter but when you start

shorter shorter but when you start getting in here it's really hard to

getting in here it's really hard to approximate the size I would not be able

approximate the size I would not be able to tell the difference between this 5.63

to tell the difference between this 5.63 5.78 two uh 7.72 I would not be able to

5.78 two uh 7.72 I would not be able to tell really the difference between these

tell really the difference between these or or kind of the the difference between

or or kind of the the difference between them very easily that's why a lot of

them very easily that's why a lot of people don't want to use them in general

people don't want to use them in general so again I want to show you this one

so again I want to show you this one because I think it's worth noting and

because I think it's worth noting and worth knowing how to use but I don't

worth knowing how to use but I don't really push people towards this because

really push people towards this because I don't think it's the best

I don't think it's the best visualization available most of the time

visualization available most of the time all right the next two are super easy

all right the next two are super easy but are used all the time uh maybe more

but are used all the time uh maybe more than some of these even but they're just

than some of these even but they're just so easy to use so I'm kind of saved them

so easy to use so I'm kind of saved them for last this one is the card and all

for last this one is the card and all the card is is it displays one number or

the card is is it displays one number or multiple numbers if you want to use a

multiple numbers if you want to use a multi- card but we'll just look at the

multi- card but we'll just look at the card for now all we're going to look at

card for now all we're going to look at is the total purchased and it's just

is the total purchased and it's just going to display it just like this and

going to display it just like this and you can make it as large or as small as

you can make it as large or as small as you'd like and normally it goes on like

you'd like and normally it goes on like the top and you'll put card here a card

the top and you'll put card here a card here um just for example I'll kind of

here um just for example I'll kind of show you how this might look so it look

show you how this might look so it look something like this right and at the top

something like this right and at the top it'll have different usually High

it'll have different usually High overarching information and this is

overarching information and this is super common to see and I'm sure if

super common to see and I'm sure if you've looked at other people's

you've looked at other people's visualization you'll see something like

visualization you'll see something like this this is usually totals or averages

this this is usually totals or averages or something like that in here where

or something like that in here where it's super easy to look at so like right

it's super easy to look at so like right here this is total purchased and we can

here this is total purchased and we can go in and look at the minimum and then

go in and look at the minimum and then we can go over here and this one can be

we can go over here and this one can be account and so it gives us a lot of

account and so it gives us a lot of information just at a really quick

information just at a really quick glance and then we have all of our more

glance and then we have all of our more in-depth colorful visualizations that

in-depth colorful visualizations that kind of have more information than just

kind of have more information than just a single piece like the card does and

a single piece like the card does and then the very last one that I'm going to

then the very last one that I'm going to show you is this one right here which is

show you is this one right here which is the table and this one is obviously

the table and this one is obviously extremely popular it's like an little

extremely popular it's like an little Excel table and we can go in here and we

Excel table and we can go in here and we can get the customer wherever that is

can get the customer wherever that is and then we'll also get the unit sold

and then we'll also get the unit sold and this is what it looks like and it's

and this is what it looks like and it's super easy and oftentimes you'll have it

super easy and oftentimes you'll have it like on the side as well uh and all the

like on the side as well uh and all the other visualizations over here and so

other visualizations over here and so you know if we're going to take all

you know if we're going to take all these visualizations and pretend they

these visualizations and pretend they were like a real thing you know there's

were like a real thing you know there's a lot in here but we'll just kind of

a lot in here but we'll just kind of really quickly do this um you know we

really quickly do this um you know we might have something like this and we'll

might have something like this and we'll make this larger and make this

make this larger and make this wider and you know we have a lot of

wider and you know we have a lot of information just in here and this is not

information just in here and this is not a project so don't go put this on your

a project so don't go put this on your portfolio I'm just threw a ton of random

portfolio I'm just threw a ton of random visualizations on you know this

visualizations on you know this dashboard but you can already see a lot

dashboard but you can already see a lot of these you most likely have seen in

of these you most likely have seen in other people's work in other people's

other people's work in other people's visualizations on LinkedIn or on YouTube

visualizations on LinkedIn or on YouTube these are very common very very popular

these are very common very very popular and again we did not go through all of

and again we did not go through all of the ones over here there are maps that

the ones over here there are maps that you can use but I haven't used Maps ever

you can use but I haven't used Maps ever in my job there are things like gauges

in my job there are things like gauges and decomposition trees and waterfall

and decomposition trees and waterfall charts and uh tree maps and all these

charts and uh tree maps and all these different things but I really have never

different things but I really have never used those in my actual job and I don't

used those in my actual job and I don't see them a lot in others people's work

see them a lot in others people's work either otherwise I would be telling you

either otherwise I would be telling you to learn these and use these but again

to learn these and use these but again try them out see which ones you like if

try them out see which ones you like if you like this video be sure to like And

you like this video be sure to like And subscribe below and go check out all the

subscribe below and go check out all the other powerbi tutorial videos that I

other powerbi tutorial videos that I have on my channel and I will see you in

have on my channel and I will see you in the

the [Music]

[Music] next what's going on everybody welcome

next what's going on everybody welcome back to the powerbi tutorial Series

back to the powerbi tutorial Series today we are going to be working on our

today we are going to be working on our final

now this is our final project of the powerbi tutorial Series so if you have

powerbi tutorial Series so if you have not watched all of those videos leading

not watched all of those videos leading up to this I recommend going and

up to this I recommend going and watching those videos so you can make

watching those videos so you can make sure that you know all the things that

sure that you know all the things that we're going to be looking at in today's

we're going to be looking at in today's project I am really excited to work on

project I am really excited to work on this project with you because I think it

this project with you because I think it is a really good one and it uses real

is a really good one and it uses real data that we collected about a month ago

data that we collected about a month ago where I took a survey of data

where I took a survey of data professionals and this is the raw data

professionals and this is the raw data that we're going to be looking at and so

that we're going to be looking at and so I think it's just really interesting

I think it's just really interesting that we collected our own data and now

that we collected our own data and now we're using for a project we're going to

we're using for a project we're going to transform the data using power query and

transform the data using power query and then we're actually create the

then we're actually create the visualizations and finalize the

visualizations and finalize the dashboards as well as create a theme and

dashboards as well as create a theme and a different color scheme to kind of make

a different color scheme to kind of make it a little bit more unique without

it a little bit more unique without further Ado let's jump onto my screen

further Ado let's jump onto my screen and get started with the project all

and get started with the project all right so before we jump into it I wanted

right so before we jump into it I wanted to let you know that you can get the

to let you know that you can get the data below it is on my GitHub you can go

data below it is on my GitHub you can go and download this exact file that we're

and download this exact file that we're going to be looking at now in the past

going to be looking at now in the past several projects we have been using this

several projects we have been using this fake apocalypse data set you know it was

fake apocalypse data set you know it was fun it was you know what whatever this

fun it was you know what whatever this data set is real this is a real data set

data set is real this is a real data set it was a survey that I took from data

it was a survey that I took from data professionals I posted on LinkedIn and

professionals I posted on LinkedIn and Twitter and all these other places and

Twitter and all these other places and we had about 600 700 people who

we had about 600 700 people who responded to the questions so before we

responded to the questions so before we actually get into it and start cleaning

actually get into it and start cleaning the data and doing all this stuff in

the data and doing all this stuff in powerbi I just wanted to show you the

powerbi I just wanted to show you the data all right so this is the CSV that I

data all right so this is the CSV that I downloaded from the survey website that

downloaded from the survey website that I used and this is completely raw data I

I used and this is completely raw data I haven't done anything to it at all let's

haven't done anything to it at all let's go through the data really quickly and

go through the data really quickly and we'll kind of see what we have and we

we'll kind of see what we have and we are not going to make any changes at all

are not going to make any changes at all in Excel we're going to do all of our

in Excel we're going to do all of our Transformations or at least a few

Transformations or at least a few transformations in powerbi because again

transformations in powerbi because again this is a powerbi tutorial and project

this is a powerbi tutorial and project so I want you to kind of learn how to

so I want you to kind of learn how to use that and not use Excel because you

use that and not use Excel because you can go through my Excel tutorial if you

can go through my Excel tutorial if you want to do that so let's just look at it

want to do that so let's just look at it in Excel and then we'll move it over to

in Excel and then we'll move it over to powerbi and actually start transforming

powerbi and actually start transforming the data so we have this unique ID these

the data so we have this unique ID these are all the people that actually took it

are all the people that actually took it oops don't want to do that we have an

oops don't want to do that we have an email which this was completely

email which this was completely Anonymous I didn't collect any data or

Anonymous I didn't collect any data or user data on this then we have the date

user data on this then we have the date Taken um and let's get into the actual

Taken um and let's get into the actual good information then we have all of

good information then we have all of these questions so we have question one

these questions so we have question one which title fits you best and they can

which title fits you best and they can choose things now uh let's add a filter

choose things now uh let's add a filter really quickly that we can look at this

really quickly that we can look at this now you had the pre-selected ones which

now you had the pre-selected ones which were like data analyst architect

were like data analyst architect engineer but then there was an option

engineer but then there was an option where you could say other and you could

where you could say other and you could spe specify what that was so if you look

spe specify what that was so if you look in here we're going to have all these

in here we're going to have all these different other please specify with

different other please specify with different titles right and there were a

different titles right and there were a lot of them now typically what you want

lot of them now typically what you want to do is really clean this up and we're

to do is really clean this up and we're not going to be doing a ton ton ton of

not going to be doing a ton ton ton of data cleaning but we are going to do

data cleaning but we are going to do some in powerbi but none in here but

some in powerbi but none in here but typically with this amount of data and

typically with this amount of data and the way that it's formatted we would do

the way that it's formatted we would do so much data cleaning um with this one I

so much data cleaning um with this one I mean I mean there is a lot of work to be

mean I mean there is a lot of work to be done um like this current year salary

done um like this current year salary this is one that I would absolutely be

this is one that I would absolutely be cleaning up because it's ranges and it

cleaning up because it's ranges and it has a dash and a k and and all these

has a dash and a k and and all these numbers this is something that I would

numbers this is something that I would be cleaning up and using but we're not

be cleaning up and using but we're not going to be cleaning this up right now

going to be cleaning this up right now so anyways let's just get into it let's

so anyways let's just get into it let's see what questions we asked uh we have

see what questions we asked uh we have the yearly salary what industry do you

the yearly salary what industry do you work in favorite programming

work in favorite programming language then there were a lot of

language then there were a lot of different options this is like one

different options this is like one question where they picked multiple

question where they picked multiple options so is how happy are you in your

options so is how happy are you in your current position with the following you

current position with the following you have your salary work life

have your salary work life balance um then we have co-workers

balance um then we have co-workers management upward Mobility learning new

management upward Mobility learning new things um and they could rank it from

things um and they could rank it from zero to 10 so some people ranked upward

zero to 10 so some people ranked upward Mobility a 10 some ranked it a zero or a

Mobility a 10 some ranked it a zero or a one um and again they can answer however

one um and again they can answer however they want how difficult was it to break

they want how difficult was it to break into Data very very difficult very easy

into Data very very difficult very easy um if you're looking for a new job we

um if you're looking for a new job we have you know what would you be looking

have you know what would you be looking for remote work better salary Etc we

for remote work better salary Etc we have male female which country you from

have male female which country you from and then this is more like demographics

and then this is more like demographics so if you're a male how old you are and

so if you're a male how old you are and this was in a Range so this is like a a

this was in a Range so this is like a a a sliding bar so you could slide it to

a sliding bar so you could slide it to the exact age you had there's some

the exact age you had there's some people who are apparently 92 um which if

people who are apparently 92 um which if that's true I mean good for you man or

that's true I mean good for you man or woman actually really quickly I'm going

woman actually really quickly I'm going to see just just while we're here I'm

to see just just while we're here I'm going to see if this is a male male or a

going to see if this is a male male or a female oh it's a female from India very

female oh it's a female from India very cool um so we have all this information

cool um so we have all this information and it is a lot of information when you

and it is a lot of information when you have something like this I mean there is

have something like this I mean there is so much data cleaning that can be done I

so much data cleaning that can be done I mean I already see like 20 plus

mean I already see like 20 plus different things that I would need to do

different things that I would need to do to make this a lot better um and we also

to make this a lot better um and we also have date Taken and the time taken as as

have date Taken and the time taken as as well as how long it they took on it like

well as how long it they took on it like the time spent really just really

the time spent really just really interesting data but again this is a

interesting data but again this is a beginner tutorial Series this is the

beginner tutorial Series this is the beginner project so we're not going to

beginner project so we're not going to get do anything too crazy I will be

get do anything too crazy I will be using this exact data set in a future

using this exact data set in a future video doing a lot more data cleaning and

video doing a lot more data cleaning and creating a much more advanced

creating a much more advanced visualization with what we have and what

visualization with what we have and what we're looking at right here but for this

we're looking at right here but for this video we're just going to be doing a

video we're just going to be doing a pretty simple visualization and D

pretty simple visualization and D dashboard that you can use uh to

dashboard that you can use uh to practice with or put on your portfolio

practice with or put on your portfolio if you know that's where you're at right

if you know that's where you're at right now so let's get out of here and let's

now so let's get out of here and let's put this into powerbi so let's exit out

put this into powerbi so let's exit out and let's come right over here to import

and let's come right over here to import data from Excel we'll click on powerbi

data from Excel we'll click on powerbi final project and

final project and open give that a second doing this all

open give that a second doing this all in real time we only have the one so

in real time we only have the one so we'll do be we won't be practicing any

we'll do be we won't be practicing any joins or anything but we're not going to

joins or anything but we're not going to load it we're going to transform this

load it we're going to transform this data so let's put it into to power query

data so let's put it into to power query editor and now we have all of our data

editor and now we have all of our data in here and it should look extremely

in here and it should look extremely familiar now when I'm looking at this

familiar now when I'm looking at this when I start looking at this information

when I start looking at this information I kind of need to know beforehand what I

I kind of need to know beforehand what I want to get out of this do I need to

want to get out of this do I need to clean every single column do I just need

clean every single column do I just need to clean a few of them do I need to get

to clean a few of them do I need to get rid of columns that's kind of where my

rid of columns that's kind of where my head's at and so right off the bat I can

head's at and so right off the bat I can already tell you that there are columns

already tell you that there are columns that we can just delete to get out of

that we can just delete to get out of our way so we're going to do that at the

our way so we're going to do that at the beginning so that we don't have to do

beginning so that we don't have to do that later on or they're just in our way

that later on or they're just in our way so I'm going to click on browser and

so I'm going to click on browser and then I'm going to hit shift and I'm

then I'm going to hit shift and I'm going to go over here to

going to go over here to refer and I'm just going to go up here

refer and I'm just going to go up here to remove columns and everything that we

to remove columns and everything that we do is going to go over here to this

do is going to go over here to this applied steps if you've been following

applied steps if you've been following this series um you know we can remove

this series um you know we can remove things add things but anything we do

things add things but anything we do will show up right over here so we can

will show up right over here so we can track it and go back if we need to now

track it and go back if we need to now one column that I know for sure that I'm

one column that I know for sure that I'm going to be using quite a bit is this

going to be using quite a bit is this which title fits you best in your

which title fits you best in your current role because I I specifically

current role because I I specifically wanted to do a breakdown of different

wanted to do a breakdown of different people's roles and how much they make

people's roles and how much they make and different stuff like that so I know

and different stuff like that so I know that I want to use this but as we saw

that I want to use this but as we saw before there's kind of the issue is is

before there's kind of the issue is is it's not very clean right it has data

it's not very clean right it has data analyst data architect engineer

analyst data architect engineer scientist databased developer and then

scientist databased developer and then like a hundred different options and

like a hundred different options and then a student or or none of these right

um and so for the purpose of this video right here we are not going to take

right here we are not going to take every single one of these options

every single one of these options because this involves a lot more data

because this involves a lot more data cleaning let me give you an example this

cleaning let me give you an example this says software engineer this also says

says software engineer this also says software engineer and with AI these two

software engineer and with AI these two would typically be combined or

would typically be combined or standardized to software engineer but

standardized to software engineer but it's not very easy to do that in powerbi

it's not very easy to do that in powerbi we could do that in Excel but not really

we could do that in Excel but not really in powerbi or even SQL if we pull this

in powerbi or even SQL if we pull this from a SQL database um and you can find

from a SQL database um and you can find lots of different you know options of

lots of different you know options of that we have data manager and data

that we have data manager and data manager if we separated these out these

manager if we separated these out these would be different options when we

would be different options when we created our visualizations and we don't

created our visualizations and we don't want that so what we are going to do uh

want that so what we are going to do uh and this is going to be kind of a an

and this is going to be kind of a an easy way out to just make sure that this

easy way out to just make sure that this is pretty clean and doesn't we don't

is pretty clean and doesn't we don't have a thousand different options we're

have a thousand different options we're going to create this to other so we're

going to create this to other so we're to simplify this a lot and then we're

to simplify this a lot and then we're going to use this so we'll have maybe

going to use this so we'll have maybe six or seven options instead of the you

six or seven options instead of the you know let's say 50 that we would have if

know let's say 50 that we would have if we actually did the harder work which

we actually did the harder work which just break it out standardize it and

just break it out standardize it and clean it up that way so what we're going

clean it up that way so what we're going to do is we're going to click on this

to do is we're going to click on this right here and we're going to go up here

right here and we're going to go up here to split column in this ribbon up top

to split column in this ribbon up top we'll go to split

we'll go to split column and we want to do it by a

column and we want to do it by a delimiter and if you notice let me see

delimiter and if you notice let me see if I can move this over if you notice we

if I can move this over if you notice we have other and then we have this

have other and then we have this parenthesis and in no other option or

parenthesis and in no other option or way is there parenthesis so what we're

way is there parenthesis so what we're going to do is we're going to use a

going to do is we're going to use a custom and we're use this open

custom and we're use this open parenthesis what that's going to do is

parenthesis what that's going to do is it's going to separate it by this

it's going to separate it by this parenthesis it's going to leave the

parenthesis it's going to leave the other it's going to create separate

other it's going to create separate columns um just one separate column for

columns um just one separate column for each of these and we can do that at each

each of these and we can do that at each occurrence or we can do the leftmost and

occurrence or we can do the leftmost and we really we only need it for the

we really we only need it for the leftmost because there's only one of

leftmost because there's only one of these uh left-handed or left-sided uh

these uh left-handed or left-sided uh brackets or or what is it whatever this

brackets or or what is it whatever this is called and then let's go and click

is called and then let's go and click okay and it should create another column

okay and it should create another column so it's going to have 0.1 Point 2 and

so it's going to have 0.1 Point 2 and now we have if we click on this now we

now we have if we click on this now we only have these options we have analyst

only have these options we have analyst architect engineer data scientist

architect engineer data scientist database developer other and student

database developer other and student looking or none that is what we want it

looking or none that is what we want it makes it so much simpler and it's not

makes it so much simpler and it's not perfect but again I'm trying to show you

perfect but again I'm trying to show you what we are able to do in powerbi so now

what we are able to do in powerbi so now we're just going to remove that column

we're just going to remove that column and we're going to go and do the exact

and we're going to go and do the exact same thing to this one as well because I

same thing to this one as well because I know that we want to use this and I

know that we want to use this and I really wanted to use this one as well

really wanted to use this one as well but if we look at this one also um

but if we look at this one also um there's a lot so I said what is your

there's a lot so I said what is your favorite programming language and people

favorite programming language and people there were pre-selected answers like

there were pre-selected answers like JavaScript Java C++ python R things like

JavaScript Java C++ python R things like that and then there was an other option

that and then there was an other option and in this other option I mean it was

and in this other option I mean it was free text so they can fill it in as they

free text so they can fill it in as they want I mean there's four five six

want I mean there's four five six different ways that people put SQL that

different ways that people put SQL that is something I would standardize and you

is something I would standardize and you know that would be the way I cleaned it

know that would be the way I cleaned it but that's not how we did it in here so

but that's not how we did it in here so we're going to do the same thing we're

we're going to do the same thing we're going to keep that other so we're going

going to keep that other so we're going to split this column again we're use a

to split this column again we're use a delimiter and for this delimiter though

delimiter and for this delimiter though we're going to use a colon so we're

we're going to use a colon so we're going to say we're going to do a colon

going to say we're going to do a colon right there we'll just do the leftmost

right there we'll just do the leftmost we'll click okay and then we have our

we'll click okay and then we have our options and it's much simpler now I

options and it's much simpler now I really would have rather kept all these

really would have rather kept all these and because sql's in there quite a bit

and because sql's in there quite a bit but you know a lot of people don't think

but you know a lot of people don't think SQL is even a programming language so uh

SQL is even a programming language so uh we're going to delete that column now

we're going to delete that column now one that I just skipped and I kind of

one that I just skipped and I kind of wanted to go back to is this current

wanted to go back to is this current yearly salary I really want to use this

yearly salary I really want to use this let's see if we can use it I here's what

let's see if we can use it I here's what I want to do with it and this is not

I want to do with it and this is not perfect um for this video I want to try

perfect um for this video I want to try it what I want to do is break up these

it what I want to do is break up these numbers 106 125 and then take the

numbers 106 125 and then take the average of those numbers so then we'll

average of those numbers so then we'll use some docks in there so we'll take

use some docks in there so we'll take 106 125 create that into two separate

106 125 create that into two separate columns then we'll create a third column

columns then we'll create a third column that will give us the average of those

that will give us the average of those two numbers so we'll do 106 plus 125

two numbers so we'll do 106 plus 125 divided by two and then we'll have the

divided by two and then we'll have the average of that now that is not perfect

average of that now that is not perfect but it's going to give us at least you

but it's going to give us at least you know an average of kind of roundabout

know an average of kind of roundabout number because they gave us this range

number because they gave us this range they said my salary is between 106 and

they said my salary is between 106 and 125,000 so if we say that their salary

125,000 so if we say that their salary was

was 112,000 at least gives us it makes it

112,000 at least gives us it makes it usable it's a numeric value instead of

usable it's a numeric value instead of being this which is text which we really

being this which is text which we really we could use and and I'll show you how

we could use and and I'll show you how to do that because we're going to keep

to do that because we're going to keep this column I'll create a copy of this

this column I'll create a copy of this and I'll show you the difference between

and I'll show you the difference between this and using the average but for but

this and using the average but for but for this data cleaning portion let's

for this data cleaning portion let's just try it let's see what we can do and

just try it let's see what we can do and see if we can make it work so first

see if we can make it work so first let's create a duplicate so we're going

let's create a duplicate so we're going to uh duplicate the column so now we

to uh duplicate the column so now we have this copy at the very very end and

have this copy at the very very end and we can use this one instead of having to

we can use this one instead of having to use the original way way way back here

use the original way way way back here so we're going to leave that one how it

so we're going to leave that one how it is and we're going to use this one so

is and we're going to use this one so let's go ahead and split this one up

let's go ahead and split this one up we're going to click on the column

we're going to click on the column header then we're going to click on

header then we're going to click on split column and we'll do it by digit to

split column and we'll do it by digit to non-digit and if you look at it right

non-digit and if you look at it right here it's broken it out kind of um in

here it's broken it out kind of um in the fact that now in this one we just

the fact that now in this one we just have numeric values and in this one we

have numeric values and in this one we have k- numeric or just Dash numeric and

have k- numeric or just Dash numeric and now this can be easily cleaned whereas

now this can be easily cleaned whereas this one we can just completely get rid

this one we can just completely get rid of because it's only K so we'll just

of because it's only K so we'll just remove that column and then in this one

remove that column and then in this one we're going to rightclick we're going to

we're going to rightclick we're going to click on replace values and so if it

click on replace values and so if it just has we're just do a k we'll replace

just has we're just do a k we'll replace with nothing we'll do okay and then for

with nothing we'll do okay and then for the last one we'll go to replace values

the last one we'll go to replace values and we'll do the dash or the minus sign

and we'll do the dash or the minus sign and we'll place that with nothing and so

and we'll place that with nothing and so now we have our values as well oh we

now we have our values as well oh we also have a plus let me get rid of that

also have a plus let me get rid of that because that's when some people had 250

because that's when some people had 250 or 225,000 plus so for that one the

or 225,000 plus so for that one the average is just going to be 225 we'll

average is just going to be 225 we'll have to specify that in our dock I

have to specify that in our dock I forgot but actually if somebody has

forgot but actually if somebody has 225 let me find this plus really quick

225 let me find this plus really quick uh let me filter by it because that's a

uh let me filter by it because that's a lot faster what we actually want to do

lot faster what we actually want to do for the purpose of this one is we want

for the purpose of this one is we want to put 225 here so that when we do 225

to put 225 here so that when we do 225 plus 225 divide by two it comes out to

plus 225 divide by two it comes out to 225 that's just what we're going to put

225 that's just what we're going to put it as and there's only two people so uh

it as and there's only two people so uh I'm actually going to replace this I'm

I'm actually going to replace this I'm going to do replace values I'm G to say

going to do replace values I'm G to say Plus

Plus with

with 225 and we'll click okay awesome we can

225 and we'll click okay awesome we can unfilter these select all so we're going

unfilter these select all so we're going to go right up here to add column we're

to go right up here to add column we're going to say custom

going to say custom column and we're going to go right over

column and we're going to go right over here actually let's make it uh

here actually let's make it uh average salary let's make it average

average salary let's make it average salary so we're going to insert this I'm

salary so we're going to insert this I'm going to

going to say parentheses and we're going to say

say parentheses and we're going to say plus this

plus this insert and close the parenthesis divided

insert and close the parenthesis divided by two and it says no syntax errors have

by two and it says no syntax errors have been detected let's click on okay and

been detected let's click on okay and it's giving us an error so it's saying

it's giving us an error so it's saying we cannot apply operator plus to types

we cannot apply operator plus to types text and text which makes perfect sense

text and text which makes perfect sense these aren't uh numbers so let's make it

these aren't uh numbers so let's make it a whole number and let's make it a whole

a whole number and let's make it a whole number and then let's see if this will

number and then let's see if this will actually work

actually work no or maybe we just need to try a whole

no or maybe we just need to try a whole another one so let's try transform or

another one so let's try transform or add column custom

add column custom column let's try this all again see if

column let's try this all again see if uh I can make it

uh I can make it work

work insert do this

insert do this one

one plus this

plus this one and we'll do divid by two and let's

one and we'll do divid by two and let's try this one and there we go so now

try this one and there we go so now let's get rid of this column

let's get rid of this column columns and we can actually remove these

columns and we can actually remove these ones as

ones as well because now we have this

well because now we have this um average salary

um average salary column which when we look at this or

column which when we look at this or when we use this uh we can let me see if

when we use this uh we can let me see if I can just move this way way way over

I can just move this way way way over all right I might cut because this is

all right I might cut because this is taking forever so if you take the

taking forever so if you take the average of these two numbers you'll get

average of these two numbers you'll get 53 if you take the average of 0 and 40

53 if you take the average of 0 and 40 you'll get 20 so now we have this

you'll get 20 so now we have this average salary and again when we get to

average salary and again when we get to the actual visualization part I'll show

the actual visualization part I'll show you why this isn't as useful as having

you why this isn't as useful as having this average salary and just a reminder

this average salary and just a reminder this is not perfect uh I wouldn't

this is not perfect uh I wouldn't typically do this especially if I had it

typically do this especially if I had it in Excel or if I was you know creating

in Excel or if I was you know creating this survey in a different way I would

this survey in a different way I would probably have a very specific value

probably have a very specific value where they could do it on a slider but

where they could do it on a slider but this is how it is so we've at least made

this is how it is so we've at least made it usable or more usable in my mind and

it usable or more usable in my mind and we have a few other things that we can

we have a few other things that we can change like what industry do you work in

change like what industry do you work in where we can break this one out so I'm

where we can break this one out so I'm going to go ahead and break this one out

going to go ahead and break this one out as well

as well as this one right here which country do

as this one right here which country do you live in I'm going to break bro both

you live in I'm going to break bro both of those out to where it's the country

of those out to where it's the country or other I'm not going to have these

or other I'm not going to have these other values although there are a lot of

other values although there are a lot of them because there's a lot of people who

them because there's a lot of people who live in these different countries but we

live in these different countries but we can't really do that super well in here

can't really do that super well in here because again the same issue kept

because again the same issue kept happening Argentina Argentina Argentine

happening Argentina Argentina Argentine a Australia so we can't normalize those

a Australia so we can't normalize those values unless we spend just a copious

values unless we spend just a copious amount of time doing that so I'm going

amount of time doing that so I'm going to go ahead and do these I'm going to

to go ahead and do these I'm going to fast I'm going to fast speed this so it

fast I'm going to fast speed this so it goes a lot faster so I'm just going to

goes a lot faster so I'm just going to go silent and let this happen really

go silent and let this happen really quick and then we'll get to the end and

quick and then we'll get to the end and we'll actually start building our

visualizations all right so we've split them up and as you can see we have all

them up and as you can see we have all the these options as well as other and I

the these options as well as other and I think you know there is let me tell you

think you know there is let me tell you there is so much more that we could do

there is so much more that we could do with this I mean just so many other

with this I mean just so many other things but this is like what the bare

things but this is like what the bare minimum of what we need for this project

minimum of what we need for this project so let's go ahead and close and apply

so let's go ahead and close and apply this and if we need to come back at any

this and if we need to come back at any point and actually fix anything or

point and actually fix anything or change anything we can so it's not like

change anything we can so it's not like that's permanent um so as you can see we

that's permanent um so as you can see we have everything over here we have all

have everything over here we have all our data as it is transformed in here as

our data as it is transformed in here as well and now we can start building out

well and now we can start building out our visualization let's go back to our

our visualization let's go back to our report and let's start building

report and let's start building something out all right so let's add a

something out all right so let's add a title to our

title to our dashboard we want to make this right at

dashboard we want to make this right at the

the top we call this the

top we call this the data

data professional

professional survey

survey breakdown and let's make make that quite

breakdown and let's make make that quite a bit

a bit larger make it bold why not and we'll

larger make it bold why not and we'll put that in the

put that in the center and now let's um let's add some

center and now let's um let's add some effects let's change that background to

effects let's change that background to something like it's too dark something

something like it's too dark something like this and I do not like that Boldt

like this and I do not like that Boldt let's take that

let's take that off there we go so something like this

off there we go so something like this just as a quick title to what we're

just as a quick title to what we're about to do what we are about to build

about to do what we are about to build so we're going to start off with the

so we're going to start off with the most simple visualizations that we're

most simple visualizations that we're going to do and we'll kind of work our

going to do and we'll kind of work our way towards kind of the harder ones so

way towards kind of the harder ones so the first one that we're going to start

the first one that we're going to start off with is a card and the cards are

off with is a card and the cards are obviously like just super super easy

obviously like just super super easy they usually just display one piece of

they usually just display one piece of information so we're going to go right

information so we're going to go right over here to the very bottom at the

over here to the very bottom at the unique ID and we're going to select it

unique ID and we're going to select it and we're going to say a account of

and we're going to say a account of distinct or account it doesn't matter um

distinct or account it doesn't matter um it says 630 count of unique ID now we're

it says 630 count of unique ID now we're not going to keep that as is we're

not going to keep that as is we're actually going to go right over here

actually going to go right over here we're going to say rename for this

we're going to say rename for this Visual and it says count of unique ID

Visual and it says count of unique ID but we're going to say count

but we're going to say count of survey takers and you can say

of survey takers and you can say whatever you want here but in in general

whatever you want here but in in general that is what it is we're we're counting

that is what it is we're we're counting how many people um you know took this

how many people um you know took this survey and that's just a kind of a total

survey and that's just a kind of a total maybe I should say total amount or of

maybe I should say total amount or of survey takers but you can say count of

survey takers but you can say count of survey takers how many people took this

survey takers how many people took this survey so let's click out of there let's

survey so let's click out of there let's click on card let's make it about the

click on card let's make it about the same size we're going to drag it up

same size we're going to drag it up here and try to make them about the same

here and try to make them about the same we will in a little bit we'll make them

we will in a little bit we'll make them the same size um but for this one we're

the same size um but for this one we're going to look at age so we're going to

going to look at age so we're going to look at current age so I'm going click

look at current age so I'm going click on that and we'll say want the average

on that and we'll say want the average age so our average age taker is almost

age so our average age taker is almost 30 years old so let's go right over here

30 years old so let's go right over here we're going to say rename for this

we're going to say rename for this visual we'll say a average age of

visual we'll say a average age of survey oop this might be too

survey oop this might be too long average age of survey taker again

long average age of survey taker again name it whatever you'd like so again

name it whatever you'd like so again these are meant to be highlevel numbers

these are meant to be highlevel numbers so when somebody's looking at your

so when somebody's looking at your dashboard they can just really quickly

dashboard they can just really quickly glance at this and know exactly what it

glance at this and know exactly what it is instead of like some of these other

is instead of like some of these other visualizations that we're about to

visualizations that we're about to create they don't really have to dig

create they don't really have to dig into it look at the x- axis the y axis

into it look at the x- axis the y axis the the different uh Legend colors and

the the different uh Legend colors and whatnot they can just see these high

whatnot they can just see these high numbers and get a really quick glance of

numbers and get a really quick glance of the data now let's create our first

the data now let's create our first visualization and what we're going to do

visualization and what we're going to do for that one is a clustered bar chart so

for that one is a clustered bar chart so let's go ahead and click on the

let's go ahead and click on the clustered bar chart we can create as

clustered bar chart we can create as small or as large as we'd like and for

small or as large as we'd like and for this one we're going to be looking at

this one we're going to be looking at the job titles now remember we kind of

the job titles now remember we kind of changed the job titles or you know U

changed the job titles or you know U transform those if you want to say that

transform those if you want to say that so we're going to look at Job titles and

so we're going to look at Job titles and then we're going to look at their

then we're going to look at their average salary and if you remember we

average salary and if you remember we transformed that one as well we have a

transformed that one as well we have a average salary now this one is it looks

average salary now this one is it looks like a text right now so it may not work

like a text right now so it may not work properly and what we're actually going

properly and what we're actually going to do is go over

to do is go over here I want to see the average

here I want to see the average salary so let's click on average salary

salary so let's click on average salary and see if we can change this data type

and see if we can change this data type from a text to a decimal number let's

from a text to a decimal number let's click yes I forgot to do that when we

click yes I forgot to do that when we were transforming it and there we go

were transforming it and there we go this is perfect um so now we can go

this is perfect um so now we can go back and we can select our average

back and we can select our average salary and as you can see it has this um

salary and as you can see it has this um this function symbol and so now we can

this function symbol and so now we can click on it and it'll look a lot better

click on it and it'll look a lot better and although this says average salary as

and although this says average salary as the title it's actually doing a count or

the title it's actually doing a count or the sum so we can click average right

the sum so we can click average right here and what we want to do is actually

here and what we want to do is actually break this down by the job title and so

break this down by the job title and so now we can see data scientists are

now we can see data scientists are making the most by far far they're

making the most by far far they're making average of 93,000 at least from

making average of 93,000 at least from the survey takers that took it then we

the survey takers that took it then we have our data Engineers making

have our data Engineers making 65,000 data Architects are making 63 and

65,000 data Architects are making 63 and then where the data analysts data

then where the data analysts data analysts are right here making 55 so

analysts are right here making 55 so again we had 630 people take this survey

again we had 630 people take this survey and so the vast majority of them were

and so the vast majority of them were data analysts so this one's probably the

data analysts so this one's probably the most accurate out of all of them and I

most accurate out of all of them and I actually don't like how this looks as

actually don't like how this looks as the cluster bar chart let's try the

the cluster bar chart let's try the stocked bar chart and put this as the

stocked bar chart and put this as the legend that's more what I was going for

legend that's more what I was going for I don't know I didn't want as skinny

I don't know I didn't want as skinny because when you're doing this one it

because when you're doing this one it typically they have multiple options per

typically they have multiple options per um uh x axis and so I think that's why

um uh x axis and so I think that's why it was that little skinny line but this

it was that little skinny line but this one is more what I was looking for but

one is more what I was looking for but let's make that smaller and let's

let's make that smaller and let's definitely change that title because

definitely change that title because good night um this is like incredibly

good night um this is like incredibly long let's go over here to this format

long let's go over here to this format visual ual we'll go to the general the

visual ual we'll go to the general the title and we're just going to say

title and we're just going to say average salary by job title just like

average salary by job title just like that and this looks a lot better now

that and this looks a lot better now we're not going to kind of format all

we're not going to kind of format all our whole dashboard yet we're going to

our whole dashboard yet we're going to create our visualizations and then we're

create our visualizations and then we're going to kind of organize everything and

going to kind of organize everything and kind of play Tetris with it to make it

kind of play Tetris with it to make it look the best so we're just going to

look the best so we're just going to minimize this and put it right up here

minimize this and put it right up here for now um but we will go back and kind

for now um but we will go back and kind of make everything look better at the

of make everything look better at the end and actually while we're here I also

end and actually while we're here I also want to change this as well so rename

want to change this as well so rename for this we're going to say job title

for this we're going to say job title Oops why did I do that

Oops why did I do that job title and for this one we're just

job title and for this one we're just going to

going to say name average

say name average salary there we go looks much better

salary there we go looks much better much cleaner uh took away a lot of the

much cleaner uh took away a lot of the anxiety that I was feeling about two

anxiety that I was feeling about two minutes ago when we first put that up

minutes ago when we first put that up there so let's go on to our second

there so let's go on to our second visualization the next one that I'm

visualization the next one that I'm interested in is actually what

interested in is actually what programming language people were using

programming language people were using the most so we have salary there's a

the most so we have salary there's a thousand different things we can look at

thousand different things we can look at in here but I want to know you know what

in here but I want to know you know what is people's favorite programming

is people's favorite programming language so let's take a look at that so

language so let's take a look at that so we have favorite programming language

we have favorite programming language let's find that so we have our favorite

let's find that so we have our favorite programming language and we also have

programming language and we also have how many people actually took it or the

how many people actually took it or the unique people so right now this is

unique people so right now this is columns we don't want that let's um

columns we don't want that let's um let's do a clustered column chart click

let's do a clustered column chart click on this right here and it looks

on this right here and it looks like here we go that is kind of what

like here we go that is kind of what we're looking for and instead of count

we're looking for and instead of count of unique ID we'll say count

of unique ID we'll say count of let's do count of

of let's do count of Voters and for favorite program language

Voters and for favorite program language we'll

we'll say favorite oops favorite programming

say favorite oops favorite programming language and get rid of that as well and

language and get rid of that as well and then we're going to go into here also

then we're going to go into here also and change the title and say favorite

and change the title and say favorite programming

programming languages or favorite pro programming

languages or favorite pro programming language just like this now let's make

language just like this now let's make this a lot bigger so you can see it but

this a lot bigger so you can see it but really quickly at a glance you can see

really quickly at a glance you can see python is by far the most popular are

python is by far the most popular are other C++ JavaScript Java now all we're

other C++ JavaScript Java now all we're seeing is the count so it's all the same

seeing is the count so it's all the same it's just blue we can see how many

it's just blue we can see how many people voted for each one but if we

people voted for each one but if we wanted to break it out similar to how we

wanted to break it out similar to how we did with the job titles we could still

did with the job titles we could still do that so all we'd have to do is break

do that so all we'd have to do is break it out uh or bring this job title down

it out uh or bring this job title down to the legend and now breaks out like

to the legend and now breaks out like this and that's not exactly what I was

this and that's not exactly what I was going for I was going more for something

going for I was going more for something like this where we can see the still the

like this where we can see the still the whole count but now we can see who is

whole count but now we can see who is actually V voting for these things so

actually V voting for these things so I'm just not a huge fan of the colors

I'm just not a huge fan of the colors that are pre-selected here and kind of

that are pre-selected here and kind of the whole theme of this dashboard at the

the whole theme of this dashboard at the very end we're going to completely

very end we're going to completely revamp this change a bunch of colors the

revamp this change a bunch of colors the background and make this look a lot

background and make this look a lot nicer rather than just the white

nicer rather than just the white background like we have it um and so for

background like we have it um and so for now let's

now let's just make this a lot smaller and put it

just make this a lot smaller and put it into this corner these will not be

into this corner these will not be staying there but we need to we need

staying there but we need to we need room to create our next visualizations

room to create our next visualizations and just just a cleaner space to do

and just just a cleaner space to do things now the next thing that I really

things now the next thing that I really want to include is a way to break down

want to include is a way to break down where they're from their country because

where they're from their country because especially something like salary is very

especially something like salary is very dependent on your country whereas the

dependent on your country whereas the average salary in the United States for

average salary in the United States for a data analyst may be like 60,000 in

a data analyst may be like 60,000 in another country it could be 20,000 that

another country it could be 20,000 that could bring down the average quite a bit

could bring down the average quite a bit so we need a way to be able to break

so we need a way to be able to break that down now we can do something like a

that down now we can do something like a filled map and there's no problem with

filled map and there's no problem with that at all um

that at all um but you know for what we're building

but you know for what we're building what we're creating it's not probably

what we're creating it's not probably going to work out the best I mean this

going to work out the best I mean this looks okay we could stick it in the

looks okay we could stick it in the corner or something um and you can do

corner or something um and you can do that and that's perfectly fine I think

that and that's perfectly fine I think what I'm going to do is something like a

what I'm going to do is something like a tree map which I don't use a lot but I

tree map which I don't use a lot but I want something where they can just click

want something where they can just click on it they can look at the

on it they can look at the values

values distinct they can look at the values and

distinct they can look at the values and just click on it and it'll be right

just click on it and it'll be right there for them so they don't have to

there for them so they don't have to filter it out on their own or no

filter it out on their own or no geography and look at this map they can

geography and look at this map they can just read Canada other United Kingdom

just read Canada other United Kingdom India United States and click on that

India United States and click on that and so for example let's click over here

and so for example let's click over here on United States the numbers change

on United States the numbers change quite a bit now the average salary for a

quite a bit now the average salary for a data scientist is

data scientist is 139,000 for data analyst it's 80 and if

139,000 for data analyst it's 80 and if we look at India you know the average

we look at India you know the average salary for a data scientist is 68 the

salary for a data scientist is 68 the average salary is 26 for a data analyst

average salary is 26 for a data analyst that doesn't mean that they make less

that doesn't mean that they make less money in India that just means that the

money in India that just means that the cost of living is probably lower in

cost of living is probably lower in India therefore they don't need the

India therefore they don't need the higher US Dollars salary because again

higher US Dollars salary because again this was all done in US dollars so just

this was all done in US dollars so just something to think about uh let's click

something to think about uh let's click out of that so we'll keep that one as

out of that so we'll keep that one as well so now let's create our next

well so now let's create our next visualization and this is one that I do

visualization and this is one that I do not get to use enough in my actual job

not get to use enough in my actual job so we're going to use it in this project

so we're going to use it in this project um and it's going to be this gauge right

um and it's going to be this gauge right here so let's add that one put it right

here so let's add that one put it right over here we're going to add two of

over here we're going to add two of those let's just go ahead and add

those let's just go ahead and add another one while we're at it because

another one while we're at it because we're going to have them kind of like

we're going to have them kind of like right here right next to each other the

right here right next to each other the first one and these ones are really good

first one and these ones are really good for kind of looking at these kind of

for kind of looking at these kind of surveys and I don't get to work with

surveys and I don't get to work with surveys enough but we can see you know

surveys enough but we can see you know how happy are they in terms of work life

how happy are they in terms of work life balance so we can add that we're going

balance so we can add that we're going to add work life balance um and right

to add work life balance um and right now it's doing a count and we don't have

now it's doing a count and we don't have minimum or maximum values in there yet

minimum or maximum values in there yet so it's going to look kind of weird but

so it's going to look kind of weird but we're going to look at the average rate

we're going to look at the average rate or the the average score of these then

or the the average score of these then we're going to pull this over to the

we're going to pull this over to the minimum value and we want to put that at

minimum value and we want to put that at the minimum and pull this over and add

the minimum and pull this over and add the maximum value so now it actually has

the maximum value so now it actually has zero to 10 and it shows that the average

zero to 10 and it shows that the average person is happy with which one was this

person is happy with which one was this their average person is happy with their

their average person is happy with their work life balance uh they rate about a

work life balance uh they rate about a 5.74 overall now let's really quickly

5.74 overall now let's really quickly change the title of this because this is

change the title of this because this is ridiculous I want to say happy with work

ridiculous I want to say happy with work life balance

life balance so this is their rating uh you know

so this is their rating uh you know change it to whatever title you want

change it to whatever title you want that's what I'm going to do and we'll

that's what I'm going to do and we'll also do happy with their salary let's

also do happy with their salary let's click on salary We'll add that to

click on salary We'll add that to minimum and we'll add the maximum value

minimum and we'll add the maximum value as well to make sure that we know how to

as well to make sure that we know how to use

use that and then we'll take the average so

that and then we'll take the average so not many people are happy with their

not many people are happy with their salary I'm just finding out I mean this

salary I'm just finding out I mean this is a real survey this is real data so I

is a real survey this is real data so I mean it's h pretty interesting let's go

mean it's h pretty interesting let's go to the title let's go to happy with or

to the title let's go to happy with or maybe it's happiness happiness with

maybe it's happiness happiness with salary maybe that's what we should make

salary maybe that's what we should make it and I'm going to change that over

it and I'm going to change that over here as well I think it sounds better

here as well I think it sounds better some of this I've already planned out

some of this I've already planned out some I haven't this is not something

some I haven't this is not something I've planned out so uh so we're going to

I've planned out so uh so we're going to say happiness with work life balance

say happiness with work life balance happiness with salary really interesting

happiness with salary really interesting um we may go back and tweak these just a

um we may go back and tweak these just a little bit in the future but the very

little bit in the future but the very last visualization that we're going to

last visualization that we're going to do is male versus female kind to got to

do is male versus female kind to got to have that in there um I don't typically

have that in there um I don't typically like pie charts and dut charts but uh

like pie charts and dut charts but uh you know I'm feeling I'm just feeling it

you know I'm feeling I'm just feeling it so let's try it um and we will

so let's try it um and we will do let see let's make this larger so we

do let see let's make this larger so we have male

have male female and what do we want to look at

female and what do we want to look at like what do we want to measure so we

like what do we want to measure so we have male versus female we can measure

have male versus female we can measure anything um but maybe what we'll do is

anything um but maybe what we'll do is the average salary again I mean we've

the average salary again I mean we've kind of only looked at salary once

kind of only looked at salary once in this one right here um and a little

in this one right here um and a little bit of like how happy they are but we'll

bit of like how happy they are but we'll look at the average salary between males

look at the average salary between males and females and then we'll look at not

and females and then we'll look at not the current age Oops I meant average

the current age Oops I meant average salary and then we'll look at the

salary and then we'll look at the average and it looks like the average

average and it looks like the average salary is actually really close versus

salary is actually really close versus males versus females 55 for female

males versus females 55 for female versus 53 for male so actually the

versus 53 for male so actually the females are a little bit higher

females are a little bit higher congratulations so they're just a little

congratulations so they're just a little bit higher in terms of pay so now we

bit higher in terms of pay so now we need to start organizing all of this

need to start organizing all of this cleaning it up making it look a lot

cleaning it up making it look a lot better than it does right now it looks

better than it does right now it looks great uh you know but we can do a lot

great uh you know but we can do a lot more with this so I'm gonna we're we're

more with this so I'm gonna we're we're going to keep these or all these kind of

going to keep these or all these kind of over on this left hand side I'm GNA put

over on this left hand side I'm GNA put this I want this up here we also need to

this I want this up here we also need to change that title I want this up here um

change that title I want this up here um and again we're going to kind of change

and again we're going to kind of change the theme as we go

the theme as we go I I just want to format it

I I just want to format it right we'll have it just like this let's

right we'll have it just like this let's change the title of

change the title of this let's go to title and we're going

this let's go to title and we're going to say country of survey

to say country of survey takers uh I'm not the the survey takers

takers uh I'm not the the survey takers I'm not really stuck on that if you find

I'm not really stuck on that if you find something better you think of something

something better you think of something better I would go with that but um you

better I would go with that but um you know it definitely doesn't look bad and

know it definitely doesn't look bad and where did this where did my other

where did this where did my other visualization go there goes um I think

visualization go there goes um I think this one I want to make kind of more

this one I want to make kind of more tall um so I might move it this way jeez

tall um so I might move it this way jeez this is such a I hate I hate having a

this is such a I hate I hate having a lot of visualizations on here it just

lot of visualizations on here it just really is annoying to me so what we're

really is annoying to me so what we're going to do I think we're

going to do I think we're gonna step this to the side put this to

gonna step this to the side put this to the side as

the side as well I want to make it to where it's

well I want to make it to where it's just okay I didn't want it to cut

just okay I didn't want it to cut off we'll do that might make these

off we'll do that might make these um make these a little bigger actually

um make these a little bigger actually so I want it to kind of match the

so I want it to kind of match the size like right there I'll match this

size like right there I'll match this perfect this one I kind of want to bring

perfect this one I kind of want to bring over

over here and bring it down a little bit

here and bring it down a little bit maybe something like

maybe something like this maybe I'm not sure I'm not I'm not

this maybe I'm not sure I'm not I'm not sold on that um I added a few different

sold on that um I added a few different visualizations that I didn't have in my

visualizations that I didn't have in my original so now I'm kind of having to do

original so now I'm kind of having to do this on the fly so I might fast forward

this on the fly so I might fast forward some of the parts where I'm like really

some of the parts where I'm like really thinking about it or taking too much

thinking about it or taking too much time on it but I'm going to bring this

time on it but I'm going to bring this down a little bit actually because I

down a little bit actually because I don't like how close that is to um the

don't like how close that is to um the the text above it but one thing we do

the text above it but one thing we do need to

do I'm going to put this up kind of like this I think that looks fine I think I'm

this I think that looks fine I think I'm going to put this at the very bottom so

going to put this at the very bottom so let's make some room for

let's make some room for it all right just like that stretch it

it all right just like that stretch it to the side and we'll lower

to the side and we'll lower it and I think we'll keep that as

it and I think we'll keep that as is kind of like this um okay there's a

is kind of like this um okay there's a lot going on in here and there are some

lot going on in here and there are some things I'm just noticing as we're

things I'm just noticing as we're walking through this that I kind of

walking through this that I kind of missed um like I need to change some

missed um like I need to change some titles and stuff like that so let me go

titles and stuff like that so let me go ahead and change some of those things so

ahead and change some of those things so we're going to do

we're going to do title do average

title do average salary by gender or by

salary by gender or by sex do like that average salary by sex I

sex do like that average salary by sex I also don't like that it's in the middle

also don't like that it's in the middle um I don't like that it's on the outside

um I don't like that it's on the outside I want them on the inside for this so

I want them on the inside for this so let's go to the details let's go to

let's go to the details let's go to inside and see if that looks any better

inside and see if that looks any better oh that looks terrible um let me see if

oh that looks terrible um let me see if I can change that maybe I don't no I

I can change that maybe I don't no I definitely want it

definitely want it um I guess we'll do outside I you can't

um I guess we'll do outside I you can't even see the information oh the decimal

even see the information oh the decimal is crazy long um let me go and see if I

is crazy long um let me go and see if I can change that decimal to just like a

can change that decimal to just like a whole number or like

whole number or like 1.1 uh because that's a problem so maybe

1.1 uh because that's a problem so maybe I need to go over here to the

I need to go over here to the value all right so I think I want to

value all right so I think I want to change this one it's just not working

change this one it's just not working out exactly how I wanted and you guys

out exactly how I wanted and you guys know if I make mistakes I'm going to

know if I make mistakes I'm going to keep it in here so you guys can see it I

keep it in here so you guys can see it I I hoped that this was going to turn out

I hoped that this was going to turn out better but it didn't um one that I do

better but it didn't um one that I do want to add because this is kind of a a

want to add because this is kind of a a breakdown and a nice visualization I

breakdown and a nice visualization I want to add this difficulty piece so I

want to add this difficulty piece so I want to add this how difficult was it

want to add this how difficult was it for you to break into data science let's

for you to break into data science let's get rid of these and I want to click on

get rid of these and I want to click on this really quickly see what it gives us

this really quickly see what it gives us um values okay so now this shows us

um values okay so now this shows us percentages um of how easy it was again

percentages um of how easy it was again it's neither easy nor difficult

it's neither easy nor difficult difficult easy very difficult very easy

difficult easy very difficult very easy these numbers make absolutely no sense

these numbers make absolutely no sense we need to kind of order them a little

we need to kind of order them a little better so I'm going to come over here to

better so I'm going to come over here to slices we have our colors over here we

slices we have our colors over here we want very difficult to be like the most

want very difficult to be like the most difficult um so we're going to make that

difficult um so we're going to make that red and then we want difficult to be

red and then we want difficult to be maybe like an

maybe like an orange let see if we can find an orange

orange let see if we can find an orange there we have an orange this does not

there we have an orange this does not look red enough there we go oh

look red enough there we go oh no no no very difficult is red difficult

no no no very difficult is red difficult is orange we have neither easy nor

is orange we have neither easy nor difficult and that's kind of a neutral

difficult and that's kind of a neutral um let's see if we have something

um let's see if we have something neutral in

here kind of like this yellow I don't know let's try it out then we have easy

know let's try it out then we have easy and very easy and these will be like our

and very easy and these will be like our Blues so I'm going to keep that um I'm

Blues so I'm going to keep that um I'm going to keep that kind of like a dark

going to keep that kind of like a dark blueish and then our blue for super easy

blueish and then our blue for super easy is just going to be like really blue U

is just going to be like really blue U and that doesn't look bad the I mean

and that doesn't look bad the I mean look I'm I'm not a color person I I'm

look I'm I'm not a color person I I'm not great with colors and we're going to

not great with colors and we're going to kind of organize this in just a little

kind of organize this in just a little bit but this looks better to me um but

bit but this looks better to me um but we need to change up some stuff as well

we need to change up some stuff as well like the title need to

like the title need to do difficulty to break into

do difficulty to break into Data there we go

Data there we go and we're also going to

and we're also going to change this title right here we're just

change this title right here we're just say

say difficulty difficulty difficulty this

difficulty difficulty difficulty this looks better to me um again not perfect

looks better to me um again not perfect and there's a thousand different things

and there's a thousand different things you could have done but that's just what

you could have done but that's just what we're going to do I need to go through

we're going to do I need to go through here and see what I need to change so

here and see what I need to change so right off the bat I can see I need to

right off the bat I can see I need to change this

change this um to let's see right here I'm going to

um to let's see right here I'm going to rename this job title just like we did

rename this job title just like we did in this one right here uh count of

in this one right here uh count of Voters that's fine progr language

Voters that's fine progr language breaking into difficulty happiness

breaking into difficulty happiness happiness average count okay okay so

happiness average count okay okay so what we have here is very close to a

what we have here is very close to a finished product now it's not 100%

finished product now it's not 100% complete I mean I I do want to make it

complete I mean I I do want to make it look a little nicer rather than just the

look a little nicer rather than just the typical white so what we're gonna do

typical white so what we're gonna do we're GNA go up here we'll go to uh what

we're GNA go up here we'll go to uh what is it View and we have all these

is it View and we have all these different filters and we're just going

different filters and we're just going to play around with it see if we can

to play around with it see if we can find something that we like um this

find something that we like um this doesn't look too bad it's not really my

doesn't look too bad it's not really my style we can do this one Frontier this

style we can do this one Frontier this is pretty neat I kind of am digging this

is pretty neat I kind of am digging this we might come back to it I like the

we might come back to it I like the natural tones I don't know why I said

natural tones I don't know why I said tones like that but I did um this one's

tones like that but I did um this one's not bad but I don't I don't it's not

not bad but I don't I don't it's not that's not my I don't like how dark that

that's not my I don't like how dark that is um and so maybe it's like you know we

is um and so maybe it's like you know we change like the background color of all

change like the background color of all of these as well as match it with um

of these as well as match it with um match it with something else whatever

match it with something else whatever you want genuinely you customize this

you want genuinely you customize this however you want I kind of like this one

however you want I kind of like this one it's kind of groovy man and um it's not

it's kind of groovy man and um it's not perfect by any means but what we can do

perfect by any means but what we can do and we can customize this current theme

and we can customize this current theme we can come in here customize this theme

we can come in here customize this theme however we'd like I personally don't

however we'd like I personally don't want color five which is the data

want color five which is the data analyst color I don't like it to I don't

analyst color I don't like it to I don't want to go go and change it because I

want to go go and change it because I don't like it but I don't really like

don't like it but I don't really like that color per se you know I might want

that color per se you know I might want to choose a different color um but it

to choose a different color um but it has to be like this muted like that it

has to be like this muted like that it has a style to it so you can come in

has a style to it so you can come in here and you can customize this and make

here and you can customize this and make it however you'd like and and really

it however you'd like and and really mess around with it play play around

mess around with it play play around with it for me uh I'm just going to keep

with it for me uh I'm just going to keep it how it is because I don't really want

it how it is because I don't really want to mess with it and break it or anything

to mess with it and break it or anything like that so U let me just put that up

like that so U let me just put that up just a tiny bit so this is it this is

just a tiny bit so this is it this is the project I hope that it was helpful

the project I hope that it was helpful um I am not joking when I say that I'm

um I am not joking when I say that I'm because I'm gonna do a different project

because I'm gonna do a different project I'm gonna go really in depth in another

I'm gonna go really in depth in another project it's probably gonna be like a

project it's probably gonna be like a two-hour project it's going to be crazy

two-hour project it's going to be crazy long um well for a YouTube video but I

long um well for a YouTube video but I can see doing thousand different things

can see doing thousand different things with this data creating a really great

with this data creating a really great dashboard really cleaning the data which

dashboard really cleaning the data which is a large part of of actually doing

is a large part of of actually doing this and we didn't do much data cleaning

this and we didn't do much data cleaning at all there's just so much you can do

at all there's just so much you can do with this and so really dig into this

with this and so really dig into this see what you like see what you don't

see what you like see what you don't like see what you want to clean what you

like see what you want to clean what you don't want to clean you could put it in

don't want to clean you could put it in SQL you could put it in um Excel and

SQL you could put it in um Excel and just and just standardize the data to

just and just standardize the data to make it a lot more usable do whatever

make it a lot more usable do whatever you want with it I mean I I took this

you want with it I mean I I took this survey for you guys that we could use it

survey for you guys that we could use it so go out and use it and make the best

so go out and use it and make the best dashboard that you can possibly do so I

dashboard that you can possibly do so I hope that this was helpful I hope that

hope that this was helpful I hope that you enjoyed this thank you so much for

you enjoyed this thank you so much for watching this video If you like this

watching this video If you like this thank you so much for watching if you

thank you so much for watching if you like this video be sure to like And

like this video be sure to like And subscribe below and I'll see you in the

subscribe below and I'll see you in the next

next [Music]

[Music] video

video what's going on everybody welcome back

what's going on everybody welcome back to another video today we're going to be

to another video today we're going to be starting our Python tutorial

[Music] series now I am extremely excited for

series now I am extremely excited for this series we're going to be walking

this series we're going to be walking through all the things that you need to

through all the things that you need to know to get started in Python we'll be

know to get started in Python we'll be looking at variables data types for

looking at variables data types for Loops y Loops operators and a ton more

Loops y Loops operators and a ton more after this beginner series we're going

after this beginner series we're going to be going into another set of Series

to be going into another set of Series where we look at pandas mat plat lib

where we look at pandas mat plat lib Seaborn web scraping and more now in

Seaborn web scraping and more now in this video we're just going to be

this video we're just going to be setting up our environment to where we

setting up our environment to where we can learn python in future videos in

can learn python in future videos in this series we're going to be using

this series we're going to be using jupyter notebooks for all of our

jupyter notebooks for all of our tutorials because I feel like it's a

tutorials because I feel like it's a really great place to learn the basics

really great place to learn the basics but then in future videos I'll show you

but then in future videos I'll show you different idees that you can use for

different idees that you can use for your python code I genuinely cannot wait

your python code I genuinely cannot wait to get started on this series I

to get started on this series I absolutely love python so without

absolutely love python so without further Ado let's jump on my screen I'm

further Ado let's jump on my screen I'm going to show you how to install jupyter

going to show you how to install jupyter notebooks all right so let's get started

notebooks all right so let's get started by downloading anaconda anaconda is an

by downloading anaconda anaconda is an open- Source distribution of python and

open- Source distribution of python and R products so within Anaconda is our

R products so within Anaconda is our Jupiter notebooks as well as a lot of

Jupiter notebooks as well as a lot of other things but we're going to be using

other things but we're going to be using it for our Jupiter notebooks so let's go

it for our Jupiter notebooks so let's go right down here and if I hit download

right down here and if I hit download it's going to download for me because

it's going to download for me because I'm on Windows but if you want

I'm on Windows but if you want additional installers if you're running

additional installers if you're running on Mac or Linux then you can get those

on Mac or Linux then you can get those all right here now if you are running on

all right here now if you are running on Windows just make sure to check your

Windows just make sure to check your system to see if it's a 32bit or a 64

system to see if it's a 32bit or a 64 you can go into your about in your

you can go into your about in your system settings to find that information

system settings to find that information I'm going to click on this 64

I'm going to click on this 64 bit it's going to pop up on my screen

bit it's going to pop up on my screen right here and I'm going to click

right here and I'm going to click save now it's going to start downloading

save now it's going to start downloading it it says it could take a little while

it it says it could take a little while but honestly it's going to take probably

but honestly it's going to take probably about 2 to three minutes and then we'll

about 2 to three minutes and then we'll get going now that it's done I'm just

get going now that it's done I'm just going to click on it and it's going to

going to click on it and it's going to pull up this window right here we are

pull up this window right here we are just going to click next because we want

just going to click next because we want to install it this is our license

to install it this is our license agreement you can read through this if

agreement you can read through this if you would like I will not I'm just going

you would like I will not I'm just going to click I agree now we can select our

to click I agree now we can select our installation type and you can either

installation type and you can either select it for just me or if you have

select it for just me or if you have multiple admin or users on one laptop

multiple admin or users on one laptop you can do that as well for me it's just

you can do that as well for me it's just me so I'm going to use this one as it

me so I'm going to use this one as it recommends now it's going to show you

recommends now it's going to show you where it's installing it on your

where it's installing it on your computer this is the actual file path

computer this is the actual file path it's going to take about 3.5 gigs of

it's going to take about 3.5 gigs of space I have plenty of space but make

space I have plenty of space but make sure you have enough space and then once

sure you have enough space and then once you do you can come right over here to

you do you can come right over here to next and now we can do some Advanced

next and now we can do some Advanced options we can add Anaconda 3 to my path

options we can add Anaconda 3 to my path environment variable

environment variable and when you're using python you

and when you're using python you typically have a default path with

typically have a default path with whatever python IDE or notebook that

whatever python IDE or notebook that you're using I use a lot of Visual

you're using I use a lot of Visual Studio code so if I do this I'm worried

Studio code so if I do this I'm worried it might mess something up so I am not

it might mess something up so I am not going to do this it also says it doesn't

going to do this it also says it doesn't recommend it again messing with these

recommend it again messing with these paths is kind of something that you

paths is kind of something that you might want to do once you know more

might want to do once you know more about python so I don't really recommend

about python so I don't really recommend you having this checked we can also

you having this checked we can also register in AA 3 as my default python

3.9 you can do this one and I'm to keep it this way just so I have the exact

it this way just so I have the exact same settings as you do so let's go

same settings as you do so let's go ahead and click install and now it is

ahead and click install and now it is going to actually install this on your

going to actually install this on your computer now once that's complete we can

computer now once that's complete we can hit next and now we're going to hit next

hit next and now we're going to hit next again and finally we're going to hit

again and finally we're going to hit finish but if you want to you can have

finish but if you want to you can have this tutorial and this getting started

this tutorial and this getting started with Anaconda I don't want either of

with Anaconda I don't want either of them because I don't need them but if

them because I don't need them but if you would like to have those keep those

you would like to have those keep those checked and you can get those let's

checked and you can get those let's click finish now let's go down and and

click finish now let's go down and and we're going to search for Anaconda and

we're going to search for Anaconda and it'll say Anaconda Navigator and we're

it'll say Anaconda Navigator and we're going to click on that and it should

going to click on that and it should open up for us so this is what you

open up for us so this is what you should be seeing on your screen this is

should be seeing on your screen this is the Anaconda Navigator and this is where

the Anaconda Navigator and this is where that distribution of python and R is

that distribution of python and R is going to be so we have a lot of

going to be so we have a lot of different options in here and some of

different options in here and some of them may look familiar we have things

them may look familiar we have things like Visual Studio code spider our

like Visual Studio code spider our studio and then right up here we have

studio and then right up here we have our Jupiter notebooks and this is what

our Jupiter notebooks and this is what work we're going to be using throughout

work we're going to be using throughout our tutorials so let's go ahead and

our tutorials so let's go ahead and click on launch and this is what should

click on launch and this is what should kind of pop up on your screen now I've

kind of pop up on your screen now I've been using this a lot um so I have a ton

been using this a lot um so I have a ton of notebooks and files in here but if

of notebooks and files in here but if you are just now seeing this it might be

you are just now seeing this it might be completely blank or just have some you

completely blank or just have some you know default folders in here but this is

know default folders in here but this is where we're going to open up a new

where we're going to open up a new Jupiter notebook where we can write code

Jupiter notebook where we can write code and all the things that we're going to

and all the things that we're going to be learning in future tutorials and you

be learning in future tutorials and you can use this area to save things and

can use this area to save things and create folders and organize everything

create folders and organize everything if you already have some notebooks from

if you already have some notebooks from previous projects or something you can

previous projects or something you can upload them here but what we're going to

upload them here but what we're going to do is go right to this new we're going

do is go right to this new we're going to click on the drop down and we're

to click on the drop down and we're going to open up a Python 3 kernel and

going to open up a Python 3 kernel and so we're going to open this up right

so we're going to open this up right here now right here is where we're going

here now right here is where we're going to be spending 99% of our time in future

to be spending 99% of our time in future videos this is where we're going to

videos this is where we're going to write all of our code so right here is a

write all of our code so right here is a cell and this is where we can type

cell and this is where we can type things so I can say print I can do the

things so I can say print I can do the famous hello world

famous hello world and then I'll run that by clicking shift

and then I'll run that by clicking shift enter and this is where all of our code

enter and this is where all of our code is going to go these are called cells so

is going to go these are called cells so each one of these are a cell and we have

each one of these are a cell and we have a ton of stuff up here and I'm going to

a ton of stuff up here and I'm going to get to that in just a second one thing I

get to that in just a second one thing I wanted to show you is that you don't

wanted to show you is that you don't only have to write code here you can

only have to write code here you can also do something called markdown and so

also do something called markdown and so markdown is its own kind of you could

markdown is its own kind of you could say language but um it's just a

say language but um it's just a different way of writing especially

different way of writing especially within a notebook so all we're going to

within a notebook so all we're going to do is do this little hashtag and

do is do this little hashtag and actually I think it's a pound sign but

actually I think it's a pound sign but I'm G to call it hashtag we're going to

I'm G to call it hashtag we're going to do that and we're going to say first

do that and we're going to say first notebook and then if I run that we have

notebook and then if I run that we have our first notebook and we can make

our first notebook and we can make little comments and little notes like

little comments and little notes like that that don't actually run any code

that that don't actually run any code they just kind of organize things for us

they just kind of organize things for us and I'm going to do that in a lot of our

and I'm going to do that in a lot of our future videos so just want to show you

future videos so just want to show you how to do that now let's look right up

how to do that now let's look right up here a lot of these things are pretty

here a lot of these things are pretty important uh one of the first things

important uh one of the first things that's really important is actually

that's really important is actually saving this so let's say we wanted to

saving this so let's say we wanted to change the title to I'm going to do a AA

change the title to I'm going to do a AA because I want it to be at the beginning

because I want it to be at the beginning um so I can show you this I'm do AA a

um so I can show you this I'm do AA a new notebook and I'm going to rename it

new notebook and I'm going to rename it and then I'm going to save that so if I

and then I'm going to save that so if I go right back over here you can see AAA

go right back over here you can see AAA new notebook that green means that it's

new notebook that green means that it's currently running and when I say running

currently running and when I say running I mean right up here and if we wanted to

I mean right up here and if we wanted to we go ahead and shut that down which

we go ahead and shut that down which means it wouldn't run the code anymore

means it wouldn't run the code anymore and then we'd have to run up a new

and then we'd have to run up a new cluster uh so let's go ahead and do that

cluster uh so let's go ahead and do that I didn't plan on doing that but let's do

I didn't plan on doing that but let's do it so we have no notebooks running and

it so we have no notebooks running and right here it says we have a dead kernel

right here it says we have a dead kernel so this was our Python 3 kernel and now

so this was our Python 3 kernel and now since I stopped it it's no longer

since I stopped it it's no longer processing anything so let's go ahead

processing anything so let's go ahead and say try restarting

and say try restarting now and it says kernel is ready so it's

now and it says kernel is ready so it's back up and running and we're good to go

back up and running and we're good to go the next thing is this button right here

the next thing is this button right here now this is an insert cell below so if I

now this is an insert cell below so if I have a lot of code I know I'm going to

have a lot of code I know I'm going to be writing I can click a lot of that and

be writing I can click a lot of that and I often do that because I just don't

I often do that because I just don't like having to do that all the time so I

like having to do that all the time so I make a bunch of cells just so I can use

make a bunch of cells just so I can use them you can delete cells so say we have

them you can delete cells so say we have some code here we'll say here and we

some code here we'll say here and we have code here and then we have this

have code here and then we have this empty cell right here we can just get

empty cell right here we can just get rid of that by doing this cut selected

rid of that by doing this cut selected cells we can also copy selected cells so

cells we can also copy selected cells so if I hit copy selected cells and I can

if I hit copy selected cells and I can go right here and say paste selected

go right here and say paste selected cells and as you can see it pasted that

cells and as you can see it pasted that exact same cell you can also move this

exact same cell you can also move this up and down so I can actually take this

up and down so I can actually take this one and say I wanted it in this location

one and say I wanted it in this location I can take this cell and move it up or I

I can take this cell and move it up or I can move it down and that's just an easy

can move it down and that's just an easy way to kind of organize it instead of

way to kind of organize it instead of having to like copy this and moving it

having to like copy this and moving it right down here and pasting it you can

right down here and pasting it you can just take this cell and move it up which

just take this cell and move it up which is really nice now earlier when I ran

is really nice now earlier when I ran this code right here I hit shift enter

this code right here I hit shift enter you can also run and it'll run the cell

you can also run and it'll run the cell below so you can hit run and it works

below so you can hit run and it works properly if you're running a script and

properly if you're running a script and it's taking forever and it's not working

it's taking forever and it's not working properly at least it's you don't think

properly at least it's you don't think it's working properly you can stop that

it's working properly you can stop that by doing this interrupt the kernel right

by doing this interrupt the kernel right here and anything you're trying to do

here and anything you're trying to do within this kernel if it's just not

within this kernel if it's just not working properly it'll stop it you can

working properly it'll stop it you can restart it then you can try fixing your

restart it then you can try fixing your code you can also hit this button if you

code you can also hit this button if you want to restart your kernel and this

want to restart your kernel and this button if you want to restart the kernel

button if you want to restart the kernel and then rerun the entire notebook as we

and then rerun the entire notebook as we talked about just a second ago we have

talked about just a second ago we have our code and our markdown code we're not

our code and our markdown code we're not going to talk about either of these

going to talk about either of these because we're not going to use that

because we're not going to use that throughout the entire series the next

throughout the entire series the next thing I want to show you is right up

thing I want to show you is right up here if you open this file we can create

here if you open this file we can create a new notebook we can open an existing

a new notebook we can open an existing notebook we can copy it save it rename

notebook we can copy it save it rename it all that good stuff we can also edit

it all that good stuff we can also edit it so a lot of these things that we were

it so a lot of these things that we were talking about you can cut the cells and

talking about you can cut the cells and copy the cells using these shortcuts if

copy the cells using these shortcuts if you would like to we also go to view and

you would like to we also go to view and you can toggle a lot of these things if

you can toggle a lot of these things if you would like to which just means it'll

you would like to which just means it'll show it or not show it depending on what

show it or not show it depending on what you want so if we toggle this toolbar

you want so if we toggle this toolbar it'll take away the toolbar for us or if

it'll take away the toolbar for us or if we go back and we toggle the toolbar we

we go back and we toggle the toolbar we can bring it back we can also insert a

can bring it back we can also insert a few different things like inserting a

few different things like inserting a cell above or a cell below so instead of

cell above or a cell below so instead of saying This plus button you can just say

saying This plus button you can just say A or B adding above or below we also

A or B adding above or below we also have the cell in which we can run our

have the cell in which we can run our cells or run all of them or all above or

cells or run all of them or all above or all below and then we have our kernels

all below and then we have our kernels right here which we were talking about

right here which we were talking about earlier where we can interrupt it and

earlier where we can interrupt it and restart those there are widgets we're

restart those there are widgets we're not going to be looking at any widgets

not going to be looking at any widgets in this series but if it's something

in this series but if it's something you're interested in you can definitely

you're interested in you can definitely do that and then we have help so if you

do that and then we have help so if you are looking for some help on any of

are looking for some help on any of these things especially some of these

these things especially some of these references which are really nice you can

references which are really nice you can use those and you can also edit your own

use those and you can also edit your own keyboard shortcuts and now that we

keyboard shortcuts and now that we walked through all of that you now have

walked through all of that you now have anacon and jupyter notebooks installed

anacon and jupyter notebooks installed on your computer in future videos this

on your computer in future videos this is where we're going to be writing all

is where we're going to be writing all of our python code so be sure to check

of our python code so be sure to check those out so we can learn python

those out so we can learn python together thank you guys so much for

together thank you guys so much for watching I hope you were able to get

watching I hope you were able to get everything installed correctly I am

everything installed correctly I am super excited for this series ahead of

super excited for this series ahead of us if you like this video be sure to

us if you like this video be sure to like And subscribe below and I will see

like And subscribe below and I will see you in the next

you in the next [Music]

[Music] video

[Music] hello everybody today we're going to be

hello everybody today we're going to be learning about variables in Python a

learning about variables in Python a variable is basically just a container

variable is basically just a container for storing data values so you'll take a

for storing data values so you'll take a value like a number or a string you can

value like a number or a string you can assign it to a variable and then the

assign it to a variable and then the variable will carry and contain whatever

variable will carry and contain whatever you put into it so for example let's go

you put into it so for example let's go right over here we're going to say x and

right over here we're going to say x and this is going to be our variable we're

this is going to be our variable we're going to say is equal to now we can

going to say is equal to now we can assign the value to it so let's say I

assign the value to it so let's say I want to put

want to put 22 x is now equal to 22 so we won't have

22 x is now equal to 22 so we won't have to write out the number 22 in later

to write out the number 22 in later scripts that we write we can just say x

scripts that we write we can just say x because X is equal to 22 it now contains

because X is equal to 22 it now contains that number so now we can hit enter and

that number so now we can hit enter and say print we do an open parentheses and

say print we do an open parentheses and we'll say x now I'm going to hit shift

we'll say x now I'm going to hit shift enter and now it prints out that 22

enter and now it prints out that 22 because we are printing x and x is equal

because we are printing x and x is equal 22 this is our value and this is our

22 this is our value and this is our variable one really great thing about

variable one really great thing about variables is that it assigns its own

variables is that it assigns its own data type it's going to automatically do

data type it's going to automatically do this so we didn't have to go and tell X

this so we didn't have to go and tell X that it's an integer it just

that it's an integer it just automatically knew that 22 is a number

automatically knew that 22 is a number so we can check that by saying type and

so we can check that by saying type and then open parenthesis and writing X and

then open parenthesis and writing X and we'll do shift enter again and this says

we'll do shift enter again and this says that X is an integer type now we only

that X is an integer type now we only assigned an integer to X let's try

assigned an integer to X let's try assigning a string value or some text to

assigning a string value or some text to a variable so we'll say Y is equal to uh

a variable so we'll say Y is equal to uh let's say mint chocolate chip I'm

let's say mint chocolate chip I'm feeling some ice cream today so we'll

feeling some ice cream today so we'll say mint chocolate chip now if we print

say mint chocolate chip now if we print that again we'll do print open

that again we'll do print open parenthesis Y and do shift enter it'll

parenthesis Y and do shift enter it'll print mint chocolate chip and if we look

print mint chocolate chip and if we look at the type we can see that the type is

at the type we can see that the type is a string this time and not an integer

a string this time and not an integer now again we did not tell it that X was

now again we did not tell it that X was an integer and Y was a string it just

an integer and Y was a string it just automatically knew this let's go up here

automatically knew this let's go up here really quickly we're going to add

really quickly we're going to add several rows in here because we're about

several rows in here because we're about to write a lot of different variables

to write a lot of different variables and really learn in- depth how to use

and really learn in- depth how to use variables the next thing to know about

variables the next thing to know about variables is that you can overwrite

variables is that you can overwrite previous variables right now we have

previous variables right now we have mint chocolate chip and that is assigned

mint chocolate chip and that is assigned to the variable y so if I go down here I

to the variable y so if I go down here I say print y I hit shift enter it's going

say print y I hit shift enter it's going to print out mint chocolate chip but

to print out mint chocolate chip but if I go right above it I say Y is equal

if I go right above it I say Y is equal to and let's say chocolate if I print

to and let's say chocolate if I print that out it's now going to say chocolate

that out it's now going to say chocolate whereas up here I'm reassigning it to Y

whereas up here I'm reassigning it to Y it's still going to say mint chocolate

it's still going to say mint chocolate chip so if I come right down here and I

chip so if I come right down here and I copy this and I'm going to paste this

copy this and I'm going to paste this right here initially it is going to

right here initially it is going to assign y to Chocolate but then right

assign y to Chocolate but then right here it will automatically overwrite y

here it will automatically overwrite y as mint chocolate chip and when we hit

as mint chocolate chip and when we hit shift enter it's going to show mint

shift enter it's going to show mint chocolate chip variables are also case

chocolate chip variables are also case sensitive so if I come up here and I say

sensitive so if I come up here and I say a capital Y this is a lowercase Y and

a capital Y this is a lowercase Y and this is a capital Y it is going to print

this is a capital Y it is going to print out the correct one instead of mint

out the correct one instead of mint chocolate chip and then if I go down

chocolate chip and then if I go down here to the print and I type the capital

here to the print and I type the capital Y it will give us the mint chocolate

Y it will give us the mint chocolate chip up till now we've only assigned one

chip up till now we've only assigned one value to one variable but we can

value to one variable but we can actually assign multiple values to

actually assign multiple values to multiple variables so let's do X comma y

multiple variables so let's do X comma y comma Z is equal to and now we can

comma Z is equal to and now we can assign multiple values to all of those

assign multiple values to all of those so we can say

so we can say chocolate and then we'll do a comma oops

chocolate and then we'll do a comma oops a comma then we can say vanilla and then

a comma then we can say vanilla and then we'll do another comma and we'll say

we'll do another comma and we'll say rocky road now this is going to assign

rocky road now this is going to assign chocolate to X

chocolate to X vanilla to Y and Rocky Road to Z so what

vanilla to Y and Rocky Road to Z so what we can do is we'll say

we can do is we'll say print and we'll go print print print and

print and we'll go print print print and we'll say X Y and Z so it prints out

we'll say X Y and Z so it prints out chocolate vanilla and rocky road and

chocolate vanilla and rocky road and these are our three different values we

these are our three different values we can also assign multiple variables to

can also assign multiple variables to one value and we can do this by saying X

one value and we can do this by saying X is equal to Y is equal to Z is equal to

is equal to Y is equal to Z is equal to and we can put whatever we would like

and we can put whatever we would like let's do root beer float then we'll come

let's do root beer float then we'll come back up here we'll copy this and let's

back up here we'll copy this and let's print off our X our Y and Z and they are

print off our X our Y and Z and they are all the exact same now so far we've

all the exact same now so far we've really only looked at integers and

really only looked at integers and strings but you can assign things like

strings but you can assign things like lists dictionaries tupal and sets all to

lists dictionaries tupal and sets all to variables as well so let's go right down

variables as well so let's go right down here so let's create our very first list

here so let's create our very first list I'm going to say icore cream is equal to

I'm going to say icore cream is equal to and that is our variable right there the

and that is our variable right there the ice cream is our variable so now we're

ice cream is our variable so now we're going to do an Open Bracket like this

going to do an Open Bracket like this and we're going to come up here and copy

and we're going to come up here and copy all of these values and we're going to

all of these values and we're going to stick it within our list so now within

stick it within our list so now within ice cream we have three string values

ice cream we have three string values chocolate vanilla and rocky road all

chocolate vanilla and rocky road all within this list so what we can do is we

within this list so what we can do is we can say x comma y comma Z is equal to

can say x comma y comma Z is equal to icore cream so so now these three values

icore cream so so now these three values chocolate vanilla and rocky road will be

chocolate vanilla and rocky road will be assigned to these three variables X Y

assigned to these three variables X Y and Z and we can copy this print up

and Z and we can copy this print up here and we'll hit shift enter and now

here and we'll hit shift enter and now the X Y and Z all were assigned these

the X Y and Z all were assigned these values of chocolate vanilla and rocky

values of chocolate vanilla and rocky road now something that we just did

road now something that we just did which is really important or something

which is really important or something that you really need to consider is how

that you really need to consider is how you name your variables so right here we

you name your variables so right here we have ice cream now this to me is exactly

have ice cream now this to me is exactly how I usually write my variables but

how I usually write my variables but there are many different ways that you

there are many different ways that you can write your variables so let's take a

can write your variables so let's take a look at that really quickly and let's

look at that really quickly and let's add just a few more because I have a

add just a few more because I have a feeling we're going to go a little bit

feeling we're going to go a little bit longer than what we have so there are a

longer than what we have so there are a few best practices for naming variables

few best practices for naming variables first I'm going to show you kind of what

first I'm going to show you kind of what a lot of people will do I'll show you

a lot of people will do I'll show you some good practices and I'm going to

some good practices and I'm going to show you some bad practices as well that

show you some bad practices as well that you should avoid doing the first thing

you should avoid doing the first thing that we're going to look at is something

that we're going to look at is something called camel case and let's say we want

called camel case and let's say we want to name it t test variable case oops

to name it t test variable case oops case now if we have a test variable case

case now if we have a test variable case the camel case is going to look like

the camel case is going to look like this we'll have lowercase test and then

this we'll have lowercase test and then we'll have uppercase variable and

we'll have uppercase variable and uppercase case is equal to this is what

uppercase case is equal to this is what this variable is going to look like and

this variable is going to look like and we can assign it a nilla

swirl and this is what your camel case will look like it's going to be

will look like it's going to be lowercase and then all the rest of those

lowercase and then all the rest of those uh compound words or however you want to

uh compound words or however you want to say that these letters are going to be

say that these letters are going to be capitalized to kind of separate where

capitalized to kind of separate where the words end and begin let's go right

the words end and begin let's go right down here we're going to copy this the

down here we're going to copy this the next one is called Pascal case so Pascal

next one is called Pascal case so Pascal case is going to look just a little bit

case is going to look just a little bit different instead of the lowercase at

different instead of the lowercase at test it's going to be a capital T in

test it's going to be a capital T in test so test variable case again this is

test so test variable case again this is a very similar way of writing it very

a very similar way of writing it very similar to camel case U but just a

similar to camel case U but just a capital at the beginning now let's look

capital at the beginning now let's look at the last one and this one is my

at the last one and this one is my personal favorite this one is going to

personal favorite this one is going to be the snake case now this one is quite

be the snake case now this one is quite a bit different in the fact that you

a bit different in the fact that you don't use any capital letters and you

don't use any capital letters and you separate everything using underscore so

separate everything using underscore so we're going to write

we're going to write testore variable underscore case now

testore variable underscore case now typically let me have them all in there

typically let me have them all in there typically these are the best practices

typically these are the best practices these are what you typically want to do

these are what you typically want to do but probably the best one to to use is

but probably the best one to to use is this snake case right here what a lot of

this snake case right here what a lot of people say is that it improves

people say is that it improves readability if you take a look at either

readability if you take a look at either the camel case or the Pascal case which

the camel case or the Pascal case which you will see people do it's not as easy

you will see people do it's not as easy to distinguish exactly what it says and

to distinguish exactly what it says and the name of a variable is important

the name of a variable is important because you can gain information from it

because you can gain information from it if people name them appropriately so

if people name them appropriately so when I'm naming variables I usually

when I'm naming variables I usually write it in snake case because I just

write it in snake case because I just find it a lot easier to read because

find it a lot easier to read because each word is broken up by this

each word is broken up by this underscore score so now let's look at

underscore score so now let's look at some good variable names these are all

some good variable names these are all ones that you can use or could use let's

ones that you can use or could use let's do something like test VAR so test VAR

do something like test VAR so test VAR is completely appropriate we can also do

is completely appropriate we can also do something like testore VAR oops

something like testore VAR oops underscore we could do underscore test

underscore we could do underscore test underscore VAR you'll see that often as

underscore VAR you'll see that often as well well people will start it with an

well well people will start it with an underscore you can do test

underscore you can do test bar capital T oops capital T capital V

bar capital T oops capital T capital V in test VAR or you could even do

in test VAR or you could even do something like test VAR two now adding a

something like test VAR two now adding a number to your variable is not

number to your variable is not inherently a Bad Thing usually it's

inherently a Bad Thing usually it's semif fround upon but there are

semif fround upon but there are definitely some use cases where you can

definitely some use cases where you can use it but one thing that you cannot do

use it but one thing that you cannot do is do something

is do something like putting the two at the front if you

like putting the two at the front if you put the two at the front it no longer

put the two at the front it no longer works it won't run properly at all so

works it won't run properly at all so we're going to take that out so we can't

we're going to take that out so we can't do that so I'm going to use this as an

do that so I'm going to use this as an example of what you should not do you

example of what you should not do you also can't use a dash so something like

also can't use a dash so something like test- var2 that doesn't work either and

test- var2 that doesn't work either and you also can't use something like a

you also can't use something like a space or a comma or really any kind of

space or a comma or really any kind of symbol like a period or a backslash or

symbol like a period or a backslash or equal sign none of those things will

equal sign none of those things will work within your variable now another

work within your variable now another thing that you can do within your

thing that you can do within your variable is use the plus sign so let's

variable is use the plus sign so let's assign this we'll say x is equal to and

assign this we'll say x is equal to and we'll do a string we'll say ice

we'll do a string we'll say ice cream is my

cream is my favorite and then we'll do a plus sign

favorite and then we'll do a plus sign and we'll say period now what this will

and we'll say period now what this will do is it will literally add these two

do is it will literally add these two strings together so let's do print and

strings together so let's do print and we'll do X so now it says ice cream is

we'll do X so now it says ice cream is my favorite one thing that we cannot do

my favorite one thing that we cannot do in a variable is we cannot add a string

in a variable is we cannot add a string and a number or an integer so we can't

and a number or an integer so we can't do ice cream as my favorite two if we

do ice cream as my favorite two if we try to do that it will give us this

try to do that it will give us this error right here so in this error it's

error right here so in this error it's saying you can only concatenate a string

saying you can only concatenate a string not an integer to a string so only a

not an integer to a string so only a string plus a string for this example

string plus a string for this example you can also do and we'll say x is equal

you can also do and we'll say x is equal to or we'll say

to or we'll say y we'll say Y is equal

y we'll say Y is equal to 3 + 2 and it should output five

to 3 + 2 and it should output five because you can also do an integer and

because you can also do an integer and an integer now so far we've only been

an integer now so far we've only been outputting one variable in the print

outputting one variable in the print statement but you can actually add

statement but you can actually add multiple variables within a print

multiple variables within a print statement so let's go right down here

statement so let's go right down here we're going to say let's give it some

we're going to say let's give it some more right there so we'll say x is equal

more right there so we'll say x is equal to ice

to ice cream and we'll say Y is equal

cream and we'll say Y is equal to is and then the last one Z is equal

to is and then the last one Z is equal to my favorite and we'll do a period at

to my favorite and we'll do a period at the end now we can go to the bottom and

the end now we can go to the bottom and we can say print x + y + C and when we

we can say print x + y + C and when we enter

enter that and when we run and when we run

that and when we run and when we run that we get ice cream is my favorite now

that we get ice cream is my favorite now we can actually add a space before is a

we can actually add a space before is a space before my and when we hit shift

space before my and when we hit shift enter it says ice cream is my favorite

enter it says ice cream is my favorite you can also do this exact same thing

you can also do this exact same thing with numbers as well so we'll say x = to

with numbers as well so we'll say x = to 1 2 and what Z is equal to three so this

1 2 and what Z is equal to three so this should equal six now one thing that we

should equal six now one thing that we tried to do was assign to one variable a

tried to do was assign to one variable a string plus an integer and that did not

string plus an integer and that did not work but what you can do is you can take

work but what you can do is you can take something like this and you can say ice

something like this and you can say ice cream and we'll get rid of this one and

cream and we'll get rid of this one and we'll get rid of the Z now saying plus

we'll get rid of the Z now saying plus is actually not going to work let's try

is actually not going to work let's try running this

running this so again we can't concatenate these but

so again we can't concatenate these but what we can do in the print statement is

what we can do in the print statement is we can separate it by a comma so when we

we can separate it by a comma so when we add this comma it should work properly

add this comma it should work properly let's hit enter and it says ice cream 2

let's hit enter and it says ice cream 2 again this makes no sense but you are

again this makes no sense but you are able to combine a string and an integer

able to combine a string and an integer separating by a comma now this is the

separating by a comma now this is the meat and potatoes of variables there are

meat and potatoes of variables there are some other things as well but some of

some other things as well but some of those things are a little bit more

those things are a little bit more advanced and not something I wanted to

advanced and not something I wanted to cover in this tutorial although we may

cover in this tutorial although we may be looking at some of those things in

be looking at some of those things in future tutorials

future tutorials but this is definitely the basics what

but this is definitely the basics what you really really need to know about

you really really need to know about variables I hope that this video was

variables I hope that this video was helpful if it was be sure to like And

helpful if it was be sure to like And subscribe below and I will see you in

subscribe below and I will see you in the next

the next [Music]

[Music] video hello everybody today we're going

video hello everybody today we're going to be talking about data types in Python

to be talking about data types in Python data types are the classification of the

data types are the classification of the data that you are storing these

data that you are storing these classifications tell you what operations

classifications tell you what operations can be performed on your data we're

can be performed on your data we're going to be looking at the main data

going to be looking at the main data types within python including numeric

types within python including numeric sequence type set Boolean and dictionary

sequence type set Boolean and dictionary so let's get started actually writing

so let's get started actually writing some of this out and first let's look at

some of this out and first let's look at numeric there are three different types

numeric there are three different types of numeric data types we have integers

of numeric data types we have integers float and complex numbers let's take a

float and complex numbers let's take a look at integers an integer is basically

look at integers an integer is basically just a whole number whether it's

just a whole number whether it's positive or negative so an integer could

positive or negative so an integer could be a 12 and we can check that by saying

be a 12 and we can check that by saying type we'll do an open parenthesis and a

type we'll do an open parenthesis and a Clos parenthesis and if we say the type

Clos parenthesis and if we say the type of 12 it's going to give us an integer

of 12 it's going to give us an integer or if we say a -2 that is also an

or if we say a -2 that is also an integer we can also perform basic

integer we can also perform basic calculations like -2 + 100 and that'll

calculations like -2 + 100 and that'll tell us it is also an integer so whether

tell us it is also an integer so whether it's just a static value or you're

it's just a static value or you're performing an operation on it it's still

performing an operation on it it's still going to be that data type if those

going to be that data type if those numbers are whole numbers whether

numbers are whole numbers whether negative or positive now let's take this

negative or positive now let's take this exact one and let's say

exact one and let's say 12 and we'll do+

12 and we'll do+ 10.25 when we run this it's no longer

10.25 when we run this it's no longer going to be a whole number it'll now be

going to be a whole number it'll now be a float so let's check this and now this

a float so let's check this and now this is a float type because is no longer a

is a float type because is no longer a whole number it's now a decimal number

whole number it's now a decimal number and the last data type within the

and the last data type within the numeric data type is called complex

numeric data type is called complex let's copy this right down here now

let's copy this right down here now personally this is not one that I've

personally this is not one that I've used almost ever but it is one just

used almost ever but it is one just worth noting so you can do 12 plus and

worth noting so you can do 12 plus and let's say 3 J

let's say 3 J and if we do this it's going to give us

and if we do this it's going to give us a complex the complex data type is used

a complex the complex data type is used for imaginary numbers for me it's not

for imaginary numbers for me it's not often used but if you do use it J is

often used but if you do use it J is used as that imaginary number if you use

used as that imaginary number if you use something like C or any other number

something like C or any other number it's going to give you an error J is the

it's going to give you an error J is the only one that will work with it now

only one that will work with it now let's take a look at Boolean values so

let's take a look at Boolean values so we'll say Boolean the Boolean data type

we'll say Boolean the Boolean data type only has two built-in values either true

only has two built-in values either true or false so let's go right down here and

or false so let's go right down here and say type

say type true and when we run this it'll say bu

true and when we run this it'll say bu which stands for Boolean we can do the

which stands for Boolean we can do the exact same thing with false that is also

exact same thing with false that is also Boolean and this can be used with

Boolean and this can be used with something like a comparison operator so

something like a comparison operator so let's say 1 is greater than 5 and let's

let's say 1 is greater than 5 and let's check this this is giving us a Boolean

check this this is giving us a Boolean because it's telling us whether one is

because it's telling us whether one is greater than five let's bring that right

greater than five let's bring that right down here this will give us a false so

down here this will give us a false so it's telling us that one is not greater

it's telling us that one is not greater than five and just as we got a false we

than five and just as we got a false we can say 1 is equal to one and this

can say 1 is equal to one and this should give us a true so now let's take

should give us a true so now let's take a look at our sequence type data types

a look at our sequence type data types and that includes strings lists and

and that includes strings lists and tupal let's start off by looking at

tupal let's start off by looking at strings in Python strings are arrays of

strings in Python strings are arrays of bytes representing Unicode characters

bytes representing Unicode characters when you're using strings you put them

when you're using strings you put them either in a single quote a double quote

either in a single quote a double quote or a trible quote I call them

or a trible quote I call them apostrophes it's just what I was raised

apostrophes it's just what I was raised to call them but most people who use

to call them but most people who use Python call them quotes so right here we

Python call them quotes so right here we have a single quote and that works well

have a single quote and that works well we can do a double quote and that works

we can do a double quote and that works also and as you can see they are the

also and as you can see they are the exact same output and then we have a

exact same output and then we have a triple quote just like this and this is

triple quote just like this and this is called a multi-line so we can write on

called a multi-line so we can write on multiple lines here so let's write a

multiple lines here so let's write a nice little poem so we'll say the ice

nice little poem so we'll say the ice cream vanquished my longing for

cream vanquished my longing for sweets upon this diet

sweets upon this diet I look

I look away it no longer

away it no longer exists on this day and then if we run

exists on this day and then if we run that it's going to look a little bit

that it's going to look a little bit weird it's basically giving us the raw

weird it's basically giving us the raw text which is completely fine but let's

text which is completely fine but let's call this a

call this a multi-line and we're going to call this

multi-line and we're going to call this a variable multi-line and we're going to

a variable multi-line and we're going to come down here and say

come down here and say print and before I run this I have to

print and before I run this I have to make sure that this is Ran So now let's

make sure that this is Ran So now let's print out our multi-line and now we have

print out our multi-line and now we have our nice little poem right down here now

our nice little poem right down here now something to know about these single and

something to know about these single and double quotes is how they're actually

double quotes is how they're actually used so if we use a single quote and we

used so if we use a single quote and we say I've always wanted to eat a gallon

say I've always wanted to eat a gallon of ice cream and then we do an

of ice cream and then we do an apostrophe at the end obviously

apostrophe at the end obviously something went wrong here what went

something went wrong here what went wrong is when you use a single quote and

wrong is when you use a single quote and then within your text within your

then within your text within your sentence you have another apostrophe

sentence you have another apostrophe it's going to give you an error so what

it's going to give you an error so what we want to do is whenever we have a

we want to do is whenever we have a quote within it we need to use a double

quote within it we need to use a double quote these double quotes will negate

quote these double quotes will negate any single quotes that you have within

any single quotes that you have within your statement they won't however negate

your statement they won't however negate another double quote so you need to make

another double quote so you need to make sure you aren't using double quotes

sure you aren't using double quotes within your sentence if you want to do

within your sentence if you want to do something like that you need to use the

something like that you need to use the triple quotes like we did above so we

triple quotes like we did above so we can do double double and then let's

can do double double and then let's paste this within

paste this within it

it and anything you do Within These triple

and anything you do Within These triple quotes will be completely fine as long

quotes will be completely fine as long as you don't do triple quotes within

as you don't do triple quotes within your triple quotes we'll say this is

your triple quotes we'll say this is wrong so even though it's between these

wrong so even though it's between these two triple quotes it doesn't work

two triple quotes it doesn't work exactly again you just have to

exactly again you just have to understand how that works you have to

understand how that works you have to use the proper apostrophes or quotes

use the proper apostrophes or quotes within your string and just to check

within your string and just to check this we can always say here's our

this we can always say here's our multi-line we can always say type of

multi-line we can always say type of multi-line and that is still a string

multi-line and that is still a string one really important thing to know about

one really important thing to know about strings is that they can be indexed

strings is that they can be indexed indexing means that you can search

indexing means that you can search within it and that index starts at zero

within it and that index starts at zero so let's go ahead and create a variable

so let's go ahead and create a variable and we'll just say a is equal to and

and we'll just say a is equal to and let's do the all popular hello world

let's do the all popular hello world let's run this and now when we print the

let's run this and now when we print the string we can say a and we're going to

string we can say a and we're going to do a bracket and now we can search

do a bracket and now we can search throughout our string using the index so

throughout our string using the index so all you have to do is do a colon and we

all you have to do is do a colon and we can say five what this is going to do is

can say five what this is going to do is is going to say zero position zero all

is going to say zero position zero all the way up to five which should give us

the way up to five which should give us the whole hello I believe let's run this

the whole hello I believe let's run this and it's giving us the first five

and it's giving us the first five positions of this string we can also get

positions of this string we can also get rid of the colon and just say something

rid of the colon and just say something like five and then when we run this it's

like five and then when we run this it's actually going to give us position five

actually going to give us position five so this is 0o 1 2 3 4 and then five is

so this is 0o 1 2 3 4 and then five is the space let's do six so we can see the

the space let's do six so we can see the ACT ual letter and that is our w we can

ACT ual letter and that is our w we can also use a negative when we're indexing

also use a negative when we're indexing through our string so we could say -3

through our string so we could say -3 and it'll give us the L because it's NE

and it'll give us the L because it's NE -1 2 and three we can also specify a

-1 2 and three we can also specify a range if we don't want to use the

range if we don't want to use the default of zero so before we did 0 to

default of zero so before we did 0 to five and it started at zero because that

five and it started at zero because that was our default but we could also do two

was our default but we could also do two to five let's run this and now we go

to five let's run this and now we go position 0 1 and then we start at 2 L L

position 0 1 and then we start at 2 L L now we can also also multiply strings

now we can also also multiply strings and we have this a hello world so we can

and we have this a hello world so we can do a * 3 and if we run this it'll give

do a * 3 and if we run this it'll give us hello world three times and we can

us hello world three times and we can also do a plus a and that is Hello World

also do a plus a and that is Hello World hello world now let's go down here and

hello world now let's go down here and take a look at lists lists are really

take a look at lists lists are really fantastic because they store multiple

fantastic because they store multiple values the string was stored as one

values the string was stored as one value multiple characters but a list can

value multiple characters but a list can store multiple separate values so let's

store multiple separate values so let's create our very first list list we'll

create our very first list list we'll say list really quickly and then we'll

say list really quickly and then we'll put a bracket and a bracket means this

put a bracket and a bracket means this is going to be a list there are other

is going to be a list there are other ones like a squiggly bracket and a

ones like a squiggly bracket and a parenthesis these denote that they are

parenthesis these denote that they are different types of data types the

different types of data types the bracket is what makes a list list so to

bracket is what makes a list list so to keep it super simple we'll say one two

keep it super simple we'll say one two three and we'll run this and now we have

three and we'll run this and now we have a list that has three separate values in

a list that has three separate values in it the comma in our list denotes that

it the comma in our list denotes that they are separate values and a list is

they are separate values and a list is indexed just like a string is indexed so

indexed just like a string is indexed so position zero is this one position one

position zero is this one position one is the two and position two is the three

is the two and position two is the three now when we made this list we didn't

now when we made this list we didn't have to use any quotes because these are

have to use any quotes because these are numbers but if we wanted to create a

numbers but if we wanted to create a list and we wanted to add string values

list and we wanted to add string values we have to do it with our quotes so

we have to do it with our quotes so we'll say quote cookie

we'll say quote cookie dough then we'll do a comma to separate

dough then we'll do a comma to separate the value and then we'll say

the value and then we'll say strawberry and then we'll do one more

strawberry and then we'll do one more and this will just be chocolate and when

and this will just be chocolate and when we run this we have all three of these

we run this we have all three of these values stored in our list now one of the

values stored in our list now one of the best things about list is you can have

best things about list is you can have any data type within them they don't

any data type within them they don't just have to be numbers or strings you

just have to be numbers or strings you can basically put anything you want in

can basically put anything you want in there so let's create a new list and

there so let's create a new list and let's say

let's say vanilla and then we'll do three and then

vanilla and then we'll do three and then we'll add a list within a list and we'll

we'll add a list within a list and we'll say

say Scoops comma spoon and then we'll get

Scoops comma spoon and then we'll get out of that list and then we'll add

out of that list and then we'll add another value of true for Boolean and

another value of true for Boolean and now we can hit shift enter and we just

now we can hit shift enter and we just created a list with several different

created a list with several different data types within one list now let's

data types within one list now let's take this one list right here with all

take this one list right here with all of our different ice cream flavors we'll

of our different ice cream flavors we'll say icore cream is equal to this list

say icore cream is equal to this list now one thing that's really great about

now one thing that's really great about lists is that they are changeable that

lists is that they are changeable that means we can change the data in here we

means we can change the data in here we can also add and remove items from the

can also add and remove items from the list after we've already created it so

list after we've already created it so let's go and take ice cream and we'll

let's go and take ice cream and we'll say ice cream. append and this is going

say ice cream. append and this is going to append it to the very end of the list

to append it to the very end of the list we do an open parenthesis and let's say

we do an open parenthesis and let's say salted caramel now when we run this and

salted caramel now when we run this and we call it just like this it's going to

we call it just like this it's going to take this list add salted caramel to the

take this list add salted caramel to the end and we'll print it off and as you

end and we'll print it off and as you can see it was added to the list and

can see it was added to the list and just like I said before let me go down

just like I said before let me go down here we can also change things from this

here we can also change things from this list so let's say ice cream and then we

list so let's say ice cream and then we need to look at the indexed position so

need to look at the indexed position so we're going to say zero and that's going

we're going to say zero and that's going to be this cookie d right here we can

to be this cookie d right here we can say that is equal to so we can now

say that is equal to so we can now change that value so let's call that

change that value so let's call that butter econ and now when we call

butter econ and now when we call it we can now see that the cookie dough

it we can now see that the cookie dough was changed to butter peacon another

was changed to butter peacon another thing that you saw just a little bit ago

thing that you saw just a little bit ago is something called a list within a list

is something called a list within a list basically a nested list so we had Scoops

basically a nested list so we had Scoops spoon true let's give this and we'll say

spoon true let's give this and we'll say nested uncore list is equal to now when

nested uncore list is equal to now when we run this we now have this nested list

we run this we now have this nested list so if we look at the index and we say

so if we look at the index and we say zero we'll get vanilla if we say two

zero we'll get vanilla if we say two we'll get Scoops and spoons now since we

we'll get Scoops and spoons now since we have a list within a list we can also

have a list within a list we can also look at the index of that nested list so

look at the index of that nested list so let's now say one and that should give

let's now say one and that should give us just spoon and you can go on and on

us just spoon and you can go on and on and on with this you can do lists within

and on with this you can do lists within lists within lists and all of them will

lists within lists and all of them will have indexing that you can call now

have indexing that you can call now let's go down here and start taking a

let's go down here and start taking a look at tupal so a list and a tupal are

look at tupal so a list and a tupal are actually quite similar but the biggest

actually quite similar but the biggest difference between a list and a tuple is

difference between a list and a tuple is that a tupal is something called

that a tupal is something called immutable it means it cannot be modified

immutable it means it cannot be modified or changed after it's created let's go

or changed after it's created let's go right up here we're going to say

right up here we're going to say Tuple and let's write our very first

Tuple and let's write our very first tupal so we'll say Tuple score

tupal so we'll say Tuple score Scoops is equal to and then we'll do an

Scoops is equal to and then we'll do an open parentheses now these open

open parentheses now these open parentheses you've seen if you do like a

parentheses you've seen if you do like a print statement but that's different

print statement but that's different because that's executing a function this

because that's executing a function this is actually creating a tupal which is

is actually creating a tupal which is going to store data for us so we'll say

going to store data for us so we'll say one 2 3 two and one let's go ahead and

one 2 3 two and one let's go ahead and create that Tuple and we can just check

create that Tuple and we can just check the data type really quickly and it's a

the data type really quickly and it's a tupal and just like we saw before a

tupal and just like we saw before a tupal is also index text so if we go at

tupal is also index text so if we go at the very first position which is a one

the very first position which is a one we will get the output of a one but we

we will get the output of a one but we can't do something like

can't do something like aend and then add a value like three if

aend and then add a value like three if we do that it's going to say Tuple

we do that it's going to say Tuple object has no attribute append it's just

object has no attribute append it's just because you cannot change or add

because you cannot change or add anything to a tupal just like we were

anything to a tupal just like we were talking about before typically people

talking about before typically people will use tupal for when data is never

will use tupal for when data is never going to change an example for this

going to change an example for this might be something like a city name a

might be something like a city name a country a location

country a location something that won't change they

something that won't change they definitely have their use cases but I

definitely have their use cases but I don't think they're as popular as just

don't think they're as popular as just using a list so now let's scroll down

using a list so now let's scroll down and start taking look at sets but really

and start taking look at sets but really quickly let me add a few more cells for

quickly let me add a few more cells for us and let's say

us and let's say sets now a set is somewhat similar to a

sets now a set is somewhat similar to a list and a tupal but they are a little

list and a tupal but they are a little bit different in the fact that they

bit different in the fact that they don't have any duplicate elements

don't have any duplicate elements another big difference is that the

another big difference is that the values within a set cannot be accessed

values within a set cannot be accessed using an index because it doesn't have

using an index because it doesn't have an index because it's actually unordered

an index because it's actually unordered we can still Loop through the items in a

we can still Loop through the items in a set with something like a for Loop but

set with something like a for Loop but we can't access it using the bracket and

we can't access it using the bracket and then accessing its index point so let's

then accessing its index point so let's go ahead and create our very first set

go ahead and create our very first set so we're going to say daily uncore pints

so we're going to say daily uncore pints then we're going to say equal to and to

then we're going to say equal to and to create a set we're going to use these

create a set we're going to use these squiggly brackets I don't know if

squiggly brackets I don't know if there's an actual name for those if I'm

there's an actual name for those if I'm being honest I call them squiggly

being honest I call them squiggly brackets and that's what we're going to

brackets and that's what we're going to go with we're to put in a one a two and

go with we're to put in a one a two and a three so let's go ahead and run

a three so let's go ahead and run this and let's look at the type and as

this and let's look at the type and as you can see it is a set now when we

you can see it is a set now when we print this out it's going to show us one

print this out it's going to show us one a two and a three and those are all the

a two and a three and those are all the values within our set but if we copy

values within our set but if we copy this and we'll say daily pant log this

this and we'll say daily pant log this is going to be every single day maybe I

is going to be every single day maybe I had different

had different values now when we run this and we do

values now when we run this and we do the exact same thing now when we print

the exact same thing now when we print this it's going to have just the unique

this it's going to have just the unique values within that set now a use case

values within that set now a use case for set and this is something that I've

for set and this is something that I've done in the past is comparing two

done in the past is comparing two separate sets maybe you have a list or a

separate sets maybe you have a list or a tupal and you convert that into a set

tupal and you convert that into a set and that will narrow it down to its

and that will narrow it down to its unique values then you can compare the

unique values then you can compare the unique values of one set to the unique

unique values of one set to the unique values in another set and then we can

values in another set and then we can see what's the same and what's different

see what's the same and what's different so let's go down here and let's say

so let's go down here and let's say wife's

wife's uncore daily just copy this right here

uncore daily just copy this right here we'll say is equal to let's do our

we'll say is equal to let's do our squiggly lines let's do one two let's do

squiggly lines let's do one two let's do just random

just random numbers so now this is my daily log and

numbers so now this is my daily log and this is my wife's daily log and now we

this is my wife's daily log and now we can compare these values so let's go

can compare these values so let's go right down here let's say print we'll do

right down here let's say print we'll do my daily logs and then we'll do this bar

my daily logs and then we'll do this bar right here and this is going to show us

right here and this is going to show us the combined unique values it's

the combined unique values it's basically like putting them all in one

basically like putting them all in one second set and then trimming it down to

second set and then trimming it down to just the unique values so we'll take

just the unique values so we'll take wife's daily pintes log and when we run

wife's daily pintes log and when we run this we actually need to run this first

this we actually need to run this first when we run this we should see all the

when we run this we should see all the unique values between these two sets and

unique values between these two sets and so as you can see 0 1 2 3 4 5 6 7 24 31

so as you can see 0 1 2 3 4 5 6 7 24 31 so these are all the unique values

so these are all the unique values between these two

between these two sets we can also do another one and

sets we can also do another one and instead of this bar we're going to do

instead of this bar we're going to do this symbol right here which I believe

this symbol right here which I believe is called an Amper sand

is called an Amper sand don't quote me on that but when we run

don't quote me on that but when we run this it's going to show what matches

this it's going to show what matches that means which ones show up in both

that means which ones show up in both sets so the only ones that show up in

sets so the only ones that show up in both sets are 1 2 3 and five we can also

both sets are 1 2 3 and five we can also do the opposite of that by doing a minus

do the opposite of that by doing a minus sign and this is going to show us what

sign and this is going to show us what doesn't match and so we have four 6 and

doesn't match and so we have four 6 and 31 now where is our 24 that was in our

31 now where is our 24 that was in our wife's daily pints log it's in this one

wife's daily pints log it's in this one but we're subtracting the values on this

but we're subtracting the values on this one so let's reverse reverse this and

one so let's reverse reverse this and we'll say daily pints

we'll say daily pints log and let's run it now those are our

log and let's run it now those are our other values so we're taking the values

other values so we're taking the values of this and then we're subtracting all

of this and then we're subtracting all the ones that are the same and getting

the ones that are the same and getting the remaining values and then for our

the remaining values and then for our last one we can get rid of this and

last one we can get rid of this and we'll do this symbol right here and this

we'll do this symbol right here and this is going to show if a value is either in

is going to show if a value is either in one or the other but not in both so

one or the other but not in both so let's run this so these values are

let's run this so these values are completely unique only two each of those

completely unique only two each of those sets now the very last one that we're

sets now the very last one that we're going to look at in this video is

going to look at in this video is dictionaries so let's go right down here

dictionaries so let's go right down here let's add a few cells and let's say

let's add a few cells and let's say dictionaries now I saved dictionary for

dictionaries now I saved dictionary for last because this one is probably the

last because this one is probably the most different out of all the previous

most different out of all the previous data types that we've looked at within a

data types that we've looked at within a data type we have something called a

data type we have something called a key value pair that means when we use a

key value pair that means when we use a dictionary it's not like a list where

dictionary it's not like a list where you just have a value comma value comma

you just have a value comma value comma value we have a key that indicates what

value we have a key that indicates what that value is attributed to so let's

that value is attributed to so let's write out a dictionary to see how this

write out a dictionary to see how this looks so we're going to say

looks so we're going to say dictionary cream and just like a set we

dictionary cream and just like a set we use a squiggly line but the thing that

use a squiggly line but the thing that differentiates it is that in a

differentiates it is that in a dictionary we'll have that key value

dictionary we'll have that key value pair whereas in a set each value is just

pair whereas in a set each value is just separated by a comma so let's write name

separated by a comma so let's write name and this is our key and then we do a

and this is our key and then we do a colon and this is then where we input

colon and this is then where we input our value so we're going to say Alex

our value so we're going to say Alex freeberg and then we separate that key

freeberg and then we separate that key value Pair by a comma and now we can do

value Pair by a comma and now we can do another key value pair so we'll say

another key value pair so we'll say weekly intake and a colon and we'll say

weekly intake and a colon and we'll say five pints of ice cream do a comma and

five pints of ice cream do a comma and then we'll do favorite ice creams and

then we'll do favorite ice creams and now what we're going to do is we're

now what we're going to do is we're going to put in here a list so within

going to put in here a list so within this dictionary we can also add a list

this dictionary we can also add a list we'll do MCC from mint chocolate chip

we'll do MCC from mint chocolate chip and then we'll add chocolate another one

and then we'll add chocolate another one of my favorites so now we have our very

of my favorites so now we have our very first dictionary let's copy this and run

first dictionary let's copy this and run it and let's just look at the

it and let's just look at the type and as you can see it says that

type and as you can see it says that this is a dictionary let's also print it

this is a dictionary let's also print it out now if we want to we can take our

out now if we want to we can take our dictionary cream and say dot values with

dictionary cream and say dot values with an open parenthesis and when we execute

an open parenthesis and when we execute this we'll see all of the values within

this we'll see all of the values within this dictionary so here's our values of

this dictionary so here's our values of Alex freeberg five mint chocolate chip

Alex freeberg five mint chocolate chip and chocolate we can also say keys and

and chocolate we can also say keys and when we run this all of the keys the

when we run this all of the keys the name weekly intake and favorite ice

name weekly intake and favorite ice creams and we can also

creams and we can also say items so this key value pair is one

say items so this key value pair is one item and this key value pair is another

item and this key value pair is another item now one difference between

item now one difference between something like a list and a dictionary

something like a list and a dictionary is how you call the index but you can't

is how you call the index but you can't call it by doing something like like

call it by doing something like like this where you just do a bracket oops

this where you just do a bracket oops and say zero so this would in theory

and say zero so this would in theory take this very first one right our very

take this very first one right our very first key value pair that's going to

first key value pair that's going to give us an error how you call a

give us an error how you call a dictionary is actually by the key so it

dictionary is actually by the key so it doesn't technically have an index but

doesn't technically have an index but you can specify what you want to call

you can specify what you want to call and take it out so we're going to say

and take it out so we're going to say name and this is going to call that key

name and this is going to call that key right here and when we run this we'll

right here and when we run this we'll get the value which is Alex freeberg one

get the value which is Alex freeberg one other thing that you can do is you can

other thing that you can do is you can also update information in a dictionary

also update information in a dictionary which we can't with some other data

which we can't with some other data types so for this for the name it was

types so for this for the name it was Alex freeberg now let's say Ste freeberg

Alex freeberg now let's say Ste freeberg and when we update that I'm also going

and when we update that I'm also going to

to print the dictionary get rid of this so

print the dictionary get rid of this so it's going to update Christine freeberg

it's going to update Christine freeberg in that value of the name so let's go

in that value of the name so let's go ahead and run this and now it changed

ahead and run this and now it changed the name from Alex freeberg to Christine

the name from Alex freeberg to Christine freeberg we can also update all of these

freeberg we can also update all of these values at one time so let's copy

values at one time so let's copy this and I'm going to put it right down

this and I'm going to put it right down here I'm going to say dictionary.c

here I'm going to say dictionary.c cream. update then we're going to put a

cream. update then we're going to put a bracket or not a bracket but a

bracket or not a bracket but a parentheses around these so now what

parentheses around these so now what we're going to do is update this entire

we're going to do is update this entire thing let me take this say print this

thing let me take this say print this dictionary now we can update this to

dictionary now we can update this to anything we want so instead of here I

anything we want so instead of here I can

can say I'll say

say I'll say weight and because of all that ice cream

weight and because of all that ice cream I now weigh 300 lb so let's run this and

I now weigh 300 lb so let's run this and as you can see it did not delete our key

as you can see it did not delete our key value pair right here instead it just

value pair right here instead it just added to it when you're using the update

added to it when you're using the update we can't actually delete that's the

we can't actually delete that's the delete statement and I'll show you that

delete statement and I'll show you that in just a second but all we did was

in just a second but all we did was added this new value it also is going to

added this new value it also is going to check and see if you changed anything

check and see if you changed anything with your key value pair so we can go in

with your key value pair so we can go in here here and change this value and

here here and change this value and we'll say 10 so now when we run this the

we'll say 10 so now when we run this the value of this key value pair was changed

value of this key value pair was changed but let's say we do want to delete it

but let's say we do want to delete it we'll say deel that stands for delete

we'll say deel that stands for delete part of this dictionary cream and now

part of this dictionary cream and now let's specify the key which will also

let's specify the key which will also delete the value with it well let's

delete the value with it well let's specify the key that we want to get rid

specify the key that we want to get rid of and let's say

of and let's say wait and then let's print that

wait and then let's print that again and as you can see the weight was

again and as you can see the weight was deleted from that dictionary so that is

deleted from that dictionary so that is all we're going to cover in this data

all we're going to cover in this data types video thank you guys so much for

types video thank you guys so much for watching I really appreciate it if you

watching I really appreciate it if you like this video be sure to like And

like this video be sure to like And subscribe below and I'll see you in the

subscribe below and I'll see you in the next

next [Music]

[Music] video hello everybody today we're going

video hello everybody today we're going to be taking a look at comparison

to be taking a look at comparison logical and membership operators in

logical and membership operators in Python operators are used to perform

Python operators are used to perform operations on variables and values for

operations on variables and values for example you're often going to want to

example you're often going to want to compare two separate values to see if

compare two separate values to see if they are the same or if they're

they are the same or if they're different within Python and that's where

different within Python and that's where the comparison operator comes in right

the comparison operator comes in right here you can see our operators you can

here you can see our operators you can also see what they do so this equal sign

also see what they do so this equal sign equal sign stands for equal we have the

equal sign stands for equal we have the does not equal the greater than less

does not equal the greater than less than greater than or equal to and less

than greater than or equal to and less than or equal to and honestly I use

than or equal to and honestly I use these almost every single time I use

these almost every single time I use Python so these are very important to

Python so these are very important to know and know how to use so let's get

know and know how to use so let's get rid of that really quickly and actually

rid of that really quickly and actually start writing it out and see how these

start writing it out and see how these comparison operators work in Python the

comparison operators work in Python the very first one that we're going to look

very first one that we're going to look at is equal to now you can't just say 10

at is equal to now you can't just say 10 is equal to 10 let's try running that

is equal to 10 let's try running that really quickly by clicking shift enter

really quickly by clicking shift enter it's going to say cannot assign to

it's going to say cannot assign to literal that's because this is like

literal that's because this is like assigning a variable we're trying to say

assigning a variable we're trying to say 10 is equal to 10 and then we can call

10 is equal to 10 and then we can call that 10 later but that's not how this

that 10 later but that's not how this actually works what we're trying to do

actually works what we're trying to do is to determine whether 10 is equal to

is to determine whether 10 is equal to 10 so we're going to say equal sign

10 so we're going to say equal sign equal sign and then if we run that by

equal sign and then if we run that by clicking shift enter again it's going to

clicking shift enter again it's going to say true now if we put something else

say true now if we put something else like 50 in there and we try to run this

like 50 in there and we try to run this it's going to say false so really what

it's going to say false so really what you're going to get when you use these

you're going to get when you use these comparison operators is either a true or

comparison operators is either a true or a false if we take this right down here

a false if we take this right down here we can also say does not equal and we're

we can also say does not equal and we're going to use an exclamation point equal

going to use an exclamation point equal sign and that says 10 is not equal to 50

sign and that says 10 is not equal to 50 and that should be true you can also

and that should be true you can also compare strings and variables so let's

compare strings and variables so let's go right down here and we're going to

go right down here and we're going to say vanilla is not

say vanilla is not equal to chocolate and when we run this

equal to chocolate and when we run this it'll say false now if it was the same

it'll say false now if it was the same just like when we did our numbers it

just like when we did our numbers it should say true and we can also compare

should say true and we can also compare variables so we'll say x is equal to

variables so we'll say x is equal to vanilla and Y is equal to chocolate and

vanilla and Y is equal to chocolate and then when we come down here we can say x

then when we come down here we can say x is equal to Y and it'll give us a false

is equal to Y and it'll give us a false and we say X is not equal to Y and it'll

and we say X is not equal to Y and it'll give us a true the next one that we're

give us a true the next one that we're going to take take a look at is the less

going to take take a look at is the less than so let's copy this one right up

than so let's copy this one right up here let's scroll

here let's scroll down and let's say 10 is less than 50

down and let's say 10 is less than 50 now this will come out as true now let's

now this will come out as true now let's say we put a 10 in here before 10 was of

say we put a 10 in here before 10 was of course less than 50 but is 10 less than

course less than 50 but is 10 less than 10 no that's false because they are the

10 no that's false because they are the same so if we want an output that is

same so if we want an output that is true all we would have to add is an

true all we would have to add is an equal sign right here and this would say

equal sign right here and this would say 10 is less than or it is equal to 10 and

10 is less than or it is equal to 10 and now it's true of course we can say the

now it's true of course we can say the exact same thing by saying greater than

exact same thing by saying greater than so 10 is equal or greater than 10

so 10 is equal or greater than 10 that'll be true because 10 is equal to

that'll be true because 10 is equal to 10 we can also say 50 is greater or

10 we can also say 50 is greater or equal to 10 because 50 is obviously

equal to 10 because 50 is obviously greater than 10 now let's look at

greater than 10 now let's look at logical operators that are often

logical operators that are often combined with comparison operators so

combined with comparison operators so our operators are and or and not so if

our operators are and or and not so if you have an and that returns true if

you have an and that returns true if both statements are true if it's or only

both statements are true if it's or only one of the statements has to be true and

one of the statements has to be true and the not basically reverses the result so

the not basically reverses the result so if it was going to return true it would

if it was going to return true it would return false I don't use this not one a

return false I don't use this not one a lot but I will show you how it works so

lot but I will show you how it works so let's actually test that out so before

let's actually test that out so before we were saying 10 is greater than 50 and

we were saying 10 is greater than 50 and of course this returned false so now

of course this returned false so now let's add a parentheses around this 10

let's add a parentheses around this 10 is greater than 50 and we're going to

is greater than 50 and we're going to say and we'll do an open parenthesis 50

say and we'll do an open parenthesis 50 is greater than 10 now this statement

is greater than 10 now this statement right here is true 50 is greater than 10

right here is true 50 is greater than 10 so we have a true statement and a false

so we have a true statement and a false statement but this and is going to look

statement but this and is going to look at both of them and it's going to say

at both of them and it's going to say they both need to be true in order to

they both need to be true in order to return a true so let's try running this

return a true so let's try running this and we still have a false if we want it

and we still have a false if we want it to return true we're going to have to

to return true we're going to have to change this to make it a true statement

change this to make it a true statement so 70 is greater than 50 and 50 is

so 70 is greater than 50 and 50 is greater than 10 when we run this it

greater than 10 when we run this it should return true now let's look at the

should return true now let's look at the or so let's copy this and we'll say 10

or so let's copy this and we'll say 10 is greater than 50 or 50 is greater than

is greater than 50 or 50 is greater than 10 now this is a false statement and

10 now this is a false statement and this is a true statement so if even one

this is a true statement so if even one of them is a true statement the output

of them is a true statement the output should be true and again we can do this

should be true and again we can do this even with strings so we can do

even with strings so we can do vanilla and

vanilla and chocolate there we go and vanilla is

chocolate there we go and vanilla is actually greater than chocolate because

actually greater than chocolate because V is a higher number in the alphabetical

V is a higher number in the alphabetical order so V is like 20 something whereas

order so V is like 20 something whereas chocolate is three right so actually

chocolate is three right so actually looks at the spelling for this so if we

looks at the spelling for this so if we say or here it will come out true and if

say or here it will come out true and if we say and here it should also be true

we say and here it should also be true because V is greater than C and 50 is

because V is greater than C and 50 is greater than 10 so this should also be

greater than 10 so this should also be true now let's copy this right here and

true now let's copy this right here and we're going to say not so what we had

we're going to say not so what we had before is 50 is greater than 10 that

before is 50 is greater than 10 that returned true but now all we're doing is

returned true but now all we're doing is putting not in front of it so instead of

putting not in front of it so instead of returning true it's going to return

returning true it's going to return false so now let's take a look at

false so now let's take a look at membership operators and we use this to

membership operators and we use this to check if something whether it's a value

check if something whether it's a value or a string or something like that is

or a string or something like that is within another value or string or

within another value or string or sequence our operators are in and not in

sequence our operators are in and not in so it's pretty simple if it's in it's

so it's pretty simple if it's in it's going to return true if the sequence

going to return true if the sequence with a specified value is present in the

with a specified value is present in the object just like we were talking about

object just like we were talking about and for not in it's basically the exact

and for not in it's basically the exact same thing if it's not in that object so

same thing if it's not in that object so let's start out by taking a look at a

let's start out by taking a look at a string we're going to say ice _ cream is

string we're going to say ice _ cream is equal to I love chocolate

equal to I love chocolate ice

ice cream and then we're going to say love

cream and then we're going to say love in ice cream and that will will turn

in ice cream and that will will turn true so all we're doing is searching if

true so all we're doing is searching if the word love or that string is in this

the word love or that string is in this larger string we could also just do that

larger string we could also just do that by literally copying this and putting

by literally copying this and putting this where this is so we can check is

this where this is so we can check is this string part of this string and

this string part of this string and it'll say true we can also make a list

it'll say true we can also make a list so we'll say Scoops is equal to and then

so we'll say Scoops is equal to and then we'll do a bracket and we'll say 1 2 3 4

we'll do a bracket and we'll say 1 2 3 4 4 five and then we'll say two in Scoops

4 five and then we'll say two in Scoops so all we're doing is searching to see

so all we're doing is searching to see if two is within this list and that

if two is within this list and that should return true now if we put a six

should return true now if we put a six here and we said not in it will also

here and we said not in it will also return true because six is not in Scoops

return true because six is not in Scoops and that is true and just like we did we

and that is true and just like we did we could also say wanted underscore Scoops

could also say wanted underscore Scoops and we'll say eight so I wanted eight

and we'll say eight so I wanted eight Scoops so we can say wanted Scoops in

Scoops so we can say wanted Scoops in scoops and this should return true

scoops and this should return true because there's not an eight within the

because there's not an eight within the Scoops that we wanted and if we said in

Scoops that we wanted and if we said in and we said we wanted eight is that

and we said we wanted eight is that within our list that we created and

within our list that we created and that's going to return a false so that

that's going to return a false so that is a quick breakdown of comparison

is a quick breakdown of comparison logical and membership operators I hope

logical and membership operators I hope that this was helpful thank you guys so

that this was helpful thank you guys so much for watching if you like this video

much for watching if you like this video be sure to like And subscribe and I will

be sure to like And subscribe and I will see you in the next

see you in the next [Music]

[Music] video

[Music] hello everybody today we're going to be

hello everybody today we're going to be taking a look at the if statement within

taking a look at the if statement within python now it's actually the if LF else

python now it's actually the if LF else statement but that's a mouthful so I'm

statement but that's a mouthful so I'm just going to call it the if else

just going to call it the if else statement now we have this flowchart and

statement now we have this flowchart and I apologize for being blurry but this is

I apologize for being blurry but this is the absolute best one that I could find

the absolute best one that I could find right up top we have our if condition

right up top we have our if condition now if this if condition is true we're

now if this if condition is true we're going to run a body of code but if that

going to run a body of code but if that condition is false we're going to go

condition is false we're going to go over here and go to the LF condition the

over here and go to the LF condition the LF condition or statement is basically

LF condition or statement is basically saying if the first if statement doesn't

saying if the first if statement doesn't work let's try this if statement if this

work let's try this if statement if this LF statement is true it goes to this

LF statement is true it goes to this body of code if it's false it'll come

body of code if it's false it'll come over here to the else and the else is

over here to the else and the else is basically if all these things don't work

basically if all these things don't work then run this body of code now you can

then run this body of code now you can have as many ill if statements as you

have as many ill if statements as you want but you can only have one if

want but you can only have one if statement and one else statement so

statement and one else statement so let's write out some code and see how

let's write out some code and see how this actually looks let's first start

this actually looks let's first start off by writing if that that is our if

off by writing if that that is our if statement and now we have to write our

statement and now we have to write our condition which is about to be either

condition which is about to be either met or not met so we'll say if 25 is

met or not met so we'll say if 25 is greater than 10 which is true we'll say

greater than 10 which is true we'll say colon and then we're going to hit enter

colon and then we're going to hit enter and it's going to automatically indent

and it's going to automatically indent that line of code for us and this is our

that line of code for us and this is our body of code so if 25 is greater than 10

body of code so if 25 is greater than 10 our body of code will execute so for us

our body of code will execute so for us we're just going to write print and

we're just going to write print and we'll say it worked now if we run this

we'll say it worked now if we run this it's going to check is 25 greater than

it's going to check is 25 greater than 10 if that is true true print this so

10 if that is true true print this so let's hit shift enter and it worked now

let's hit shift enter and it worked now let's take this exact code we'll paste

let's take this exact code we'll paste it right down here and we'll say is less

it right down here and we'll say is less than and right now this if statement is

than and right now this if statement is not true so it's not actually going to

not true so it's not actually going to work as you can see there's no output

work as you can see there's no output there's nothing that happened really but

there's nothing that happened really but it did check to see if 25 was less than

it did check to see if 25 was less than 10 but it just wasn't true now we can

10 but it just wasn't true now we can use our else statement so we're going to

use our else statement so we're going to come right down here and we're going to

come right down here and we're going to say else and we'll do a colon and we'll

say else and we'll do a colon and we'll hit enter again automatically indenting

hit enter again automatically indenting and we're going to say print and we're

and we're going to say print and we're going to say it did not work dot dot dot

going to say it did not work dot dot dot so what it's going to do is it's going

so what it's going to do is it's going to come up here and check is 25 less

to come up here and check is 25 less than 10 no it's not so this body of code

than 10 no it's not so this body of code is not going to be executed it's going

is not going to be executed it's going to go right down to this else statement

to go right down to this else statement now this else statement is going to be

now this else statement is going to be printed there's no condition on this so

printed there's no condition on this so the if statement has a condition 25 is

the if statement has a condition 25 is less than 10 this has no condition so if

less than 10 this has no condition so if this doesn't work if this is false it's

this doesn't work if this is false it's going to come down here and it will run

going to come down here and it will run this body of code let's run this by

this body of code let's run this by clicking shift enter and as you can see

clicking shift enter and as you can see our output is it did not work now let's

our output is it did not work now let's go back up here and put greater than

go back up here and put greater than because this is now true it's going to

because this is now true it's going to say if 25 is greater than 10 print it

say if 25 is greater than 10 print it worked and then it's going to stop it's

worked and then it's going to stop it's not going to go to this lse statement at

not going to go to this lse statement at all so let's run this and our output is

all so let's run this and our output is it worked so what if we have a lot of

it worked so what if we have a lot of different conditions that we want to try

different conditions that we want to try let's come right down here this is where

let's come right down here this is where the LF comes in so so really quickly

the LF comes in so so really quickly let's change this to a not true a false

let's change this to a not true a false statement we're going to go down and say

statement we're going to go down and say LF and we're going to say if it is and

LF and we're going to say if it is and let's say

let's say 30 we'll say LF

30 we'll say LF worked so now it's going to check is 25

worked so now it's going to check is 25 less than 10 no it's not let's look at

less than 10 no it's not let's look at the next condition is 25 less than 30

the next condition is 25 less than 30 and if it is we'll print L if worked so

and if it is we'll print L if worked so let's try running this and L if worked

let's try running this and L if worked now we can do as many of these LF

now we can do as many of these LF statements as we want we can do let's

statements as we want we can do let's just try a few of them right here so

just try a few of them right here so we'll say if 25 is less than 20 is less

we'll say if 25 is less than 20 is less than

than 21 and let's do 40 and let's do 50 so

21 and let's do 40 and let's do 50 so we'll say LF lf2 lf3 and lf4 now if you

we'll say LF lf2 lf3 and lf4 now if you look at this the first one that is

look at this the first one that is actually going to work is this 25 to 40

actually going to work is this 25 to 40 right here once this one is checked and

right here once this one is checked and it comes out as true none of the other

it comes out as true none of the other LF or L statements will work so let's

LF or L statements will work so let's try this one it should be

try this one it should be lf3 and this one ran properly now within

lf3 and this one ran properly now within our condition so far we've only used a

our condition so far we've only used a comparison operator we can also use a

comparison operator we can also use a logical operator like and or or so we

logical operator like and or or so we can say if 25 is less than 10 which it's

can say if 25 is less than 10 which it's not and let's say or actually and we'll

not and let's say or actually and we'll say or 1 is less than three which is

say or 1 is less than three which is true if we run this now it will actually

true if we run this now it will actually work so we can use several different

work so we can use several different types of operators within our if

types of operators within our if statement to see if a condition is true

statement to see if a condition is true or not or several conditions are true

or not or several conditions are true there's also a way to write an IFL

there's also a way to write an IFL statement in one line if you want to do

statement in one line if you want to do that so we can write print we'll say it

that so we can write print we'll say it worked and then we'll come over here and

worked and then we'll come over here and say if 10 is greater than 30 and then

say if 10 is greater than 30 and then we'll write else print and we'll say it

we'll write else print and we'll say it did not work just like we had before

did not work just like we had before except now it's all occurring on one

except now it's all occurring on one line so let's just try this and see if

line so let's just try this and see if it works so it's saying print it worked

it works so it's saying print it worked if 10 is greater than 30 which it wasn't

if 10 is greater than 30 which it wasn't so it went to the lse statement and then

so it went to the lse statement and then it printed out our body right here

it printed out our body right here although we didn't have any indentation

although we didn't have any indentation or multiple lines it was all done in one

or multiple lines it was all done in one line now there's one other thing that we

line now there's one other thing that we haven't looked at yet uh and I'm going

haven't looked at yet uh and I'm going to show it to you really quickly and

to show it to you really quickly and that's a nested if statement so when we

that's a nested if statement so when we run this it's going to say it worked it

run this it's going to say it worked it works because it says 25 is less than 10

works because it says 25 is less than 10 or one is less than three since this is

or one is less than three since this is true it's going to print out it worked

true it's going to print out it worked but we can also do a nested if statement

but we can also do a nested if statement so we can do multiple if statements as

so we can do multiple if statements as well so we're going to hit enter and

well so we're going to hit enter and we'll say if and we'll do a true

we'll say if and we'll do a true statement here so we'll say if 10 is

statement here so we'll say if 10 is greater than five let's do a colon hit

greater than five let's do a colon hit enter then we'll say print and then

enter then we'll say print and then we'll type A String saying this nested

we'll type A String saying this nested if

if statement oops

statement oops worked now let's try this out and and

worked now let's try this out and and see what we get so it went through the

see what we get so it went through the first if statement it said it was true

first if statement it said it was true and it prints out it worked this is

and it prints out it worked this is still the body of code so it goes down

still the body of code so it goes down to this next if statement and it says if

to this next if statement and it says if 10 is greater than five we're going to

10 is greater than five we're going to print this out and you could do this on

print this out and you could do this on and on and on it can basically go on

and on and on it can basically go on forever and you can create a really

forever and you can create a really in-depth logic and that actually happens

in-depth logic and that actually happens a lot when you start writing more

a lot when you start writing more advanced code so I hope that this was

advanced code so I hope that this was helpful I hope that you understand the

helpful I hope that you understand the IFL statement better I hope that you

IFL statement better I hope that you understand how nested if statements work

understand how nested if statements work as well thank you guys so much for

as well thank you guys so much for watching if you like this video be sure

watching if you like this video be sure to like And subscribe below and I'll see

to like And subscribe below and I'll see you in the next

you in the next [Music]

[Music] video hello everybody today we're going

video hello everybody today we're going to be learning about for Loops in Python

to be learning about for Loops in Python the for Loop is used to iterate over a

the for Loop is used to iterate over a sequence which could be a list a tupal

sequence which could be a list a tupal an array a string or even a dictionary

an array a string or even a dictionary here's the list that we'll be working

here's the list that we'll be working with throughout this video and I have

with throughout this video and I have this little diagram right here which

this little diagram right here which kind of explains how a for Loop works

kind of explains how a for Loop works the for Loop is going to start by

the for Loop is going to start by looking at the very first item in our

looking at the very first item in our sequence or our list and that's going to

sequence or our list and that's going to be our one right here it's going to ask

be our one right here it's going to ask is this the last element in our list and

is this the last element in our list and it is not so it's going to go down to

it is not so it's going to go down to this body of the for Loop now we can

this body of the for Loop now we can have a thousand different things that

have a thousand different things that can happen in the body of the for loop

can happen in the body of the for loop as we're about to look out in just a

as we're about to look out in just a second then it's going to go up to the

second then it's going to go up to the next element and ask is this the last

next element and ask is this the last element reached so it'll be no again

element reached so it'll be no again because we'll be going to the two and

because we'll be going to the two and then the three and then the four and the

then the three and then the four and the five once it reaches the five it'll go

five once it reaches the five it'll go to the body the for Loop and then when

to the body the for Loop and then when it asks if that's the last element the

it asks if that's the last element the answer would be yes because it's

answer would be yes because it's iterated through all the items within

iterated through all the items within the list and then we would exit the loop

the list and then we would exit the loop and the for Loop would be over now that

and the for Loop would be over now that may not have made perfect sense but

may not have made perfect sense but let's actually start writing out the

let's actually start writing out the syntax of a for Loop so we can

syntax of a for Loop so we can understand this better to start our for

understand this better to start our for loop we're going to say four and and

loop we're going to say four and and then we're going to give it a temporary

then we're going to give it a temporary variable for this for Loop so it's a

variable for this for Loop so it's a variable as it iterates through these

variable as it iterates through these numbers it's going to assign the

numbers it's going to assign the variable to that number so for this one

variable to that number so for this one we're just going to say number because

we're just going to say number because it's pretty appropriate because these

it's pretty appropriate because these are all numbers and then we're going to

are all numbers and then we're going to say in integers now right here you can

say in integers now right here you can put just about anything this could be

put just about anything this could be the list this could be a tuple this

the list this could be a tuple this could be a string even but that is what

could be a string even but that is what we're going to iterate through so we're

we're going to iterate through so we're saying for the variables each of these

saying for the variables each of these numbers within this list of integers and

numbers within this list of integers and then we're going to write a colon this

then we're going to write a colon this is the body of code that's going to

is the body of code that's going to actually be executed when we run through

actually be executed when we run through and iterate through our list so for our

and iterate through our list so for our first example we're going to start off

first example we're going to start off super simple and all we're going to do

super simple and all we're going to do is say print open parentheses and say

is say print open parentheses and say number as it iterates through the 1 2 3

number as it iterates through the 1 2 3 4 and five number becomes our variable

4 and five number becomes our variable that is going to be printed so during

that is going to be printed so during that first loop our one will be printed

that first loop our one will be printed because that will be assigned right here

because that will be assigned right here then through the next iteration the two

then through the next iteration the two will be assigned and'll be put right

will be assigned and'll be put right here in each Loop until the very end so

here in each Loop until the very end so let's hit shift

let's hit shift enter and as you can see it did exactly

enter and as you can see it did exactly that now in this body and I'll copy and

that now in this body and I'll copy and paste this down here in this body we

paste this down here in this body we really can do just about anything we

really can do just about anything we want we don't even have to use this

want we don't even have to use this variable number right here we can just

variable number right here we can just print yep if we wanted to and what it's

print yep if we wanted to and what it's going to do is for each iteration all

going to do is for each iteration all five of those every time it Loops

five of those every time it Loops through it's going to print off yep so

through it's going to print off yep so let's hit shift enter and it printed it

let's hit shift enter and it printed it off for us so really we weren't even

off for us so really we weren't even using the numbers within the list we

using the numbers within the list we were really just using it as almost a

were really just using it as almost a counter now let's copy this integers

counter now let's copy this integers once again let's go right up here and

once again let's go right up here and let's go copy this for Loop that we

let's go copy this for Loop that we wrote now we do not have to call this

wrote now we do not have to call this number this can be anything you want any

number this can be anything you want any variable name that you'd like to name it

variable name that you'd like to name it we could call it

we could call it jelly and we can

jelly and we can do jelly plus

do jelly plus jelly I think you're getting the picture

jelly I think you're getting the picture right when it Loops through that one

right when it Loops through that one it's doing 1 plus one when it Loops

it's doing 1 plus one when it Loops through the two it's doing two plus two

through the two it's doing two plus two that is basically how a four Loop works

that is basically how a four Loop works now for a dictionary it's going to

now for a dictionary it's going to handle it a little bit differently so

handle it a little bit differently so let's create a dictionary really quickly

let's create a dictionary really quickly so we'll say ice

so we'll say ice cream dictionary is equal to we're going

cream dictionary is equal to we're going to do a squiggly brackets so we're going

to do a squiggly brackets so we're going to say name and we're going to say colon

to say name and we're going to say colon we need to assign our value for that

we need to assign our value for that item so we're going to say Alex freeberg

item so we're going to say Alex freeberg we'll do our next one separated by a

we'll do our next one separated by a comma and we'll say weekly intake and

comma and we'll say weekly intake and I'll say five Scoops per week the next

I'll say five Scoops per week the next one we will do is favorite ice creams

one we will do is favorite ice creams and for this one we're going to do

and for this one we're going to do something a little bit different for

something a little bit different for this we're going to have a list within

this we're going to have a list within this dictionary so we'll say within our

this dictionary so we'll say within our list of my favorite ice creams we'll say

list of my favorite ice creams we'll say mint chocolate chip and I'll just do MCC

mint chocolate chip and I'll just do MCC for that and we'll separate that out by

for that and we'll separate that out by a comma and we'll say chocolate so now

a comma and we'll say chocolate so now we have this dictionary ice cream dick

we have this dictionary ice cream dick and within it we have my name my weekly

and within it we have my name my weekly intake and my favorite ice creams with a

intake and my favorite ice creams with a list in there as well let's hit shift

list in there as well let's hit shift enter and now we're going to start

enter and now we're going to start writing our for Loop now the for Loop is

writing our for Loop now the for Loop is going to look very similar but to call a

going to look very similar but to call a dictionary it's just a little bit

dictionary it's just a little bit different so we're going to say four the

different so we're going to say four the cream in icore

cream in icore creamore

creamore dictionary. values and then we're going

dictionary. values and then we're going to do parentheses and then a colon now

to do parentheses and then a colon now we're going to print the cream so in

we're going to print the cream so in order to indicate what we actually want

order to indicate what we actually want to pull we have to specify within the

to pull we have to specify within the dictionary what we want are we pulling

dictionary what we want are we pulling the item are we pulling the value we

the item are we pulling the value we need to specify this so that's why we

need to specify this so that's why we have this dot values right here so let's

have this dot values right here so let's run this and see what we get so as you

run this and see what we get so as you can see we are pulling in the values

can see we are pulling in the values right here that's why we're pulling in

right here that's why we're pulling in Alex freeberg 5 and mint chocolate chip

Alex freeberg 5 and mint chocolate chip SL chocolate now we are able to call

SL chocolate now we are able to call both of those both the key and the value

both of those both the key and the value so let's go right down here and we can

so let's go right down here and we can do both the key and the value so we can

do both the key and the value so we can pull two things at one time and we're

pull two things at one time and we're going to do this by saying do items so

going to do this by saying do items so we could also do do key if we just

we could also do do key if we just wanted to do a key but we want to do

wanted to do a key but we want to do items so we going to do both of them

items so we going to do both of them so we're going to go right down here and

so we're going to go right down here and say for key and value in ice cream

say for key and value in ice cream dictionary. items print and let's write

dictionary. items print and let's write key and then we'll do a comma and then

key and then we'll do a comma and then let's give it a little arrow or

let's give it a little arrow or something like that uh something like

something like that uh something like this and then we'll do a comma and we'll

this and then we'll do a comma and we'll say value and let's print this off and

say value and let's print this off and see what we get so it's looping through

see what we get so it's looping through and for each key and value it's saying

and for each key and value it's saying here is the key so that's the name then

here is the key so that's the name then we have weekly intake then we have

we have weekly intake then we have favorite ice creams it's giving us a

favorite ice creams it's giving us a little arrow and then we're also

little arrow and then we're also printing off the value so we have name

printing off the value so we have name Alex freeberg weekly intake five

Alex freeberg weekly intake five favorite ice creams mint chocolate chip

favorite ice creams mint chocolate chip and chocolate so now let's talk about

and chocolate so now let's talk about nested for Loops we've looked at for

nested for Loops we've looked at for Loops we understand how they work and

Loops we understand how they work and why they do what they do but what about

why they do what they do but what about a nested for Loop a for Loop within a

a nested for Loop a for Loop within a for Loop for this example let's create

for Loop for this example let's create two separate lists let's create

two separate lists let's create flavors and let's make that a list by

flavors and let's make that a list by making it a bracket we'll do vanilla the

making it a bracket we'll do vanilla the classic

classic chocolate and then cookie dough all

chocolate and then cookie dough all great flavors so that's our first list

great flavors so that's our first list and then we're going to say toppings and

and then we're going to say toppings and we'll do a bracket for that as well and

we'll do a bracket for that as well and we'll say hot

we'll say hot fudge and then we'll do

fudge and then we'll do Oreos and then we'll do

Oreos and then we'll do marshmallows is how you spell

marshmallows is how you spell marshmallows

marshmallows I think it's an e that looks wrong I

I think it's an e that looks wrong I might be spelling it wrong but that's

might be spelling it wrong but that's okay so let's save this by clicking

okay so let's save this by clicking shift enter and now we have our flavors

shift enter and now we have our flavors and our toppings so now let's write our

and our toppings so now let's write our first for Loops we're going to say 41 as

first for Loops we're going to say 41 as in our number one for loop we're going

in our number one for loop we're going to say in flavors and we'll do a colon

to say in flavors and we'll do a colon we'll click enter now we can write our

we'll click enter now we can write our second for Loop so we're going to say 4

second for Loop so we're going to say 4 two in toppings and then we'll do a

two in toppings and then we'll do a colon and enter and then we're going to

colon and enter and then we're going to say print and we'll do an open

say print and we'll do an open parenthesis and then we're going to say

parenthesis and then we're going to say one so we're printing the one in flavors

one so we're printing the one in flavors and then we're going to say one comma

and then we're going to say one comma I'm going to say topped with comma 2 so

I'm going to say topped with comma 2 so what this is essentially going to do is

what this is essentially going to do is we're going to say for one we're going

we're going to say for one we're going to take the very first one in flavors

to take the very first one in flavors and then we're going to Loop through all

and then we're going to Loop through all of two as well so we're going to Loop

of two as well so we're going to Loop through hot fudge Oreo

through hot fudge Oreo and marshmallows and once we print that

and marshmallows and once we print that off then we will Loop all the way back

off then we will Loop all the way back to Flavors and look at the next

to Flavors and look at the next iteration or the next sequence within

iteration or the next sequence within the first for Loop so let's run this

the first for Loop so let's run this really quickly and see what we get so as

really quickly and see what we get so as you can see it goes vanilla vanilla

you can see it goes vanilla vanilla vanilla and vanilla is topped with the

vanilla and vanilla is topped with the hot fudge the Oreos and the marshmallows

hot fudge the Oreos and the marshmallows and then we start iterating through our

and then we start iterating through our second one in our first four Loop so

second one in our first four Loop so there's that hierarchy so we're

there's that hierarchy so we're iterating completely through this one

iterating completely through this one before we actually go to the very first

before we actually go to the very first for Loop and start iterating through

for Loop and start iterating through that one again now that is essentially

that one again now that is essentially how a nested for Loop works these nested

how a nested for Loop works these nested for Loops can get very complicated in

for Loops can get very complicated in fact for Loops in general can get very

fact for Loops in general can get very complicated the more you add to it and

complicated the more you add to it and the more you're wanting to do with it

the more you're wanting to do with it but that is basically how a for Loop and

but that is basically how a for Loop and a nested for Loop works thank you guys

a nested for Loop works thank you guys so much for watching be sure to like And

so much for watching be sure to like And subscribe below and I'll see you in the

subscribe below and I'll see you in the next

next [Music]

[Music] video

video [Music]

[Music] hello everybody today we're going to be

hello everybody today we're going to be taking a look at while Loops in Python

taking a look at while Loops in Python the while loop in Python is used to

the while loop in Python is used to iterate over a block of code as long as

iterate over a block of code as long as the test condition is true now the

the test condition is true now the difference between a for Loop and a

difference between a for Loop and a while loop is that a for Loop is going

while loop is that a for Loop is going to iterate over the entire sequence

to iterate over the entire sequence regardless of a condition but the while

regardless of a condition but the while loop is only going to iterate over that

loop is only going to iterate over that sequence as long as a specific condition

sequence as long as a specific condition is met once that condition is not met

is met once that condition is not met the code is going to stop and it's not

the code is going to stop and it's not going to inter through the rest of the

going to inter through the rest of the sequence so if we take a look at this

sequence so if we take a look at this flowchart right here we're going to

flowchart right here we're going to enter this while loop and we have a test

enter this while loop and we have a test condition right here the first time that

condition right here the first time that this test condition comes back false

this test condition comes back false it's going to exit the while loop so

it's going to exit the while loop so let's start actually writing out the

let's start actually writing out the code and see how this while loop works

code and see how this while loop works so let's create a variable we're just

so let's create a variable we're just going to say number is equal to one and

going to say number is equal to one and then we'll say while and now we need to

then we'll say while and now we need to write our condition that needs to be met

write our condition that needs to be met in order for our block of code beneath

in order for our block of code beneath this to run so we're going to say while

this to run so we're going to say while number is less than five and then we'll

number is less than five and then we'll do colon enter and now this is our block

do colon enter and now this is our block of code we're going to say print and

of code we're going to say print and then we'll say number now what we need

then we'll say number now what we need to do is basically create a counter

to do is basically create a counter we're going to say number equals number

we're going to say number equals number + 1 if you've never done something like

+ 1 if you've never done something like this it's kind of like a counter most

this it's kind of like a counter most people start it at zero in fact let's

people start it at zero in fact let's start it at zero and then each time it

start it at zero and then each time it runs through this while loop it's going

runs through this while loop it's going to add one to this number up here and

to add one to this number up here and then it's going to become a one a two a

then it's going to become a one a two a three each time it iterates through this

three each time it iterates through this while loop now once this number is no

while loop now once this number is no longer less than five it'll break out of

longer less than five it'll break out of the while loop and it will no longer run

the while loop and it will no longer run so let's run this really quick by

so let's run this really quick by hitting shift enter so it starts at zero

hitting shift enter so it starts at zero and it's going to say while the number

and it's going to say while the number is less than five print number so the

is less than five print number so the first time that it runs through it is

first time that it runs through it is zero and so it prints zero and then it

zero and so it prints zero and then it adds one two number and then it

adds one two number and then it continues that y Loop right here and it

continues that y Loop right here and it keeps looping through this portion it

keeps looping through this portion it never goes back up here to this line of

never goes back up here to this line of code this is just our variable that we

code this is just our variable that we start with and then once this condition

start with and then once this condition is no longer met once it is is false

is no longer met once it is is false then it's going to break out of that

then it's going to break out of that code now that we basically know how a y

code now that we basically know how a y Loop Works let's look at something

Loop Works let's look at something called a break statement so let's copy

called a break statement so let's copy this right down here and what we're

this right down here and what we're going to say is if number is equal to

going to say is if number is equal to three we're going to break now with the

three we're going to break now with the break statement we can basically Stop

break statement we can basically Stop the Loop even if the while condition is

the Loop even if the while condition is true so while this number is less than

true so while this number is less than five it's going to continue to Loop

five it's going to continue to Loop through but now we have this break

through but now we have this break statement so it's going to say if the

statement so it's going to say if the number equals three we're going to break

number equals three we're going to break out out of this while loop but if this

out out of this while loop but if this is false we're going to continue adding

is false we're going to continue adding to that number just like normal so let's

to that number just like normal so let's execute this so as you can see it only

execute this so as you can see it only went to three instead of four like

went to three instead of four like before because each time it was running

before because each time it was running through this while loop it was checking

through this while loop it was checking if the number was equal to three and

if the number was equal to three and once it got to three this became true

once it got to three this became true and then we broke out of this while loop

and then we broke out of this while loop the next thing that I want to look at

the next thing that I want to look at and we'll copy this right down here is

and we'll copy this right down here is an else statement much like an if

an else statement much like an if statement but we can use the lse

statement but we can use the lse statement with a while loop which runs

statement with a while loop which runs the block of code and when that that

the block of code and when that that condition is no longer true then it

condition is no longer true then it activates the else statement so we'll go

activates the else statement so we'll go right down here and we'll say else and

right down here and we'll say else and we'll do a colon and enter and then

we'll do a colon and enter and then we'll say print and we'll say no

we'll say print and we'll say no longer less than five now because this

longer less than five now because this if statement is still in there it will

if statement is still in there it will break so let's say six and then we'll

break so let's say six and then we'll run this and so it's going to iterate

run this and so it's going to iterate through this block of code and once this

through this block of code and once this statement is no longer true once we

statement is no longer true once we break out of it we're going to go to our

break out of it we're going to go to our else state St now as long as this

else state St now as long as this statement is true it's going to continue

statement is true it's going to continue to iterate through but once this

to iterate through but once this condition is not met then it will go to

condition is not met then it will go to our L statement and we'll run that line

our L statement and we'll run that line of code now the L statement is only

of code now the L statement is only going to trigger if the Y Loop no longer

going to trigger if the Y Loop no longer is true if we have something like this

is true if we have something like this if statement that causes it to break out

if statement that causes it to break out of the while loop the L statement will

of the while loop the L statement will no longer work so let's say if the

no longer work so let's say if the number is three and we run this the L

number is three and we run this the L statement is no longer going to trigger

statement is no longer going to trigger so this body of code will not be run now

so this body of code will not be run now the next thing that I want to look at is

the next thing that I want to look at is the continue statement if the continue

the continue statement if the continue statement is triggered it basically

statement is triggered it basically rejects all remaining statements in the

rejects all remaining statements in the current iteration of the loop and then

current iteration of the loop and then we'll go to the next iteration now to

we'll go to the next iteration now to demonstrate this I'm going to change

demonstrate this I'm going to change this break into a continue so before

this break into a continue so before when we had the break if the number was

when we had the break if the number was equal to three it would stop all the

equal to three it would stop all the code completely but when we change this

code completely but when we change this to continue which we'll do right now

to continue which we'll do right now what it's going to do is it's no longer

what it's going to do is it's no longer going to run through any of the

going to run through any of the subsequent code in this block of code

subsequent code in this block of code it's just going to go straight up to the

it's just going to go straight up to the beginning and restart our while loop so

beginning and restart our while loop so what's going to happen when we run this

what's going to happen when we run this is it's going to come to three it's

is it's going to come to three it's going to become three it's going to

going to become three it's going to continue back into the while loop but

continue back into the while loop but it's never going to have that number

it's never going to have that number changed to be added to one to continue

changed to be added to one to continue with the while loop this will basically

with the while loop this will basically create an infinite Loop let's try this

create an infinite Loop let's try this really quickly and as you can see it's

really quickly and as you can see it's going to stay three forever eventually

going to stay three forever eventually this would time out but I'm just going

this would time out but I'm just going to stop the code really quick so if we

to stop the code really quick so if we just change up the order of which we're

just change up the order of which we're doing things we're going to say there

doing things we're going to say there and we're going to put this down here

and we're going to put this down here so what it's going to do now instead of

so what it's going to do now instead of printing the number immediately and then

printing the number immediately and then adding the number later we're going to

adding the number later we're going to add the number right away and then we're

add the number right away and then we're going to say if it is three we're going

going to say if it is three we're going to continue and it's going to print the

to continue and it's going to print the number so let's try executing this and

number so let's try executing this and see what happens so as you can see we no

see what happens so as you can see we no longer have the three in our output what

longer have the three in our output what it did was when we got to the number

it did was when we got to the number three it continued and didn't execute

three it continued and didn't execute this right here which prints off that

this right here which prints off that number so that really is the basics of

number so that really is the basics of the while loop I hope that this was

the while loop I hope that this was helpful I hope that you learned

helpful I hope that you learned something in this video If you did be

something in this video If you did be sure to like And subscribe below and

sure to like And subscribe below and I'll see you in the next

I'll see you in the next [Music]

[Music] video hello everybody today we're going

video hello everybody today we're going to be taking a look at functions in

to be taking a look at functions in Python a function is a block of code

Python a function is a block of code which is only run when you call it so

which is only run when you call it so right here we're defining our function

right here we're defining our function and then this is our body of code that

and then this is our body of code that when we actually call it is going to be

when we actually call it is going to be ran so right here we have our function

ran so right here we have our function call and all we're doing is putting the

call and all we're doing is putting the function with the parenthesis that is

function with the parenthesis that is basically us calling that function and

basically us calling that function and then we have our output throughout this

then we have our output throughout this video I'm going to show you how to write

video I'm going to show you how to write a function as well as pass arguments to

a function as well as pass arguments to that function and then a few other

that function and then a few other things like arbitrary arguments keyword

things like arbitrary arguments keyword arguments and arbitrary keyword

arguments and arbitrary keyword arguments all of these things are really

arguments all of these things are really important to know when you are using

important to know when you are using functions so let's get started by

functions so let's get started by writing our very first function together

writing our very first function together we're going to start off by saying DF

we're going to start off by saying DF that is the keyword for defining a

that is the keyword for defining a function then we can actually name our

function then we can actually name our function and for this one we're just

function and for this one we're just going to do first underscore function

going to do first underscore function and then we do an open parenthesis and

and then we do an open parenthesis and then we'll put a colon we'll hit enter

then we'll put a colon we'll hit enter and it'll automatically indent for us

and it'll automatically indent for us and this is where our body of code is

and this is where our body of code is going to go now within our body of code

going to go now within our body of code we can write just about anything and in

we can write just about anything and in this video I'm not going to get super

this video I'm not going to get super Advanced we're just going to walk

Advanced we're just going to walk through the basics to make sure that you

through the basics to make sure that you understand how to use functions so for

understand how to use functions so for right now all we're going to say is

right now all we're going to say is print we'll do an open parenthesis we'll

print we'll do an open parenthesis we'll do an apostrophe and we'll say we did it

do an apostrophe and we'll say we did it and now we're going to hit shift enter

and now we're going to hit shift enter and this is not going to do anything at

and this is not going to do anything at least you won't see any output from this

least you won't see any output from this if we want to see the output or we

if we want to see the output or we actually want to run that function and

actually want to run that function and some functions don't have outputs but if

some functions don't have outputs but if we want to run that function what we

we want to run that function what we have to do is just copy this and put it

have to do is just copy this and put it right down here and now we're going to

right down here and now we're going to actually call our function so let's go

actually call our function so let's go ahead and click shift enter and now

ahead and click shift enter and now we've successfully called our first

we've successfully called our first function this function is about as

function this function is about as simple as it could possibly be but now

simple as it could possibly be but now let's take it up a notch and start

let's take it up a notch and start looking at arguments so let's go right

looking at arguments so let's go right down here and we're going to say Define

down here and we're going to say Define number underscore squared we'll do a

number underscore squared we'll do a parenthesis and our colon as well now

parenthesis and our colon as well now really quickly when you're naming your

really quickly when you're naming your function it's kind of like naming a

function it's kind of like naming a variable you can use something like X or

variable you can use something like X or Y but I tend to like to be a little bit

Y but I tend to like to be a little bit more descriptive but now let's take a

more descriptive but now let's take a look at passing an argument into a

look at passing an argument into a function the argument is going to be

function the argument is going to be passed right here in the parentheses so

passed right here in the parentheses so for us I'm just going to call it a

for us I'm just going to call it a number and then we're going to hit enter

number and then we're going to hit enter and now we'll write our body of code and

and now we'll write our body of code and all we're going to do for this is type

all we're going to do for this is type print and open parenthesis and we'll say

print and open parenthesis and we'll say number and we'll do two stars at least

number and we'll do two stars at least that's what I call it a star and a two

that's what I call it a star and a two and what this is going to do is it's

and what this is going to do is it's going to take the number that we pass

going to take the number that we pass into our function it's going to put it

into our function it's going to put it right here in our body of code and then

right here in our body of code and then for what we're doing it's going to put

for what we're doing it's going to put it to the power of two and so when the

it to the power of two and so when the user or you run this and call this

user or you run this and call this function this number is something that

function this number is something that you can specify it's an argument that

you can specify it's an argument that you can input that will then be run in

you can input that will then be run in this body of code so let's copy this

this body of code so let's copy this right here and then we'll put it right

right here and then we'll put it right down here into this next cell and we'll

down here into this next cell and we'll say five and so this five is going to be

say five and so this five is going to be passed through into this function and be

passed through into this function and be called right here for this print

called right here for this print statement let's run it and it should

statement let's run it and it should come out as I believe 25 that is my

come out as I believe 25 that is my fault I forgot to actually run this

fault I forgot to actually run this block of code so I'm going to hit shift

block of code so I'm going to hit shift enter so now we've defined our function

enter so now we've defined our function up here and now we can actually call it

up here and now we can actually call it so now we'll hit shift enter and we got

so now we'll hit shift enter and we got our output of 25 now now in this

our output of 25 now now in this function we only called one argument but

function we only called one argument but you can basically call as many arguments

you can basically call as many arguments as you want you just have to separate

as you want you just have to separate them by commas so let's copy this and

them by commas so let's copy this and we'll put it right down here now we'll

we'll put it right down here now we'll say number squared uncore custom and

say number squared uncore custom and then we'll do number and then we'll do

then we'll do number and then we'll do power so now we can specify our number

power so now we can specify our number as well as the power that we want to

as well as the power that we want to raise it to so instead of having two

raise it to so instead of having two which is what you call hardcoded we can

which is what you call hardcoded we can now customize that and we'll have power

now customize that and we'll have power power and now when we call this function

power and now when we call this function we can specify the number and the power

we can specify the number and the power and both of those will go into this body

and both of those will go into this body of code and be run and we can customize

of code and be run and we can customize those numbers so let's copy

those numbers so let's copy this and we'll

this and we'll say 5 to the power of three and let's

say 5 to the power of three and let's make sure I ran this so let's do shift

make sure I ran this so let's do shift enter and now we will call our function

enter and now we will call our function and let's hit shift enter and we got 5

and let's hit shift enter and we got 5 to the^ of 3 which is 125 and just one

to the^ of 3 which is 125 and just one last thing to mention is if you have two

last thing to mention is if you have two arguments within your function and you

arguments within your function and you are calling it right here you have to

are calling it right here you have to pass in two arguments you can't just

pass in two arguments you can't just have one so if we have a five right here

have one so if we have a five right here it's going to error out we have to

it's going to error out we have to specify both Arguments for it to work

specify both Arguments for it to work now let's take a look at arbitrary

now let's take a look at arbitrary arguments now arbitrary arguments are

arguments now arbitrary arguments are really interesting because if you don't

really interesting because if you don't know how many arguments you want to pass

know how many arguments you want to pass through if you don't know if it's a one

through if you don't know if it's a one a two or a three you can specify that

a two or a three you can specify that later when you're calling the argument

later when you're calling the argument so you don't have to do it upfront and

so you don't have to do it upfront and know that information ahead of time so

know that information ahead of time so let's define our function so we're going

let's define our function so we're going to say Define and then we're going to

to say Define and then we're going to say number underscore args and we'll do

say number underscore args and we'll do an open parenthesis and a colon now

an open parenthesis and a colon now within our argument right here typically

within our argument right here typically we would just specify here's what our

we would just specify here's what our argument will be it will be number or it

argument will be it will be number or it will be a word right but what we're

will be a word right but what we're going to do is something called an

going to do is something called an arbitrary argument so it's unknown so

arbitrary argument so it's unknown so we're going to put star and then we'll

we're going to put star and then we'll say args now you will see something

say args now you will see something exactly like this typically if you're

exactly like this typically if you're looking at tutorials that'll have star

looking at tutorials that'll have star args in there or if you're looking at

args in there or if you're looking at just a generic piece of code this is

just a generic piece of code this is what it will look like but for us we're

what it will look like but for us we're going to actually put number so again we

going to actually put number so again we have the star and then we have our

have the star and then we have our arbitrary argument right here and then

arbitrary argument right here and then we'll hit enter and we're going to say

we'll hit enter and we're going to say print open parentheses and this is where

print open parentheses and this is where it's going to get a little bit different

it's going to get a little bit different so we're going to say number and then

so we're going to say number and then we're going to do an open bracket and

we're going to do an open bracket and let's say zero and then we'll do that

let's say zero and then we'll do that times and then we'll say number again

times and then we'll say number again with a bracket of one so in a little bit

with a bracket of one so in a little bit once we run this and then we call this

once we run this and then we call this number args function right here we're

number args function right here we're going to need to specify the number zero

going to need to specify the number zero and the number one that's going to be

and the number one that's going to be called so let's go ahead and run this

called so let's go ahead and run this and then we are going to call it and

and then we are going to call it and let's say 5 comma 6 comma 1 2 8 so right

let's say 5 comma 6 comma 1 2 8 so right up here we did not know how many

up here we did not know how many arguments we were going to pass through

arguments we were going to pass through it could be five it could be a thousand

it could be five it could be a thousand we could also call in a tuple and that's

we could also call in a tuple and that's what this is right here we're calling in

what this is right here we're calling in a tupal so what it's going to do now is

a tupal so what it's going to do now is when it calls this number it's going to

when it calls this number it's going to call the very first within that tupal

call the very first within that tupal which will be that five and then it'll

which will be that five and then it'll also call in this number which will be

also call in this number which will be the first position which is the six so

the first position which is the six so let's hit shift enter and it's going to

let's hit shift enter and it's going to multiply these numbers together so 5 * 6

multiply these numbers together so 5 * 6 is equal to 30 now like I just said this

is equal to 30 now like I just said this is a tuple so we don't actually have to

is a tuple so we don't actually have to write out these numbers like we just did

write out these numbers like we just did we can pass through a tuple when we are

we can pass through a tuple when we are actually calling this function let's do

actually calling this function let's do that right up here let's just create um

that right up here let's just create um let's call it argor Tuple and we'll do

let's call it argor Tuple and we'll do open parentheses and we'll do the same

open parentheses and we'll do the same numbers let's just copy it make it

numbers let's just copy it make it easier and now we've created this tupal

easier and now we've created this tupal right here which we can then pass in and

right here which we can then pass in and this is a lot more handy a lot more

this is a lot more handy a lot more specific and this is most likely how

specific and this is most likely how someone would do something like this but

someone would do something like this but let's now create this and now we can

let's now create this and now we can copy args Tuple and pass it through now

copy args Tuple and pass it through now really quickly this is going to fail and

really quickly this is going to fail and I'm doing that on purpose but I want to

I'm doing that on purpose but I want to show you what you need to do in order to

show you what you need to do in order to pass through this

pass through this tupal so right now it's going to say

tupal so right now it's going to say Tuple index is out of range all you have

Tuple index is out of range all you have to do in order to use this is you have

to do in order to use this is you have to specify a star before it just like

to specify a star before it just like you did when you're creating your

you did when you're creating your argument up here you have to put a star

argument up here you have to put a star in front of our Tuple that we just

in front of our Tuple that we just passed through and now let's try running

passed through and now let's try running this and now it works properly now the

this and now it works properly now the last two things that we're going to look

last two things that we're going to look at are keyword arguments and arbitrary

at are keyword arguments and arbitrary keyword arguments there are more things

keyword arguments there are more things that you can learn and do within

that you can learn and do within functions but again I'm just trying to

functions but again I'm just trying to teach you the basics to make sure that

teach you the basics to make sure that you understand how they work so let's go

you understand how they work so let's go right up here and a keyword argument is

right up here and a keyword argument is kind of similar to this right here and

kind of similar to this right here and let's actually copy this and put it

let's actually copy this and put it right down here now a keyword argument

right down here now a keyword argument is very similar in that you're going to

is very similar in that you're going to specify your arguments right here but

specify your arguments right here but what we did up here let me bring this

what we did up here let me bring this down

down when we actually called the function

when we actually called the function what we did was we just put in a five

what we did was we just put in a five and a three and when we did that it

and a three and when we did that it automatically assigned number to five

automatically assigned number to five and power to three and that's totally

and power to three and that's totally fine and you can do that but if you want

fine and you can do that but if you want a little bit more control you can use a

a little bit more control you can use a keyword argument so right here we could

keyword argument so right here we could say power is equal to five and number is

say power is equal to five and number is equal to three so I just switched it

equal to three so I just switched it around right number was assigned to five

around right number was assigned to five and Power was assigned to three but I

and Power was assigned to three but I just switched it to show you how this

just switched it to show you how this might work so let's run both of these

might work so let's run both of these and now it's 3 to the^ of 5 which is

and now it's 3 to the^ of 5 which is 243 so that essentially is a keyword

243 so that essentially is a keyword argument again it just gives you a

argument again it just gives you a little bit more control you don't have

little bit more control you don't have to put them in specific positions like

to put them in specific positions like if you're just calling multiple

if you're just calling multiple arguments now let's come right down here

arguments now let's come right down here we're going to create basically another

we're going to create basically another custom function uh so for this one we're

custom function uh so for this one we're going to write Define number underscore

going to write Define number underscore bar and then we'll do an open

bar and then we'll do an open parenthesis a colon and enter and what

parenthesis a colon and enter and what this one is is this one is a keyword

this one is is this one is a keyword argument or an arbitrary keyword

argument or an arbitrary keyword argument now to specify an arbitrary

argument now to specify an arbitrary argument all we did was a star and then

argument all we did was a star and then we input number but if we're doing a

we input number but if we're doing a keyword argument we actually have to

keyword argument we actually have to have two stars right here so let's start

have two stars right here so let's start taking a look and again if you're doing

taking a look and again if you're doing arbitrary it means we don't really know

arbitrary it means we don't really know how many keyword arguments we want to

how many keyword arguments we want to pass into our function so we're just

pass into our function so we're just going to put star our number and then

going to put star our number and then later within our body of code and when

later within our body of code and when we're calling it we'll be able to

we're calling it we'll be able to specify it and just like the arbitrary

specify it and just like the arbitrary argument before the arbitrary keyword

argument before the arbitrary keyword argument means we really just don't know

argument means we really just don't know how many keyword arguments we're going

how many keyword arguments we're going to need to pass into our function so to

to need to pass into our function so to demonstrate this let's write print do an

demonstrate this let's write print do an open parenthesis and we'll say my oops

open parenthesis and we'll say my oops need to do an

need to do an apostrophe my number is we'll do just

apostrophe my number is we'll do just like that little space and we'll say

like that little space and we'll say plus and this is kind of where it gets a

plus and this is kind of where it gets a little interesting or a little bit more

little interesting or a little bit more tricky so we're going to say is number

tricky so we're going to say is number so This Is Us calling our number and

so This Is Us calling our number and then we're going to do a bracket and

then we're going to do a bracket and then I'm actually going to go to calling

then I'm actually going to go to calling the function it's a little bit backward

the function it's a little bit backward or a little bit different than what you

or a little bit different than what you might think but when we're calling it

might think but when we're calling it what I'm going to do is I'm going to say

what I'm going to do is I'm going to say integer is equal to let's just do some

integer is equal to let's just do some random number now when we're calling

random number now when we're calling that keyword within our body of code

that keyword within our body of code what we're going to do is we're going to

what we're going to do is we're going to actually type out integer just like this

actually type out integer just like this and this looks a little bit different

and this looks a little bit different but what this allows us to do is we can

but what this allows us to do is we can put as many keyword arguments in here as

put as many keyword arguments in here as we want later and I'll show you in just

we want later and I'll show you in just a second but for us we're just creating

a second but for us we're just creating this key and this value when we are

this key and this value when we are calling it within the function so now

calling it within the function so now when we create this and we run

when we create this and we run this oh whoops I forgot this has to be a

this oh whoops I forgot this has to be a string um so let's run this

string um so let's run this again now we will say my number is

again now we will say my number is 2309 then we're we're going to add we'll

2309 then we're we're going to add we'll say plus and this isn't going to look

say plus and this isn't going to look great but we'll say my other number

great but we'll say my other number because this will all be in the same

because this will all be in the same line that's okay my other number and

line that's okay my other number and then we'll say number and we can specify

then we'll say number and we can specify again what we want in there so now we

again what we want in there so now we can go down here to where we're calling

can go down here to where we're calling it we'll put a comma and we'll say

it we'll put a comma and we'll say integer oops

integer oops integer 2 is equal to we'll do a random

integer 2 is equal to we'll do a random number and then we'll put in two right

number and then we'll put in two right here and then we'll add plus right here

here and then we'll add plus right here so we don't error out we'll create this

so we don't error out we'll create this we'll run this and as you can see both

we'll run this and as you can see both numbers were passed through again the

numbers were passed through again the syntax is terrible but now you can see

syntax is terrible but now you can see that you have this arbitrary keyword

that you have this arbitrary keyword argument right here and all we have to

argument right here and all we have to do is put number number and we can pass

do is put number number and we can pass through as many of these arbitrary

through as many of these arbitrary keyword arguments as we want as long as

keyword arguments as we want as long as we just specify within our function when

we just specify within our function when we're calling it so that's all we're

we're calling it so that's all we're going to look at in today's video on

going to look at in today's video on functions there are of course other

functions there are of course other things that you can do within functions

things that you can do within functions and it can get a little bit more

and it can get a little bit more advanced but I wanted to show you the

advanced but I wanted to show you the basics the meat and potatoes of things I

basics the meat and potatoes of things I definitely think you should know in

definitely think you should know in order to get started using functions I

order to get started using functions I hope that you were able to understand

hope that you were able to understand functions better because of this video

functions better because of this video if you did be sure to like And subscribe

if you did be sure to like And subscribe below and I will see you in the next

below and I will see you in the next [Music]

[Music] video hell hello everybody today we're

video hell hello everybody today we're going to be talking about converting

going to be talking about converting data types in Python in this video I'm

data types in Python in this video I'm going to show you how to convert several

going to show you how to convert several different data types including strings

different data types including strings numbers sets tupal and even dictionaries

numbers sets tupal and even dictionaries so let's start off by creating a

so let's start off by creating a variable we'll say numor int is equal to

variable we'll say numor int is equal to 7 and we can check that data type by

7 and we can check that data type by saying type and then inserting our

saying type and then inserting our variable number undor int and that will

variable number undor int and that will tell us that our data type for this

tell us that our data type for this variable is an integer let's go ahead

variable is an integer let's go ahead and create another one we're going to

and create another one we're going to say num underscore string is equal to

say num underscore string is equal to and for this one we'll also do a seven

and for this one we'll also do a seven but let's check the type and we'll do an

but let's check the type and we'll do an open parentheses and we'll say the type

open parentheses and we'll say the type of num string and that one is a string

of num string and that one is a string now let's say we wanted to add those

now let's say we wanted to add those we'll say Num uncore Sum so the sum of

we'll say Num uncore Sum so the sum of numor int plus numor string now when

numor int plus numor string now when we're adding these two values it is not

we're adding these two values it is not going to work it's going to give us an

going to work it's going to give us an error and it's going to say unsupported

error and it's going to say unsupported op brand for INT and string so it cannot

op brand for INT and string so it cannot add both an integer and a string what we

add both an integer and a string what we need to do in order to add these two

need to do in order to add these two numbers is to convert that string into

numbers is to convert that string into an integer so let's go right up here

an integer so let's go right up here let's add another cell and let's say

let's add another cell and let's say numor string undor converted is equal to

numor string undor converted is equal to and we want to convert it into an

and we want to convert it into an integer so all we have to do to convert

integer so all we have to do to convert it into an integer is type int and then

it into an integer is type int and then we're going to say num underscore string

we're going to say num underscore string and that is as easy as it's going to get

and that is as easy as it's going to get all we have to do is say integer with

all we have to do is say integer with our numb string inside of it and then

our numb string inside of it and then it's going to convert it and we can even

it's going to convert it and we can even check it right after by saying type num

check it right after by saying type num string converted and let's run this and

string converted and let's run this and now we can see that it was converted

now we can see that it was converted into an integer so now let's add that

into an integer so now let's add that num string converted right

num string converted right here let's copy and replace that string

here let's copy and replace that string with the string

with the string converted and let's actually print out

converted and let's actually print out that numor sum and it worked properly

that numor sum and it worked properly now we did not specify what type of

now we did not specify what type of value this Num Sum was going to be but

value this Num Sum was going to be but because it was two integers in here it's

because it was two integers in here it's going to automatically apply that data

going to automatically apply that data type of integer to that Num Sum let's go

type of integer to that Num Sum let's go right down here and now let's look at

right down here and now let's look at how we can convert lists sets and tupal

how we can convert lists sets and tupal so now let's say we have a listor type

so now let's say we have a listor type and that's equal to 1 2 3 and we can

and that's equal to 1 2 3 and we can check it again by saying

check it again by saying type and that is a list let's say we

type and that is a list let's say we want to convert it to a tupal it's

want to convert it to a tupal it's fairly easy all we're going to do is

fairly easy all we're going to do is write Tuple say listor type that list

write Tuple say listor type that list uncore type is now going to be a tupal

uncore type is now going to be a tupal and we can check that by saying type and

and we can check that by saying type and wrapping it around this Tuple and it

wrapping it around this Tuple and it shows us that it is converting that list

shows us that it is converting that list into a tupal now we can also convert a

into a tupal now we can also convert a list into a set but it may change the

list into a set but it may change the actual values within it let's check that

actual values within it let's check that out really quickly so let's say we have

out really quickly so let's say we have this list and let's add a few more

this list and let's add a few more values to this just like that now let's

values to this just like that now let's say we want to convert it to a set so

say we want to convert it to a set so we're going to run this and we'll say

we're going to run this and we'll say set of listor type and let's try running

set of listor type and let's try running this and see what the output is so this

this and see what the output is so this is something that you really need to be

is something that you really need to be aware of when you are converting data

aware of when you are converting data types because set does not act the same

types because set does not act the same as a list a set is basically going to

as a list a set is basically going to take the unique values in the list and

take the unique values in the list and convert it to a set and it fundamentally

convert it to a set and it fundamentally changes the data that was in that

changes the data that was in that original list and just to check the data

original list and just to check the data type we can say

type we can say type I'm just doing this for all of them

type I'm just doing this for all of them and as you can see that is now a set now

and as you can see that is now a set now let's go down here and take a look at

let's go down here and take a look at dictionaries now let's say we have a

dictionaries now let's say we have a dictionary called dictionary type and

dictionary called dictionary type and we'll do a squiggly bracket and we'll

we'll do a squiggly bracket and we'll say name name and we'll do a colon and

say name name and we'll do a colon and we'll say Alex then we'll do age and a

we'll say Alex then we'll do age and a colon and we'll say

colon and we'll say 28 and then we'll do

28 and then we'll do hair

hair colon and so really quickly let's take

colon and so really quickly let's take that dictionary type and just confirm

that dictionary type and just confirm that it is a dictionary and it is and

that it is a dictionary and it is and now what we're going to do is take a

now what we're going to do is take a look at all of the items within that

look at all of the items within that dictionary so we're going to do

dictionary so we're going to do dictionary type. items open parenthesis

dictionary type. items open parenthesis and this is going to show us all the

and this is going to show us all the items within it now we can also take

items within it now we can also take this and look at something like the

this and look at something like the values and when we run that these are

values and when we run that these are our values So within our dictionary we

our values So within our dictionary we have items and that's what this is right

have items and that's what this is right here this is one item and then within

here this is one item and then within that we have our values which are right

that we have our values which are right here so Alex 28 and Na and then we have

here so Alex 28 and Na and then we have something called a key and this is the

something called a key and this is the key the name age and hair are all keys

key the name age and hair are all keys and we can look at that by saying dot

and we can look at that by saying dot keys so let's say we want to take all of

keys so let's say we want to take all of the keys and put that into a list what

the keys and put that into a list what we're going to do is we're going to take

we're going to do is we're going to take this right here say

this right here say list we'll do an open parenthesis we'll

list we'll do an open parenthesis we'll type that in right there so it says a

type that in right there so it says a list and we're converting these Keys

list and we're converting these Keys into a list and let's run that and now

into a list and let's run that and now this is a list and let's just check the

this is a list and let's just check the type as well just to confirm

type as well just to confirm and as you can see it was converted

and as you can see it was converted properly into a list and we can do the

properly into a list and we can do the exact same thing with

exact same thing with values and the values can also be

values and the values can also be converted into a list now we can also

converted into a list now we can also convert longer strings that aren't just

convert longer strings that aren't just numbers like we did above in our very

numbers like we did above in our very first example so let's do longcore

first example so let's do longcore string and we'll say I like to party now

string and we'll say I like to party now we're going to take this string and

we're going to take this string and we're going to say list long string so

we're going to say list long string so we're going to convert this string into

we're going to convert this string into a list and let's see what happens so it

a list and let's see what happens so it took every single character in that

took every single character in that string and put it into a list and we

string and put it into a list and we could also do a set as well that one's a

could also do a set as well that one's a lot shorter because it's only looking at

lot shorter because it's only looking at unique values so that is how you convert

unique values so that is how you convert data types in Python thank you guys so

data types in Python thank you guys so much for watching I really appreciate it

much for watching I really appreciate it if you like this video be sure to like

if you like this video be sure to like And subscribe below and I'll see you in

And subscribe below and I'll see you in the next

the next [Music]

video [Music]

[Music] hello everybody today we're going to be

hello everybody today we're going to be working on building a BMI calculator in

working on building a BMI calculator in Python now before we get started I want

Python now before we get started I want to show you this BMI calculator that I

to show you this BMI calculator that I found online and it shows you the basic

found online and it shows you the basic calculation that they use and that's the

calculation that they use and that's the one we're going to use in this video and

one we're going to use in this video and they also have this calculator right

they also have this calculator right down here and some ranges that we can

down here and some ranges that we can use for our calculator as well so for

use for our calculator as well so for reference I weigh about

reference I weigh about 170 I'm about 5 9 let's calculate this

170 I'm about 5 9 let's calculate this so I'm about a

so I'm about a 25.1 BMI which falls into the overweight

25.1 BMI which falls into the overweight category that's unfortunate but we can

category that's unfortunate but we can see exactly how this works and how ours

see exactly how this works and how ours should work when we actually build it so

should work when we actually build it so we're going to kind of reference this

we're going to kind of reference this throughout the video so let's go right

throughout the video so let's go right over here to our BMI calculator we need

over here to our BMI calculator we need to calculate weight and height and then

to calculate weight and height and then run this calculation right here so let's

run this calculation right here so let's go ahead and copy

go ahead and copy this and we're going to put it right

this and we're going to put it right down here

down here here and so now we have our calculation

here and so now we have our calculation so what we need is we need input from a

so what we need is we need input from a user and there is an input function

user and there is an input function within python that we're going to be

within python that we're going to be using so let's actually give me a few

using so let's actually give me a few more cells so the first thing that we

more cells so the first thing that we need to calculate is their weight let's

need to calculate is their weight let's type out weight right here we'll say

type out weight right here we'll say weight is equal to and this is where

weight is equal to and this is where we'll use our input function so we'll

we'll use our input function so we'll say input and when we actually run this

say input and when we actually run this it's just going to give us this blank

it's just going to give us this blank square or a user can input something

square or a user can input something we'll say Alex so this is our output is

we'll say Alex so this is our output is what the actual user input and it does

what the actual user input and it does save it to this variable so if we say

save it to this variable so if we say print weight it will still print out

print weight it will still print out Alex now this is where we want the user

Alex now this is where we want the user to just like we did before where they'll

to just like we did before where they'll input their weight so we want to kind of

input their weight so we want to kind of give them a prompt for this we'll put a

give them a prompt for this we'll put a string in here so I'll do a double quote

string in here so I'll do a double quote and then I'll say

and then I'll say enter your weight in and we're using

enter your weight in and we're using pounds

pounds say pounds colon space so now when we do

say pounds colon space so now when we do this it'll say enter your weight in

this it'll say enter your weight in pounds I'll say 170 and then when we run

pounds I'll say 170 and then when we run this it does store that now let's do

this it does store that now let's do print I should have saved it wait again

print I should have saved it wait again oops now it's only storing the value of

oops now it's only storing the value of 170 it's not actually storing this

170 it's not actually storing this string right here so that's really

string right here so that's really important for when we do our

important for when we do our calculations

calculations later um I'm going to I'm going to save

later um I'm going to I'm going to save this right down here because I'm sure

this right down here because I'm sure I'm going to use that later um so we

I'm going to use that later um so we have that it's working now we need to

have that it's working now we need to also do our height so let's copy this

also do our height so let's copy this and we'll put it right here and we'll do

and we'll put it right here and we'll do height and enter your height in inches

height and enter your height in inches so now for this one if we hit

so now for this one if we hit enter it's actually running let's stop

enter it's actually running let's stop it really quick and interrupt it let's

it really quick and interrupt it let's try running this so it's going to say

try running this so it's going to say enter your weight and pounds that's the

enter your weight and pounds that's the first input say

first input say 170 and then when I hit enter it's going

170 and then when I hit enter it's going to prompt me for that second input and

to prompt me for that second input and so in inches 59 is 69 in and then I can

so in inches 59 is 69 in and then I can hit enter again and now we have both of

hit enter again and now we have both of our inputs now we need this calculation

our inputs now we need this calculation right down here and just like that so

right down here and just like that so now we have weight in pounds time 703

now we have weight in pounds time 703 divided by height in inches by height in

divided by height in inches by height in inches so we actually have weight and

inches so we actually have weight and it's already written in there but I'm

it's already written in there but I'm just going to do like this we'll do

just going to do like this we'll do weight time 73 so that's pounds there

weight time 73 so that's pounds there our weight and pounds * 703 divided by

our weight and pounds * 703 divided by now we have our height in

now we have our height in inches times the height in inches so

inches times the height in inches so this is our calculation right here so

this is our calculation right here so let's do this exact same thing let's run

let's do this exact same thing let's run this and this times of course is not

this and this times of course is not going to work whoops we need to do our

going to work whoops we need to do our star for both of these all right now

star for both of these all right now this is our calculation so let's run

this is our calculation so let's run this so we have

this so we have 170 and that's pounds and inches was 69

170 and that's pounds and inches was 69 hit

hit enter and it says cannot multiply the

enter and it says cannot multiply the sequence of non- integer type of string

sequence of non- integer type of string Ah that's because these are being stored

Ah that's because these are being stored in strings they right down here I do and

in strings they right down here I do and we'll do type of height we run that this

we'll do type of height we run that this is actually a string so we want to

is actually a string so we want to change that because we don't need that

change that because we don't need that anymore

anymore that so we don't want it to be a string

that so we don't want it to be a string we need those to be integers or Floats

we need those to be integers or Floats or really anything besides a string it

or really anything besides a string it just needs to be numerical uh so integer

just needs to be numerical uh so integer float really so let's do integer and

float really so let's do integer and we'll wrap that input in it and we'll do

we'll wrap that input in it and we'll do the same thing for this

the same thing for this one now we have an integer for our

one now we have an integer for our weight an integer for our height so now

weight an integer for our height so now when we're running this calculation it

when we're running this calculation it should work properly let's run this

should work properly let's run this again our pounds are

again our pounds are 70 our height is 69 in

70 our height is 69 in and it's not giving us our output

and it's not giving us our output because we're not printing anything okay

because we're not printing anything okay so I just need to

so I just need to do

do print BMI so let's try this again 170

print BMI so let's try this again 170 69 and there is our BMI 25.1 so it

69 and there is our BMI 25.1 so it worked the exact same as this one so

worked the exact same as this one so they input well we input our height we

they input well we input our height we inputed our or we inputed our weight we

inputed our or we inputed our weight we inputed our height and then it

inputed our height and then it calculated rbmi the next thing that we

calculated rbmi the next thing that we need to do is we need to kind of give

need to do is we need to kind of give the user some context is that good is

the user some context is that good is there BMI in within a good range a bad

there BMI in within a good range a bad range we don't know uh so let's go ahead

range we don't know uh so let's go ahead and I'm going to see if I can copy this

and I'm going to see if I can copy this know if this will work or

know if this will work or not let's go ahead and copy this right

not let's go ahead and copy this right down here perfect so what we now need to

down here perfect so what we now need to do is we need to say okay if the user

do is we need to say okay if the user has given us this input we want to give

has given us this input we want to give them or tell them if they are a normal

them or tell them if they are a normal weight overweight obese severely obese

weight overweight obese severely obese anything like that and we have these

anything like that and we have these ranges so that should help us out quite

ranges so that should help us out quite a bit so let's just write our if

a bit so let's just write our if statement and then we'll include it up

statement and then we'll include it up here but let's go down here and we'll

here but let's go down here and we'll say if and then we'll do BMI and let's

say if and then we'll do BMI and let's just say BMI is greater than zero so if

just say BMI is greater than zero so if it's greater than zero if they had any

it's greater than zero if they had any input where the BMI was not zero which

input where the BMI was not zero which should be every time if they do it

should be every time if they do it properly and they don't you know put a

properly and they don't you know put a string in there or something or type out

string in there or something or type out 40 which maybe we should make a prompt

40 which maybe we should make a prompt for that if that happens then we can say

for that if that happens then we can say if we'll do

if we'll do BMI and now we need to give that first

BMI and now we need to give that first range so this range right here so if

range so this range right here so if it's under 18.5 so we need to do a less

it's under 18.5 so we need to do a less than so if it's less than

than so if it's less than 18.5 and it just says under it doesn't

18.5 and it just says under it doesn't say under or equal to so I'll keep it at

say under or equal to so I'll keep it at 18.5 so if it's under

18.5 so if it's under 18.5 then let's give kind of the output

18.5 then let's give kind of the output we'll say print

we'll say print and the output or the basically the

and the output or the basically the prompt is underweight so we'll just say

prompt is underweight so we'll just say you are

you are under under case underweight and just

under under case underweight and just like that um then we're going to pass

like that um then we're going to pass several ellf statements through here

several ellf statements through here well let's just say else so I guess this

well let's just say else so I guess this would be like if they are if they don't

would be like if they are if they don't input something properly if something

input something properly if something messes up

messes up maybe I we could write something like um

maybe I we could write something like um print

print oops I'm thinking all this through we

oops I'm thinking all this through we can write print

can write print enter valid

enter valid inputs or something like this or we can

inputs or something like this or we can always change that but let's really

always change that but let's really quickly let's run

quickly let's run this okay so I'm not in that range uh

this okay so I'm not in that range uh let's make the next one so then I can be

let's make the next one so then I can be within a certain range

within a certain range oops and we need we should need one one

oops and we need we should need one one more a minimum so we'll say

more a minimum so we'll say LF and

LF and LF these next two are this 24.9 so it's

LF these next two are this 24.9 so it's going to check this one first so if it's

going to check this one first so if it's 18.5 or below 18.5 it's automatically

18.5 or below 18.5 it's automatically going to print this one so this next one

going to print this one so this next one we don't have to do like a range or

we don't have to do like a range or anything we can just say if it's below

anything we can just say if it's below if it's between 25 and 29.9 so this one

if it's between 25 and 29.9 so this one actually should be less than or equal to

actually should be less than or equal to um this one is normal oh whoops

um this one is normal oh whoops 24.9 so this one is

24.9 so this one is 24.9 this one is going to say you are

24.9 this one is going to say you are normal weight so let's run this

normal weight so let's run this now let's see BMI was

now let's see BMI was 25.1 oh guys I'm just messing up here I

25.1 oh guys I'm just messing up here I apologize all right this is the one that

apologize all right this is the one that I was part of so now it's going to be

I was part of so now it's going to be I'm part of the overweight crowd now now

I'm part of the overweight crowd now now let's run this and now our prompt is you

let's run this and now our prompt is you are overweight cuz remember the BMI was

are overweight cuz remember the BMI was saved right here as

saved right here as 25.1 down here if we run through this

25.1 down here if we run through this it's saying no you're not in

it's saying no you're not in oops get rid of that no you're not in

oops get rid of that no you're not in under 18.5 you're not under

under 18.5 you're not under 24.9 if you under

24.9 if you under 29.9 you are overweight so that did work

29.9 you are overweight so that did work properly so that's really good and I

properly so that's really good and I don't think I want this to be our output

don't think I want this to be our output for person because we're going to add

for person because we're going to add this up here it's just going to give us

this up here it's just going to give us the BMI and then the output is going to

the BMI and then the output is going to say you are overweight uh let's make it

say you are overweight uh let's make it a little bit more customized um I'm

a little bit more customized um I'm going to say name is equal to input and

going to say name is equal to input and then we'll say

then we'll say enter your

enter your name um so it'll be enter your name

name um so it'll be enter your name we'll do Alex

we'll do Alex 70 69 there's our BMI now it's going to

70 69 there's our BMI now it's going to run through this logic or it will run

run through this logic or it will run through this logic and just just a

through this logic and just just a second

second when we actually finish this so then we

when we actually finish this so then we have

34.9 and let's do one more oops and then this one's going to

more oops and then this one's going to be for

39.9 so this one was overweight this one is

is obese severely obese so we'll say

obese severely obese so we'll say severely that you spell it really obese

severely that you spell it really obese and then anything that's over that 40

and then anything that's over that 40 and over so if it's not this one

and over so if it's not this one anything else should be S morbidly obese

anything else should be S morbidly obese so actually this lse statement right

so actually this lse statement right here should

here should say uh you

are you are severely obese this is going to say morbidly morbidly obese now I

to say morbidly morbidly obese now I added that name up here because I wanted

added that name up here because I wanted to add that down below actually so we're

to add that down below actually so we're we're going to say uh name plus and then

we're going to say uh name plus and then we'll do like

we'll do like comma you are underweight so it'll be a

comma you are underweight so it'll be a little bit more personalized uh I think

little bit more personalized uh I think it'll I think it'll be a nice touch I

it'll I think it'll be a nice touch I really do we'll do it like this and

really do we'll do it like this and we'll say you and let's go back and do

we'll say you and let's go back and do that to all of

that to all of them and let me see how quickly I can do

thiss oh whoops what I do get rid of that

that name plus u like that geez you guys are

name plus u like that geez you guys are seeing me mess up a h name plus you and

seeing me mess up a h name plus you and then name plus you so now let's run this

then name plus you so now let's run this and now it's a little more personalized

and now it's a little more personalized it says Alex you are overweight so this

it says Alex you are overweight so this is all really good now this is an if

is all really good now this is an if statement um what we had done before I

statement um what we had done before I think is actually what we should put

think is actually what we should put right down here so we'll say l else and

right down here so we'll say l else and then if that doesn't work we'll say what

then if that doesn't work we'll say what do we say enter valid input we'll just

do we say enter valid input we'll just put that um and let let me see if I can

put that um and let let me see if I can test this out don't I don't know if this

test this out don't I don't know if this will error out or if this will even

will error out or if this will even work let me just see if I can mess with

work let me just see if I can mess with it and see if I can get it to work

it and see if I can get it to work actually let's copy this we're going to

actually let's copy this we're going to copy this whole thing we're going to

copy this whole thing we're going to include it right

include it right here and now we have basically our

here and now we have basically our entire calculator so um let's run this

entire calculator so um let's run this enter your name we'll say Alex enter

enter your name we'll say Alex enter your pounds 170 into your inches

your pounds 170 into your inches 69 and then it's going to say

69 and then it's going to say 25.1 Alex you are overweight and that's

25.1 Alex you are overweight and that's perfect we could even go as far as

perfect we could even go as far as adding like some feedback we say you are

adding like some feedback we say you are overweight and then it would be a period

overweight and then it would be a period and we could say um you need to exercise

and we could say um you need to exercise more

more stop sitting and writing so many python

stop sitting and writing so many python tutorials so now if we run this we'll do

tutorials so now if we run this we'll do Alex

Alex 17069 it says Alex you are overweight

17069 it says Alex you are overweight you need to exercise more and stop

you need to exercise more and stop sitting and writing so many python

sitting and writing so many python tutorials period and that's it this is

tutorials period and that's it this is the entire project um you can go a ton

the entire project um you can go a ton farther you can include much more

farther you can include much more complex logic you could even build out a

complex logic you could even build out a UI to create your own you know app just

UI to create your own you know app just like this where it has this input and

like this where it has this input and this UI you can build that out with in

this UI you can build that out with in jupyter notebooks with python um but

jupyter notebooks with python um but that's not really what this tutorial is

that's not really what this tutorial is for this is just to kind of help you um

for this is just to kind of help you um think through some of the logic of

think through some of the logic of creating something like this so you know

creating something like this so you know I hope that this was helpful I hope that

I hope that this was helpful I hope that this was fun I like creating stuff like

this was fun I like creating stuff like this we have two other projects that

this we have two other projects that we're going to do and maybe I'll include

we're going to do and maybe I'll include more but we have two right now that I

more but we have two right now that I have planned um and I hope those those

have planned um and I hope those those are helpful this is probably our easiest

are helpful this is probably our easiest one and they'll get a little bit more

one and they'll get a little bit more difficult in the next projects so I hope

difficult in the next projects so I hope that this was fun I hope that this was

that this was fun I hope that this was helpful and that you can now kind of

helpful and that you can now kind of utilize those python skills that you've

utilize those python skills that you've been working on if you like this video

been working on if you like this video be sure to like And subscribe below and

be sure to like And subscribe below and I'll see you in the next

I'll see you in the next [Music]

[Music] video hello everybody today we're going

video hello everybody today we're going to be creating an automatic file sorder

to be creating an automatic file sorder for your files and file explorer now out

for your files and file explorer now out of all the projects that we've done in

of all the projects that we've done in this series so far I think this one

this series so far I think this one might be the most difficult but I also

might be the most difficult but I also think this one is the most cool because

think this one is the most cool because it has some real life applications so

it has some real life applications so without further Ado let's take a look at

without further Ado let's take a look at some files that we have right down here

some files that we have right down here in my file explorer so I have this

in my file explorer so I have this beautiful picture of Rosie uh right here

beautiful picture of Rosie uh right here this is a PNG file I have a CSV file and

this is a PNG file I have a CSV file and a text file and I want to sort all of

a text file and I want to sort all of them into their own folders depending on

them into their own folders depending on what kind of file it is so if I go right

what kind of file it is so if I go right in here and I click on this one I go to

in here and I click on this one I go to properties I can see that this is a PNG

properties I can see that this is a PNG file um if I go into this one I don't

file um if I go into this one I don't need to but if I go into this one it's a

need to but if I go into this one it's a CSV file and of course this one is a

CSV file and of course this one is a text file so I want three separate

text file so I want three separate folders in here and I want them to

folders in here and I want them to automatically go into those folders

automatically go into those folders without me having to drag and drop and

without me having to drag and drop and going and clicking now we only have four

going and clicking now we only have four files here but imagine if we have

files here but imagine if we have thousands of files

thousands of files how much time that could save us so

how much time that could save us so let's get out of here and let's start

let's get out of here and let's start writing our code so we're going to say

writing our code so we're going to say import OS comma and then we're going to

import OS comma and then we're going to say chut iil now OS obviously stands for

say chut iil now OS obviously stands for operating system shuil uh I don't know

operating system shuil uh I don't know what it actually supposed to stand for

what it actually supposed to stand for but what it will allow us to do is do

but what it will allow us to do is do some highlevel operations on our files

some highlevel operations on our files in file explorer so we're going to go

in file explorer so we're going to go ahead and import those and now that we

ahead and import those and now that we have those imported

have those imported uh something that's going to be very

uh something that's going to be very important for us to have throughout this

important for us to have throughout this whole thing and this is anytime I'm

whole thing and this is anytime I'm working with like directories or

working with like directories or something like this we want to get this

something like this we want to get this path down so I'm going to go ahead and

path down so I'm going to go ahead and copy this

copy this path and we're just going to say path is

path and we're just going to say path is equal to and we'll do this right here so

equal to and we'll do this right here so let's run this and I need to put an R

let's run this and I need to put an R right here to make this a raw text um so

right here to make this a raw text um so when you don't have the r uh it's going

when you don't have the r uh it's going to read in these you know these

to read in these you know these backslashes and these colons and

backslashes and these colons and different stuff if we do R it's just

different stuff if we do R it's just going to read it in as the raw string

going to read it in as the raw string and that's what we want so here's what

and that's what we want so here's what we need to do there there's a few

we need to do there there's a few different things that have to happen

different things that have to happen when we are writing this out one thing

when we are writing this out one thing is is we need to go in here and we need

is is we need to go in here and we need to see this path and we need to see are

to see this path and we need to see are there folders in here already um if not

there folders in here already um if not we need to create a folder so that's one

we need to create a folder so that's one of the first things that we need to do

of the first things that we need to do the next thing that we need is it needs

the next thing that we need is it needs to check each of these files

to check each of these files individually identify what kind of file

individually identify what kind of file it is and then put it into the correct

it is and then put it into the correct folder so we have to create the folder

folder so we have to create the folder then check these and then place it into

then check these and then place it into the correct folder so let's go right out

the correct folder so let's go right out of here so what we're going to start

of here so what we're going to start doing is we're going to start working

doing is we're going to start working with these paths and these directories

with these paths and these directories and some of these things you may never

and some of these things you may never have seen before but that's okay I'll

have seen before but that's okay I'll try to explain it as I go through so the

try to explain it as I go through so the first thing that we're going to write is

first thing that we're going to write is os. list directories uh and what this is

os. list directories uh and what this is actually going to do is show us all the

actually going to do is show us all the files in there we're going to say path

files in there we're going to say path so it should show us all the files

so it should show us all the files within path and so here are our results

within path and so here are our results so we have the data professional results

so we have the data professional results fake text file our image and our other

fake text file our image and our other image so this is actually showing us

image so this is actually showing us what files are in that path and that's

what files are in that path and that's super important because we're probably

super important because we're probably going to have to Loop through this in

going to have to Loop through this in some way later um I wrote this all out

some way later um I wrote this all out before so I kind of remember but I'm

before so I kind of remember but I'm doing this all off the top of my head so

doing this all off the top of my head so I guarantee you throughout this I'll

I guarantee you throughout this I'll make some mistakes but what we now need

make some mistakes but what we now need to do is we need to create folders or

to do is we need to create folders or check if there's a folder and create it

check if there's a folder and create it if it isn't there that's um The Next

if it isn't there that's um The Next Step that we need to take so let's go

Step that we need to take so let's go right down here and we want to check if

right down here and we want to check if this path exists already so if that

this path exists already so if that folder already exists so we're going to

folder already exists so we're going to say

say os. path. exists so this is going to

os. path. exists so this is going to check does this path just like this path

check does this path just like this path up here does it already exist and then

up here does it already exist and then we're going to do an open parenthesis

we're going to do an open parenthesis we'll say path so that's our path now we

we'll say path so that's our path now we need to add a folder name to this um we

need to add a folder name to this um we could hardcode it so we could do plus we

could hardcode it so we could do plus we could say CSV files and that could work

could say CSV files and that could work so it would say does this path already

so it would say does this path already exist and we can try running this and

exist and we can try running this and it's going to say false so this doesn't

it's going to say false so this doesn't already exist but the thing is is we

already exist but the thing is is we need to create three separate path so we

need to create three separate path so we could do this by just hardcoding it in

could do this by just hardcoding it in by saying CSV files image files um and

by saying CSV files image files um and text files or we can just put this all

text files or we can just put this all in a list and loop through it I think

in a list and loop through it I think it's just going to be easier to do that

it's just going to be easier to do that or I don't know visually it's going to

or I don't know visually it's going to be easier so we'll do uh folder undor

be easier so we'll do uh folder undor names and we'll say is equal to and

names and we'll say is equal to and we'll create a list so I think I want to

we'll create a list so I think I want to call it CSV files comma um image files

call it CSV files comma um image files or PNG files whatever you want to write

or PNG files whatever you want to write and then we'll do text

and then we'll do text files do text files and then we can go

files do text files and then we can go right down here um a little for Loop uh

right down here um a little for Loop uh I think what we'll do actually let's

I think what we'll do actually let's write

write folder underscore names um then we can

folder underscore names um then we can put something like uh let's write Loop

put something like uh let's write Loop why not um so a little trick for the for

why not um so a little trick for the for Loop is you going to say four and we'll

Loop is you going to say four and we'll say Loop and and we'll just do a range

say Loop and and we'll just do a range because we want it to basically go

because we want it to basically go through here we don't want it to

through here we don't want it to actually give us these file names we

actually give us these file names we just want it to count Zer one and two so

just want it to count Zer one and two so if we do range from Zer to two zero uh 0

if we do range from Zer to two zero uh 0 one2 that should work if we do um this

one2 that should work if we do um this then when it Loops through it's going to

then when it Loops through it's going to call folder name and say zero which

call folder name and say zero which would be CSV files image files and text

would be CSV files image files and text files um so

files um so let's uh yeah I need a colon let's run

let's uh yeah I need a colon let's run through this really quickly uh shouldn't

through this really quickly uh shouldn't do

do anything but what we can do now is we

anything but what we can do now is we can say okay if this does not exist what

can say okay if this does not exist what we can do is actually create it so we'll

we can do is actually create it so we'll say

say if not so if this does not exist then

if not so if this does not exist then what we're going to do is take

what we're going to do is take this and we'll say

this and we'll say os. make directory and then we'll do

os. make directory and then we'll do just like that um I think it's make

just like that um I think it's make directory S I can't I think that's

directory S I can't I think that's correct um so let's test this out really

correct um so let's test this out really quickly let's see if this

quickly let's see if this works and invalid syntax I I need a

works and invalid syntax I I need a colon okay so I just ran this let's see

colon okay so I just ran this let's see if it did actually make those

if it did actually make those folders let's refresh it and it didn't

folders let's refresh it and it didn't so let's just print this off um so if

so let's just print this off um so if not let's just print let's see does this

not let's just print let's see does this actually

actually work let's do

work let's do if

if okay ah okay so I think I know what

okay ah okay so I think I know what might be happening I think it's giving

might be happening I think it's giving us it actually be let let's check this

us it actually be let let's check this really quick go to python

really quick go to python tutorials oh

tutorials oh no I think it's

no I think it's creating yeah it's creating these Python

creating yeah it's creating these Python tutorial images right here whoops okay

tutorial images right here whoops okay so I just figured it out um let's go

so I just figured it out um let's go back into python tutorials don't take a

back into python tutorials don't take a look at any of those notebooks those are

look at any of those notebooks those are secret um we were creating them in the

secret um we were creating them in the wrong place um and that's because of

wrong place um and that's because of this right here we need a backslash so

this right here we need a backslash so we need to actually include a backslash

we need to actually include a backslash right here here in this path we didn't

right here here in this path we didn't have that um e y scanning string

have that um e y scanning string literal okay so this back slash could

literal okay so this back slash could cause an issue let's see if I can do

cause an issue let's see if I can do forward slashes on all these just stick

forward slashes on all these just stick with me guys I might cut this out I

with me guys I might cut this out I might not we'll see if this is important

might not we'll see if this is important just going to keep talking while we're

just going to keep talking while we're doing it um let's run

doing it um let's run this okay so now that we're doing these

this okay so now that we're doing these forward slashes we're still checking

forward slashes we're still checking let's make sure we can still check those

let's make sure we can still check those files good now when we Loop through this

files good now when we Loop through this I'm not going to well yeah I can print

I'm not going to well yeah I can print it off doesn't matter I'm going to print

it off doesn't matter I'm going to print it and we'll see if that name works and

it and we'll see if that name works and then we're also going to

then we're also going to um uh I said if so if it exists then

um uh I said if so if it exists then make it no no no so if not I think the

make it no no no so if not I think the not did make sense we just weren't sure

not did make sense we just weren't sure we had to do some um checking so if it

we had to do some um checking so if it exists then we're going to create it and

exists then we're going to create it and we'll keep the print in there because it

we'll keep the print in there because it doesn't really matter so it's going to

doesn't really matter so it's going to create the CSV an image but didn't

create the CSV an image but didn't create the text let's see okay let's uh

create the text let's see okay let's uh I don't know why this would work but

I don't know why this would work but let's run it okay so I think I just had

let's run it okay so I think I just had the wrong range so now we have our

the wrong range so now we have our images all through or we have our

images all through or we have our folders all three folders now we need to

folders all three folders now we need to write a script that will read in these

write a script that will read in these and check and see what kind of file it

and check and see what kind of file it is and place it into the correct

is and place it into the correct folder so let's come right down here and

folder so let's come right down here and let's see what we need to do so now I

let's see what we need to do so now I think we need to use this right here um

think we need to use this right here um I think we need to Loop through this to

I think we need to Loop through this to be able to check each one so we need to

be able to check each one so we need to name this so we'll just do um file name

name this so we'll just do um file name is equal to run that so now we have this

is equal to run that so now we have this file name um and what we can do is Loop

file name um and what we can do is Loop through this so let's say let's say for

through this so let's say let's say for file in file name so we're going to Loop

file in file name so we're going to Loop through this now when it goes through it

through this now when it goes through it needs to check the it's going to check

needs to check the it's going to check the file path and in the file path it'll

the file path and in the file path it'll say. txt CSV so let's say um if I think

say. txt CSV so let's say um if I think it should be CSV Let's test it on this

it should be CSV Let's test it on this one but if CSV is

one but if CSV is in file name or actually it's file so if

in file name or actually it's file so if if it's in

if it's in file and not in and oh not not in if

file and not in and oh not not in if it's also not in this I believe because

it's also not in this I believe because we're going to check we're going to

we're going to check we're going to check each of those folders so we're

check each of those folders so we're going to Loop through and it's going to

going to Loop through and it's going to check and see if the CSV so if that

check and see if the CSV so if that string is in the

string is in the file then what we want to do is check

file then what we want to do is check that it's also not in here that's

that it's also not in here that's actually just the folder we also need um

actually just the folder we also need um also we're not doing that for Loop

also we're not doing that for Loop anymore um

anymore um um okay I'm sorry I'm talking this

um okay I'm sorry I'm talking this through I'm figuring it out as I go

through I'm figuring it out as I go because I may have forgotten some of

because I may have forgotten some of this so we're going to say this that's

this so we're going to say this that's the CSV files so we need to check this

the CSV files so we need to check this one um let's do it like this oops okay

one um let's do it like this oops okay so it's going to check to see if CSV

so it's going to check to see if CSV files and I think it needs that in

files and I think it needs that in between it so it's going to say the path

between it so it's going to say the path so there's our path plus slash C SV

so there's our path plus slash C SV files um actually no it needs to be like

files um actually no it needs to be like this CU we're going to check that then I

this CU we're going to check that then I got it all right I figured it out now

got it all right I figured it out now then we're going to check if this file

then we're going to check if this file is in there yeah so that's right so it

is in there yeah so that's right so it says if the

says if the CSV is in the

CSV is in the file um which is right where am I

file um which is right where am I looking oh file name so if it's in that

looking oh file name so if it's in that list of the actual files which is all of

list of the actual files which is all of these if we find CSV in any of these

these if we find CSV in any of these files

files and it's not already in here so it's

and it's not already in here so it's going to say path plus CSV files did I

going to say path plus CSV files did I say files yeah CSV files plus file okay

say files yeah CSV files plus file okay that all looks correct so if it's not in

that all looks correct so if it's not in there we're going to use shuttle. move

there we're going to use shuttle. move now this is how we actually move the

now this is how we actually move the file it gives us the ability to move

file it gives us the ability to move what we want then we'll say move we need

what we want then we'll say move we need to take it from our initial path to our

to take it from our initial path to our new path so we're going to specify we'll

new path so we're going to specify we'll separate by comma we need to spef ify

separate by comma we need to spef ify its original path which it should just

its original path which it should just be

be this without this I think it should be

this without this I think it should be file path because this is where it is

file path because this is where it is now it's in the FI this path with that

now it's in the FI this path with that file name then we need to say we want to

file name then we need to say we want to move it to here that is what we want to

move it to here that is what we want to do

do um yeah so let's check it with just this

um yeah so let's check it with just this one and see if it works okay it ran

one and see if it works okay it ran through it let's go check

through it let's go check aha now that CSV file is gone perfect

aha now that CSV file is gone perfect that is exactly what we want it to

that is exactly what we want it to happen now we can just recreate this

happen now we can just recreate this for um for both our PNG files our image

for um for both our PNG files our image files and our text files so we'll say LF

files and our text files so we'll say LF and

and LF and let's do

LF and let's do PNG then we'll do image

PNG then we'll do image files and image files because again

files and image files because again we're just doing the exact same thing I

we're just doing the exact same thing I can do text files the next one's going

can do text files the next one's going to be text files text files so this

to be text files text files so this one's going to check for

one's going to check for txt now do we need anything else um

txt now do we need anything else um we'll just say else and we'll print off

we'll just say else and we'll print off print this file type is not included or

print this file type is not included or or if there's multiple files we'll say

or if there's multiple files we'll say there are files in this

there are files in this path that were're not

path that were're not moved okay so if we run through this

moved okay so if we run through this it's going to catch our CSV catch our

it's going to catch our CSV catch our PNG catch our text and if not it'll say

PNG catch our text and if not it'll say there are files in this path that we're

there are files in this path that we're not moved exclamation point all right

not moved exclamation point all right now let's run through

now let's run through this

this uh uh that's because if LF LF L

uh uh that's because if LF LF L if and then it's going to this lse

if and then it's going to this lse statement uh I don't know let's let's

statement uh I don't know let's let's Circle back around to that in a second

Circle back around to that in a second all of them were moved properly that's

all of them were moved properly that's really

really good really quickly I I'll I'll check

good really quickly I I'll I'll check and see I just don't I'm G to take that

and see I just don't I'm G to take that out for now so I'm just going to run it

out for now so I'm just going to run it um I'm we may or may not go back to that

um I'm we may or may not go back to that but let's check and see if everything

but let's check and see if everything worked properly so let's go into the CSV

worked properly so let's go into the CSV file and we have our CSV file let go

file and we have our CSV file let go into our image files and we have our

into our image files and we have our images and let's go into our text file

images and let's go into our text file and there are our text files now is

and there are our text files now is there anything else that we need to do I

there anything else that we need to do I don't believe so but what I can do is I

don't believe so but what I can do is I can take all

can take all this I can include it in

this I can include it in here and I'm going

here and I'm going to basically restart

it just to see if it works properly from scratch right I just want to make sure

scratch right I just want to make sure that I didn't miss it anything and we'll

that I didn't miss it anything and we'll delete

delete these so we have our I'm just going to

these so we have our I'm just going to rerun everything we we

rerun everything we we imported we created our path these are

imported we created our path these are our file names and then when we run this

our file names and then when we run this it should take our folder names check

it should take our folder names check through them if they aren't already

through them if they aren't already created it's going to create it don't

created it's going to create it don't need it to print so let's get rid of

need it to print so let's get rid of that then for the file within our file

that then for the file within our file names and it check it it checks each one

names and it check it it checks each one we check if there's a CSV and if it's

we check if there's a CSV and if it's already already in that file if it's

already already in that file if it's already in that folder I mean if it's in

already in that folder I mean if it's in that folder then it doesn't do anything

that folder then it doesn't do anything but if it isn't so and not it's not in

but if it isn't so and not it's not in there it is going to move it to that

there it is going to move it to that location so it's going to check CSV PNG

location so it's going to check CSV PNG and text I think everything should work

and text I think everything should work properly let's run

properly let's run this and it looks like it's working good

this and it looks like it's working good good good and perfect it worked exactly

good good and perfect it worked exactly how I had hoped um that's great so

how I had hoped um that's great so this is the automatic file sorder in

this is the automatic file sorder in file explorer project uh you can go even

file explorer project uh you can go even a step further so I had to come in here

a step further so I had to come in here and manually run this you can go a step

and manually run this you can go a step further and put a timer on this where it

further and put a timer on this where it automatically does this maybe every hour

automatically does this maybe every hour every day every 30 minutes you can run

every day every 30 minutes you can run this in your background especially if

this in your background especially if you create um like a an execution for

you create um like a an execution for this you can run this in your background

this you can run this in your background um if you are curious on how to do that

um if you are curious on how to do that I think I did something something

I think I did something something similar to that in my web scraping

similar to that in my web scraping project um my Amazon web scraping

project um my Amazon web scraping project if you want to go check that one

project if you want to go check that one out but we're not going to do it in this

out but we're not going to do it in this project this is all I wanted to show you

project this is all I wanted to show you how to do so I hope that this was

how to do so I hope that this was helpful I hope that this project was you

helpful I hope that this project was you know interesting and that you liked it I

know interesting and that you liked it I hope that you learned something and so

hope that you learned something and so if you did be sure to like And subscribe

if you did be sure to like And subscribe below and I will see you in the next

below and I will see you in the next video what's going on everybody welcome

video what's going on everybody welcome back to another video today we're going

back to another video today we're going to be starting our python web scraping

to be starting our python web scraping tutorial series now this is more of a

tutorial series now this is more of a continuation of the Python tutorial

continuation of the Python tutorial series series but because we're going to

series series but because we're going to be focusing on web scraping for three or

be focusing on web scraping for three or four videos I wanted to just make it its

four videos I wanted to just make it its own little minseries in this series I'm

own little minseries in this series I'm going to show you the basics of web

going to show you the basics of web scraping how to actually look at HTML

scraping how to actually look at HTML how to inspect a web page how to pull

how to inspect a web page how to pull that data in and then even put it into a

that data in and then even put it into a CSV file so you can save it and use it

CSV file so you can save it and use it now in this series we're just covering

now in this series we're just covering the basics which is a fantastic place to

the basics which is a fantastic place to start but in future series I'll be going

start but in future series I'll be going into some of the more advanced web

into some of the more advanced web scraping topics as well so without

scraping topics as well so without further Ado let sh up on my screen and

further Ado let sh up on my screen and get started with web scraping now the

get started with web scraping now the first thing that we need to learn is

first thing that we need to learn is HTML HTML stands for hypertext markup

HTML HTML stands for hypertext markup language and it's used to describe all

language and it's used to describe all of the elements on a web page now when

of the elements on a web page now when we actually go to a website and start

we actually go to a website and start pulling data and information we need to

pulling data and information we need to know HTML so we can specify exactly what

know HTML so we can specify exactly what we want to take off of that website so

we want to take off of that website so that's where HTML comes in and we're

that's where HTML comes in and we're going to look at the basics

going to look at the basics understanding just the basic structure

understanding just the basic structure of HTML then we'll go look at a real

of HTML then we'll go look at a real website and you'll kind of see that's a

website and you'll kind of see that's a little bit more difficult than what we

little bit more difficult than what we just have right here but this is the

just have right here but this is the basic building blocks to get to what the

basic building blocks to get to what the HTML actually looks like on a website

HTML actually looks like on a website now this is basically what HTML looks

now this is basically what HTML looks like we have these angled brackets with

like we have these angled brackets with things like HTML head title body and

things like HTML head title body and then you'll notice that at the end we'll

then you'll notice that at the end we'll have a body and then we'll have a body

have a body and then we'll have a body at the bottom this forward SL body

at the bottom this forward SL body denotes that this is the end of the body

denotes that this is the end of the body section in HTML so everything inside of

section in HTML so everything inside of this is within this body so there is

this is within this body so there is this hierarchy within HTML we have HTML

this hierarchy within HTML we have HTML and HTML at the bottom which

and HTML at the bottom which encapsulates all the HTML on the website

encapsulates all the HTML on the website then we have things like head and head

then we have things like head and head body and body now Within These sections

body and body now Within These sections we usually have things like classes tags

we usually have things like classes tags attributes text and all these other

attributes text and all these other things things that we'll get to in

things things that we'll get to in different lessons but one of the easiest

different lessons but one of the easiest ones to notice and look at are tags

ones to notice and look at are tags things like a P tag or a title tag now

things like a P tag or a title tag now Within These tags because this is a

Within These tags because this is a super simple example we have these

super simple example we have these strings here my first web page page and

strings here my first web page page and this is what's called a variable string

this is what's called a variable string and this is actual text that we could

and this is actual text that we could take out of this web page now that you

take out of this web page now that you understand the super basics of HTML

understand the super basics of HTML let's actually go to our website and I'm

let's actually go to our website and I'm going to have a link down below but it's

going to have a link down below but it's going to be this one right here this is

going to be this one right here this is basically just a website that you can

basically just a website that you can you know practice web scraping on it's

you know practice web scraping on it's called scrape the

called scrape the site.com and what we're going to do is

site.com and what we're going to do is look at the HTML behind this web page

look at the HTML behind this web page and you can do this on any website that

and you can do this on any website that you go on so we're going to right click

you go on so we're going to right click we're going to go down to inspect

we're going to go down to inspect now right off the bat this looks a lot

now right off the bat this looks a lot more complicated and a lot more complex

more complicated and a lot more complex than the very simple illustration that

than the very simple illustration that we were looking at but let's kind of

we were looking at but let's kind of roll this up just a little bit you'll

roll this up just a little bit you'll notice we have HTML and HTML at the

notice we have HTML and HTML at the bottom we have a head and there is the

bottom we have a head and there is the end of the head and then a body and the

end of the head and then a body and the end of the body so in a super simple

end of the body so in a super simple sense it is similar but just the

sense it is similar but just the information that's within it is a lot

information that's within it is a lot more difficult now if we look at this

more difficult now if we look at this title right here this is our title tag

title right here this is our title tag if we click this little arrow this is

if we click this little arrow this is our dropdown you'll notice that here we

our dropdown you'll notice that here we have the string hockey teams forms

have the string hockey teams forms searching imp pagination now let's say

searching imp pagination now let's say we didn't know we didn't want to click

we didn't know we didn't want to click on that and go find it there's something

on that and go find it there's something that's super helpful within this

that's super helpful within this inspection page that you can click on

inspection page that you can click on right here it says select an element in

right here it says select an element in the page to inspect it so we're going to

the page to inspect it so we're going to click on that and as we go through our

click on that and as we go through our page and let's click on this title it's

page and let's click on this title it's going to take us to exactly where this

going to take us to exactly where this is in our our HTML this is extremely

is in our our HTML this is extremely helpful extremely useful for example

helpful extremely useful for example let's say the data I want is down here I

let's say the data I want is down here I want to take in the Boston Bruins I can

want to take in the Boston Bruins I can click on it and it's going to take me to

click on it and it's going to take me to where that is exactly in the HTML this

where that is exactly in the HTML this is where we can start writing our web

is where we can start writing our web scraping script to specify okay I'm

scraping script to specify okay I'm looking for a TR tag I'm looking for a

looking for a TR tag I'm looking for a TD tag I'm looking for the class called

TD tag I'm looking for the class called team this is all information and things

team this is all information and things that we can use to specify exactly what

that we can use to specify exactly what we want to pull out of our web page now

we want to pull out of our web page now there are other things that didn't

there are other things that didn't really look at as well in just our

really look at as well in just our simple illustration let's come right

simple illustration let's come right over here there's things like HRS now

over here there's things like HRS now these are hyperlinks so if we went and

these are hyperlinks so if we went and then clicked on this this is just

then clicked on this this is just regular text but inside of it is this

regular text but inside of it is this hyperlink where if we clicked on it it

hyperlink where if we clicked on it it would take us to another website and

would take us to another website and typically that's denoted by this hre

typically that's denoted by this hre right here then you'll typically see

right here then you'll typically see things like a P tag which usually stands

things like a P tag which usually stands for a paragraph now the last thing that

for a paragraph now the last thing that I want to show you while we're here and

I want to show you while we're here and we're going to learn a lot more in the

we're going to learn a lot more in the next several lessons

next several lessons but if we come right down here there is

but if we come right down here there is this actual entire table here and let's

this actual entire table here and let's try to find this table and I'm having

try to find this table and I'm having trouble selecting the entire thing but

trouble selecting the entire thing but let's select this team name and if we

let's select this team name and if we look at this team name you can see that

look at this team name you can see that this is encapsulating the table this

this is encapsulating the table this table tag now these are super helpful

table tag now these are super helpful because it takes in the entire table now

because it takes in the entire table now if we wrap this up and we look just at

if we wrap this up and we look just at this it says class table and then we

this it says class table and then we have the end of this table tag now when

have the end of this table tag now when we open it it's going to have all of

we open it it's going to have all of this information so as you can see as

this information so as you can see as I'm highlighting over it we have these

I'm highlighting over it we have these th tags and we have these TD tags and

th tags and we have these TD tags and even these TR tags which is the

even these TR tags which is the individual data and this is something

individual data and this is something that we'll look at when we're actually

that we'll look at when we're actually scraping all of the data from this table

scraping all of the data from this table in a future lesson so this is how we can

in a future lesson so this is how we can use HTML how we can inspect the web page

use HTML how we can inspect the web page and see exactly what's going on kind of

and see exactly what's going on kind of under the hood and then in future

under the hood and then in future lessons we'll see how we can use this

lessons we'll see how we can use this HTML to specify exactly what data we

HTML to specify exactly what data we want to pull out thank you guys so much

want to pull out thank you guys so much for watching if you like this video be

for watching if you like this video be be sure to like And subscribe below I

be sure to like And subscribe below I will see you in the next

will see you in the next [Music]

[Music] lesson hello everybody in this lesson

lesson hello everybody in this lesson we're going to be taking a look at

we're going to be taking a look at beautiful soup and requests now these

beautiful soup and requests now these packages in Python are really useful

packages in Python are really useful these are the two main ones that I use

these are the two main ones that I use when I was first starting out with web

when I was first starting out with web scraping it can get a lot of what you

scraping it can get a lot of what you want done in order to get that

want done in order to get that information out now of course there are

information out now of course there are other packages that you can use that may

other packages that you can use that may be a little bit more advanced but again

be a little bit more advanced but again this is just the beginner Series in a

this is just the beginner Series in a future series we'll look at other

future series we'll look at other packages as well that have some more

packages as well that have some more advanced functionality so what we're

advanced functionality so what we're going to be doing is we're going to

going to be doing is we're going to import these packages and then we're

import these packages and then we're going to get all of the HTML from our

going to get all of the HTML from our website and make sure that it's in a

website and make sure that it's in a usable State and then in the next lesson

usable State and then in the next lesson we're going to kind of query around in

we're going to kind of query around in the HTML kind of pick and choose exactly

the HTML kind of pick and choose exactly what we want we look at things like tags

what we want we look at things like tags variable strings classes attributes and

variable strings classes attributes and more so let's get started by importing

more so let's get started by importing our packages what we're going to say is

our packages what we're going to say is from bs4 this is the module that we're

from bs4 this is the module that we're taking it from we're going to say import

taking it from we're going to say import and then we'll do

and then we'll do beautiful soup then we're going to come

beautiful soup then we're going to come down and we're going to say import

down and we're going to say import requests now let's go ahead and run this

requests now let's go ahead and run this I'm going hit shift enter and it works

I'm going hit shift enter and it works well for me now if this does not work

well for me now if this does not work for you you may potentially need to

for you you may potentially need to actually install bs4 so you may have to

actually install bs4 so you may have to go to your terminal window and say pip

go to your terminal window and say pip install BS 4 I'll just let you Google

install BS 4 I'll just let you Google how to do that if you need to do that

how to do that if you need to do that cuz it's pretty easy but if you're using

cuz it's pretty easy but if you're using Jupiter notebooks through Anaconda like

Jupiter notebooks through Anaconda like how we set it up at the beginning of

how we set it up at the beginning of this python series then you should be

this python series then you should be totally fine it should be there for you

totally fine it should be there for you the next thing that we need to do is

the next thing that we need to do is specify where we're taking this HTML

specify where we're taking this HTML from so what we need to actually do is

from so what we need to actually do is come right over here to our web page and

come right over here to our web page and we need to get the URL so we're going to

we need to get the URL so we're going to go here we're going to copy this URL and

go here we're going to copy this URL and I'm just going to put it right here for

I'm just going to put it right here for a second and what we're going to do is

a second and what we're going to do is we're going to be using this URL quite a

we're going to be using this URL quite a bit so we just want to assign it to a

bit so we just want to assign it to a variable so just say URL is equal to and

variable so just say URL is equal to and then we'll put it right in here now we

then we'll put it right in here now we can get rid of that so now this is our

can get rid of that so now this is our URL going forward this is where we're

URL going forward this is where we're going to be pulling data from let's go

going to be pulling data from let's go ahead and run this now we're going to

ahead and run this now we're going to use requests and what we're going to do

use requests and what we're going to do is we're going to say

is we're going to say requests.get and then we're going to put

requests.get and then we're going to put in url now this get function is going to

in url now this get function is going to use the request Library it's going to

use the request Library it's going to send a get request to that URL and it's

send a get request to that URL and it's going to return a response object let's

going to return a response object let's go ahead and run

go ahead and run this as you can see here I got a

this as you can see here I got a response of 200 if you got something

response of 200 if you got something like a 204 or a 400 or 401 or 404 all

like a 204 or a 400 or 401 or 404 all these things are potentially bad

these things are potentially bad something like a 204 would mean there

something like a 204 would mean there was no content in the actual web page

was no content in the actual web page 400 means a bad request so it was

400 means a bad request so it was invalid the server couldn't process it

invalid the server couldn't process it and you don't get any response if you

and you don't get any response if you got a 404 that might be one that you're

got a 404 that might be one that you're familiar with that's an error that means

familiar with that's an error that means the server cannot be found the next

the server cannot be found the next thing that we're going to do is take the

thing that we're going to do is take the HTML now if you remember we come right

HTML now if you remember we come right back here and we inspect this we have

back here and we inspect this we have all this HTML right here now on this web

all this HTML right here now on this web page specifically right now it's

page specifically right now it's completely static it's not a bunch of

completely static it's not a bunch of moving stuff or anything like that

moving stuff or anything like that usually when you're looking at HTML if

usually when you're looking at HTML if you're looking at something like Amazon

you're looking at something like Amazon and those web pages can update but when

and those web pages can update but when you actually pull that into python

you actually pull that into python you're basically getting a snapshot of

you're basically getting a snapshot of the HTML at that time so what we're

the HTML at that time so what we're going to do is bring in all of this HTML

going to do is bring in all of this HTML which is our snapshot of our website and

which is our snapshot of our website and then we can take a look at it so we're

then we can take a look at it so we're going to come right down here and now

going to come right down here and now we're going to say beautiful soup so now

we're going to say beautiful soup so now we'll use the beautiful soup package or

we'll use the beautiful soup package or Library so we need to say beautiful soup

Library so we need to say beautiful soup and we're going do an open parenthesis

and we're going do an open parenthesis we're going to do two things there's two

we're going to do two things there's two parameters that we need to put in here

parameters that we need to put in here first we need to put in this get request

first we need to put in this get request we actually need to name this and we'll

we actually need to name this and we'll call this page we'll say page is equal

call this page we'll say page is equal to and let's run this and now we're

to and let's run this and now we're going to put that page in here and what

going to put that page in here and what we're going to say is do text so the

we're going to say is do text so the page is what's sending that request and

page is what's sending that request and then the text is what's retrieving the

then the text is what's retrieving the actual raw HTML that we're going to be

actual raw HTML that we're going to be using then we're going to put a comma

using then we're going to put a comma here and what we need to specify is how

here and what we need to specify is how we're going to parse this information

we're going to parse this information now this is an HTML so what we're going

now this is an HTML so what we're going to do is HTML just like this this is a

to do is HTML just like this this is a standard this is already built into to

standard this is already built into to this Library so we don't need to go any

this Library so we don't need to go any further but it's basically going to

further but it's basically going to parse the information in an HTML format

parse the information in an HTML format let's go ahead and run this let's see

let's go ahead and run this let's see what we get and as you can see we have a

what we get and as you can see we have a lot of information and as we scroll down

lot of information and as we scroll down I'll try to point out some things that

I'll try to point out some things that we've already looked at in previous

we've already looked at in previous lessons

lessons um something like this th tag that

um something like this th tag that should be very similar that's the title

should be very similar that's the title then we have these TD tags and then of

then we have these TD tags and then of course if we scroll down even further

course if we scroll down even further we'll have things like ATR tag so these

we'll have things like ATR tag so these are all things that we looked at in that

are all things that we looked at in that first lesson when learning about HTML

first lesson when learning about HTML now again we want to assign this to a

now again we want to assign this to a variable so we're going to say soup

variable so we're going to say soup that's going to say equal to this

that's going to say equal to this information information right here now

information information right here now I'm not going to go into all the history

I'm not going to go into all the history behind beautiful soup what I will say is

behind beautiful soup what I will say is the guy who created this beautiful soup

the guy who created this beautiful soup Library uh what he said was is that it

Library uh what he said was is that it takes this really messy HTML or XML

takes this really messy HTML or XML which you can also use it for and makes

which you can also use it for and makes it into this kind of beautiful soup so I

it into this kind of beautiful soup so I just thought that was kind of funny uh

just thought that was kind of funny uh but that's why we're calling it soup

but that's why we're calling it soup right here and we're going to go ahead

right here and we're going to go ahead and run this and we'll come right down

and run this and we'll come right down here and we'll say print soup and let's

here and we'll say print soup and let's run it and now we have everything in

run it and now we have everything in here so we have our HTML L our head we

here so we have our HTML L our head we have some HR and some links in here

have some HR and some links in here let's scroll down a little bit more and

let's scroll down a little bit more and then we have our body right there and of

then we have our body right there and of course we have a bunch of information in

course we have a bunch of information in here now in the next lesson what we're

here now in the next lesson what we're going to be doing is learning how to

going to be doing is learning how to kind of query all of this to take

kind of query all of this to take specific information out and basically

specific information out and basically understand a lot of what's going on in

understand a lot of what's going on in this HTML to make sure we can actually

this HTML to make sure we can actually get what we need now if this looks

get what we need now if this looks really kind of messy to you and it just

really kind of messy to you and it just doesn't make a lot of sense there is one

doesn't make a lot of sense there is one more thing that I'm going to show you

more thing that I'm going to show you and we'll come right down here so we'll

and we'll come right down here so we'll say soup. pry and if you've ever used a

say soup. pry and if you've ever used a different type of programming languages

different type of programming languages uh pry is very common in a lot of them

uh pry is very common in a lot of them where it'll just make it a little bit

where it'll just make it a little bit more easy to visualize and see uh you'll

more easy to visualize and see uh you'll notice that it kind of has this

notice that it kind of has this hierarchy built in whereas if we scroll

hierarchy built in whereas if we scroll up there's no hierarchy built in it's

up there's no hierarchy built in it's all just down this left hand side so if

all just down this left hand side so if you kind of want to view it and just

you kind of want to view it and just kind of visually see the differences

kind of visually see the differences this does help a lot but it doesn't

this does help a lot but it doesn't actually help a lot when you're you know

actually help a lot when you're you know querying it or using you know find and

querying it or using you know find and find all which is what we're going to

find all which is what we're going to look at in the next lesson so that is

look at in the next lesson so that is our lesson on beautiful soup and

our lesson on beautiful soup and requests in the next two lessons we're

requests in the next two lessons we're going to be looking at find and find all

going to be looking at find and find all as well as really diving into things

as well as really diving into things like variable strings and tags and

like variable strings and tags and classes and all those things and then in

classes and all those things and then in the last lesson we're going to do kind

the last lesson we're going to do kind of this mini project where we try to get

of this mini project where we try to get all the data from this web page that

all the data from this web page that we've been using from that table and put

we've been using from that table and put it into a panda's data frame so thank

it into a panda's data frame so thank you guys so much for watching I really

you guys so much for watching I really appreciate it if you like this video be

appreciate it if you like this video be sure to like And subscribe below and I

sure to like And subscribe below and I will see you in the next

will see you in the next [Music]

[Music] lesson hello everybody in this lesson

lesson hello everybody in this lesson we're going to be taking a look at find

we're going to be taking a look at find and find all really we're going to be

and find all really we're going to be looking at a ton of different things in

looking at a ton of different things in this lesson this is where we really

this lesson this is where we really start digging in seeing how we can

start digging in seeing how we can extract specific information from our

extract specific information from our web page but in order to do that let's

web page but in order to do that let's set everything up where we actually

set everything up where we actually bring in the HTML like we did in the

bring in the HTML like we did in the last lesson and we're just going to

last lesson and we're just going to write all this out one more time just

write all this out one more time just for practice if nothing else and then

for practice if nothing else and then we'll get into actually getting that

we'll get into actually getting that information from the HTML so we're going

information from the HTML so we're going to start by saying from bs4 import

to start by saying from bs4 import beautiful soup there we go and import

beautiful soup there we go and import requests we'll go ahead and run this

requests we'll go ahead and run this then we're going to come up here grab

then we're going to come up here grab our HTML or sorry our URL so we'll say

our HTML or sorry our URL so we'll say URL is equal to and we'll have that

URL is equal to and we'll have that right here now we need to say page is

right here now we need to say page is equal to and then we'll do

equal to and then we'll do requests.get and then we'll put in our

requests.get and then we'll put in our URL right here and we're going to come

URL right here and we're going to come over here and run this and lastly we

over here and run this and lastly we need to say soup so we'll say soup is

need to say soup so we'll say soup is equal to beautiful soup there we go and

equal to beautiful soup there we go and then within our parentheses we need to

then within our parentheses we need to specify the page. text because we need

specify the page. text because we need that and our parser which is

that and our parser which is HTML

HTML and there we go and let's go ahead and

and there we go and let's go ahead and run this let's print it out make sure

run this let's print it out make sure it's

it's working and there we go so we have our

working and there we go so we have our soup right here all this should look

soup right here all this should look really similar to uh our last lesson and

really similar to uh our last lesson and so now we've brought in our HTML from

so now we've brought in our HTML from our page we have a lot a lot a lot of

our page we have a lot a lot a lot of information in here now really quickly

information in here now really quickly let's come over and let's inspect our

let's come over and let's inspect our web

web page now in here we have a ton of

page now in here we have a ton of information right we have bunch of

information right we have bunch of different tags and classes and all these

different tags and classes and all these other things but how do we actually use

other things but how do we actually use these well that's where the find and

these well that's where the find and find all is going to come into play and

find all is going to come into play and they're pretty similar and you'll see

they're pretty similar and you'll see that in just a little bit but let's say

that in just a little bit but let's say we want to take uh one of these tags and

we want to take uh one of these tags and let's come down let's say we just want

let's come down let's say we just want to take this div tag now there's going

to take this div tag now there's going to be a lot of different div tags in our

to be a lot of different div tags in our HTML but let's just come right here

HTML but let's just come right here let's go down and let's say

let's go down and let's say we're going to call Soup we're going to

we're going to call Soup we're going to say soup that's all of our information

say soup that's all of our information we're going to say do find now within

we're going to say do find now within our parentheses we can specify a lot of

our parentheses we can specify a lot of different things but we're going to keep

different things but we're going to keep it really simple right now we're just

it really simple right now we're just going to say

going to say di let's go ahead and run this what this

di let's go ahead and run this what this is going to bring up is the very first

is going to bring up is the very first div tag in our HTML and that's going to

div tag in our HTML and that's going to be this information right here now let's

be this information right here now let's copy this and we're going to do the

copy this and we're going to do the exact same thing except we're going to

exact same thing except we're going to say find underscore all now let's run

say find underscore all now let's run this now we're going to have a ton more

this now we're going to have a ton more information really all find and find all

information really all find and find all do is that they find the information now

do is that they find the information now find is only going to find the first

find is only going to find the first response in our HTML Le that's the div

response in our HTML Le that's the div class container let's go back up to the

class container let's go back up to the top that's our div class container but

top that's our div class container but find all is going to find all of them so

find all is going to find all of them so it'll put it in this list for you so

it'll put it in this list for you so it's going to have this first one and it

it's going to have this first one and it goes down to uh this SL div which should

goes down to uh this SL div which should be right here and then we have a comma

be right here and then we have a comma which separates our next div tag so that

which separates our next div tag so that is how we can use it now what if we want

is how we can use it now what if we want to specify one of these div tags we

to specify one of these div tags we pulled in a ton of them but we want to

pulled in a ton of them but we want to just look for one of them well this is

just look for one of them well this is something where the class comes in handy

something where the class comes in handy because right now we have class is equal

because right now we have class is equal to container classes equal to co

to container classes equal to co md-12 I don't know what these are at the

md-12 I don't know what these are at the off the top of my head but um usually

off the top of my head but um usually they'll be somewhat unique and we can

they'll be somewhat unique and we can use these to help us specify what we're

use these to help us specify what we're looking for for example just kind of

looking for for example just kind of glancing of this we could also use this

glancing of this we could also use this a tag if we wanted to look at this so we

a tag if we wanted to look at this so we could say oh we're looking for uh these

could say oh we're looking for uh these hrefs so we have an hre here and this

hrefs so we have an hre here and this right down here we have this hre as well

right down here we have this hre as well which again uh if you remember from

which again uh if you remember from previous lesson that stands for a

previous lesson that stands for a hyperlink now something like the class

hyperlink now something like the class or the href um or these IDs these are

or the href um or these IDs these are all attributes so we can specify or kind

all attributes so we can specify or kind of filter Down based off of these now

of filter Down based off of these now let's try it so what we can do is we can

let's try it so what we can do is we can do class first and this is kind of the

do class first and this is kind of the default uh within something like find

default uh within something like find all is you can even do class underscore

all is you can even do class underscore we can come right back up we have this

we can come right back up we have this div and then here's our class so again

div and then here's our class so again we have to have the div and the class if

we have to have the div and the class if we took this a tag this is an a tag

we took this a tag this is an a tag which would go right here with the class

which would go right here with the class of something like navlink or something

of something like navlink or something like navlink again down here we need to

like navlink again down here we need to specify that more but we have our div so

specify that more but we have our div so we'll say CL Cole

we'll say CL Cole md12 right here and let's go ahead and

md12 right here and let's go ahead and run this and now it's going to pull in

run this and now it's going to pull in just that information now we're still

just that information now we're still getting a list because we have multiple

getting a list because we have multiple of these so this div class uh Cole md-12

of these so this div class uh Cole md-12 doesn't just happen once if we scroll

doesn't just happen once if we scroll down we'll see it multiple times

down we'll see it multiple times something like right here uh or actually

something like right here uh or actually let me see right here so here's this

let me see right here so here's this comma then here's our next one so we

comma then here's our next one so we have two of these uh div tags with a

have two of these uh div tags with a class of coal- md-12 and in each of

class of coal- md-12 and in each of these we have different information this

these we have different information this looks like a paragraph with this P tag

looks like a paragraph with this P tag right here and let's scroll back up uh

right here and let's scroll back up uh so I also think we should try out doing

so I also think we should try out doing something like this P tag typically

something like this P tag typically these P tags stand for paragraphs or

these P tags stand for paragraphs or they have text information in them let's

they have text information in them let's try to P tag really quickly and let's

try to P tag really quickly and let's just see what we get and let's run this

just see what we get and let's run this and it looks like we get multiple P tags

and it looks like we get multiple P tags now if we come back here you can see

now if we come back here you can see that there's this information and it's

that there's this information and it's this information that we're pulling in

this information that we're pulling in and I'm just you know noticing that from

and I'm just you know noticing that from right here and then we have this

right here and then we have this information right here and it looks like

information right here and it looks like there's one more which is this href

there's one more which is this href which looks like this open source so

which looks like this open source so data via and then that uh hyperlink or

data via and then that uh hyperlink or that link right there so we have three

that link right there so we have three different P tags now just to verify and

different P tags now just to verify and make sure that that's correct what we

make sure that that's correct what we could do is come over here we're going

could do is come over here we're going to click on this paragraph it's going to

to click on this paragraph it's going to take us to that P tag where the class is

take us to that P tag where the class is equal to lead let's come over here and

equal to lead let's come over here and look at this paragraph now we have

look at this paragraph now we have another P tag right over here with the

another P tag right over here with the class is equal to glyphicon glyphicon

class is equal to glyphicon glyphicon education I have no idea what that means

education I have no idea what that means um and then we'll go to our last one

um and then we'll go to our last one which is right here where the P tag is

which is right here where the P tag is equal to uh we have a tag HRA class uh

equal to uh we have a tag HRA class uh and a bunch of other information so

and a bunch of other information so let's say we just wanted to pull in this

let's say we just wanted to pull in this paragraph right here let's go here and

paragraph right here let's go here and see how we can specify this information

see how we can specify this information so it looks like P or the class is equal

so it looks like P or the class is equal to lead that looks like it's going to be

to lead that looks like it's going to be unique to just that one so if we come

unique to just that one so if we come down here we're going to say comma and

down here we're going to say comma and it was class so you can do uh class

it was class so you can do uh class underscore is equal to and then we're

underscore is equal to and then we're going to say lead let's try running this

going to say lead let's try running this and we're just pulling in that

and we're just pulling in that information now let's say we actually

information now let's say we actually want to pull in this paragraph We

want to pull in this paragraph We actually want this text right here and

actually want this text right here and this is a very real use case you know

this is a very real use case you know let's say I'm trying to pull in some

let's say I'm trying to pull in some information or or a paragraph of text

information or or a paragraph of text well let's copy this and what we're

well let's copy this and what we're going to then do is say. text and let's

going to then do is say. text and let's run this now we're going to get an error

run this now we're going to get an error right here and this is a very common

right here and this is a very common error because we're trying to use find

error because we're trying to use find all unfortunately find all does not have

all unfortunately find all does not have a text attribute we actually need to

a text attribute we actually need to change this to find typically when I'm

change this to find typically when I'm working with these find and find alls

working with these find and find alls I'm using findall most of the time until

I'm using findall most of the time until I want to start extracting text then

I want to start extracting text then when I specify it I'll change this back

when I specify it I'll change this back to find just like this now let's try

to find just like this now let's try this and now we're getting in

this and now we're getting in parentheses this information now this is

parentheses this information now this is all wonky it needs to definitely be

all wonky it needs to definitely be cleaned up a little bit but if we code

cleaned up a little bit but if we code back up it's no longer in a list and we

back up it's no longer in a list and we no longer have things like these P tags

no longer have things like these P tags in here or this class attribute so we're

in here or this class attribute so we're really just trying to pull out this

really just trying to pull out this information now again this does not look

information now again this does not look perfect we could even try to do

perfect we could even try to do something like do strip look like

something like do strip look like there's some white space uh that cleans

there's some white space uh that cleans it up a little bit this definitely looks

it up a little bit this definitely looks a little better um and we could

a little better um and we could definitely go in here and clean this up

definitely go in here and clean this up more but just for you know an example

more but just for you know an example this is how we can then extract that

this is how we can then extract that information now let's look at one more

information now let's look at one more example this is some information and

example this is some information and this is what we're going to do kind of

this is what we're going to do kind of our little mini project in the next

our little mini project in the next lesson on let's say we wanted to take

lesson on let's say we wanted to take all this information what if we wanted

all this information what if we wanted to pull in something like the team name

to pull in something like the team name that's going to be in right here in this

that's going to be in right here in this TR tag and each of these TR tags have th

TR tag and each of these TR tags have th tags underneath them so if we scroll

tags underneath them so if we scroll down you'll notice that each row is this

down you'll notice that each row is this TR tag so let's go ahead and search for

TR tag so let's go ahead and search for let's do th let's just search for that

let's do th let's just search for that first so let's come right back up here

first so let's come right back up here let's use this find

let's use this find all and we'll get rid of this text for

all and we'll get rid of this text for right now and let's just say we want to

right now and let's just say we want to look for the TR is that what we said we

look for the TR is that what we said we were looking for no th so let's say

were looking for no th so let's say we're looking for th let's go ahead and

we're looking for th let's go ahead and run this so we're going to have

run this so we're going to have underneath this th we have team name

underneath this th we have team name year wins losses and notice these are

year wins losses and notice these are all the titles so these titles are the

all the titles so these titles are the only ones with these th tags if we go

only ones with these th tags if we go down you'll notice that the data is

down you'll notice that the data is actually TD tags so now let's go back

actually TD tags so now let's go back and look for TD we'll say

and look for TD we'll say D and this is going to be a lot longer

D and this is going to be a lot longer we have a lot of information but these

we have a lot of information but these are all the rows of data let's see if we

are all the rows of data let's see if we can just get one piece of this data

can just get one piece of this data we're going to get back we want just

we're going to get back we want just this team name that's all we're trying

this team name that's all we're trying to pull in for now um and then we'll try

to pull in for now um and then we'll try to get this row and then in the next

to get this row and then in the next lesson we're going to try to get all of

lesson we're going to try to get all of this information make it look really

this information make it look really nice and then we'll put it into a

nice and then we'll put it into a panda's data frame so let's just get

panda's data frame so let's just get this team name right now let's go ahead

this team name right now let's go ahead we're going to say th

we're going to say th let's run this and we have this th and

let's run this and we have this th and now that we know we're getting this

now that we know we're getting this information in we can

information in we can do find let's run this so there's our

do find let's run this so there's our team name we're just going to say. text

team name we're just going to say. text and again we can do do strip just like

and again we can do do strip just like that and Bam we have our team name so

that and Bam we have our team name so you can kind of start getting the idea

you can kind of start getting the idea of how we're pulling this information

of how we're pulling this information out we're really just specifying exactly

out we're really just specifying exactly what we're seeing in this HTML and

what we're seeing in this HTML and what's really really helpful and you

what's really really helpful and you know something that I do all the time is

know something that I do all the time is I'm inspecting it I'm just kind of

I'm inspecting it I'm just kind of searching like how what do I want what

searching like how what do I want what piece of information do I want then I go

piece of information do I want then I go ahead and click on it and then I'm

ahead and click on it and then I'm looking you know where is this sitting

looking you know where is this sitting in the hierarchy it's within the body

in the hierarchy it's within the body it's within this table with the class of

it's within this table with the class of table then it's down here where this TR

table then it's down here where this TR tag and then this TD tag so I'm looking

tag and then this TD tag so I'm looking kind of at the hierarchy and I'm

kind of at the hierarchy and I'm specifying exactly what I'm looking for

specifying exactly what I'm looking for so that is what we're going to look at

so that is what we're going to look at in today's lesson that's how we can use

in today's lesson that's how we can use find and find all we were able to look

find and find all we were able to look at classes and tags and attributes and

at classes and tags and attributes and variable strings which is this right

variable strings which is this right here getting that text uh and variable

here getting that text uh and variable strings and we will look at find and

strings and we will look at find and find all and how it's pulling that

find all and how it's pulling that information in and how we can specify

information in and how we can specify exactly what we're looking for now in

exactly what we're looking for now in the next lesson which is definitely

the next lesson which is definitely going to be the most exciting one we're

going to be the most exciting one we're going to try to pull in all of this

going to try to pull in all of this information so every single thing

information so every single thing because we'll be able to put all this

because we'll be able to put all this information into a data frame which then

information into a data frame which then we can use pandas to really search and

we can use pandas to really search and manipulate that data within that data

manipulate that data within that data frame so with that being said that is

frame so with that being said that is the end of this lesson if you like this

the end of this lesson if you like this video be sure to like And subscribe I

video be sure to like And subscribe I will see you in the next

will see you in the next [Music]

[Music] lesson hello everybody in this lesson we

lesson hello everybody in this lesson we are going to be scraping data from a

are going to be scraping data from a real website and putting it into a p

real website and putting it into a p and's data frame and maybe even

and's data frame and maybe even exporting it to CSV if we're feeling a

exporting it to CSV if we're feeling a bit spicy now in the last several

bit spicy now in the last several lessons we've been looking at this page

lessons we've been looking at this page right here and I even promised that we

right here and I even promised that we were going to be pulling this data but

were going to be pulling this data but as I was building out the project I just

as I was building out the project I just I honestly thought it was a little bit

I honestly thought it was a little bit too easy since in the last lesson we

too easy since in the last lesson we kind of already pulled out some

kind of already pulled out some information from this table and I want

information from this table and I want to kind of throw you guys off so we're

to kind of throw you guys off so we're going to be pulling from a different

going to be pulling from a different table we're going to be going on to

table we're going to be going on to Wikipedia and looking at the list of the

Wikipedia and looking at the list of the largest companies in the United States

largest companies in the United States by Revenue and we're going to be pulling

by Revenue and we're going to be pulling all of this information so if you

all of this information so if you thought this was going to be easy in a

thought this was going to be easy in a little mini project uh it's now a full

little mini project uh it's now a full project because why not so let's get

project because why not so let's get started uh what we're going to do is

started uh what we're going to do is we're going to import beautiful soup and

we're going to import beautiful soup and requests we're going to get this

requests we're going to get this information and we're going to see how

information and we're going to see how we can do this and it's going to get a

we can do this and it's going to get a little bit more complicated and a little

little bit more complicated and a little bit more tricky we're going to have to

bit more tricky we're going to have to you know format things properly to get

you know format things properly to get it into our Panda data frame to make it

it into our Panda data frame to make it looking good and making it more usable

looking good and making it more usable so let's go ahead and get rid of the

so let's go ahead and get rid of the this easy table we don't want that one

this easy table we don't want that one uh and we're going to come in here and

uh and we're going to come in here and we're just going to start off this

we're just going to start off this should look uh really familiar by now

should look uh really familiar by now we're going to say from bs4 import

we're going to say from bs4 import beautiful soup I don't know if you've

beautiful soup I don't know if you've noticed but I've messed up spelling

noticed but I've messed up spelling beautiful soup in every single uh video

beautiful soup in every single uh video I've noticed uh let's run this and now

I've noticed uh let's run this and now we need to go ahead and get our URL so

we need to go ahead and get our URL so let's come up here let's get our

let's come up here let's get our URL say URL is equal to and we'll just

URL say URL is equal to and we'll just keep it all in the same thing really

keep it all in the same thing really quickly because we know this by Heart by

quickly because we know this by Heart by now right uh we'll say request.get and

now right uh we'll say request.get and then URL to make sure that we're getting

then URL to make sure that we're getting that information it give us a response

that information it give us a response object um hopefully it'll be 200 that'll

object um hopefully it'll be 200 that'll mean a good response and then we'll say

mean a good response and then we'll say soup is equal to and then we'll say

soup is equal to and then we'll say beautiful soup and we'll do our page.

beautiful soup and we'll do our page. text now we're pulling in the

text now we're pulling in the information from this URL and then we

information from this URL and then we use our parser which will be oops HTML

use our parser which will be oops HTML and let's go ahead and run this looks

and let's go ahead and run this looks like everything went well let's print

like everything went well let's print our soup now this is completely new to

our soup now this is completely new to you it's completely new to me I don't

you it's completely new to me I don't know what I'm doing uh but it looks like

know what I'm doing uh but it looks like we're pulling in the information am I

we're pulling in the information am I right so we got a lot of things going

right so we got a lot of things going for us uh the uh stuff was imported

for us uh the uh stuff was imported properly we got our URL we got our soup

properly we got our URL we got our soup which is uh not beautiful in my opinion

which is uh not beautiful in my opinion but let's keep on rolling let's come

but let's keep on rolling let's come right down here now what we need to do

right down here now what we need to do is we we need to specify what data we're

is we we need to specify what data we're looking for so let's come and let's

looking for so let's come and let's inspect this web page now the only

inspect this web page now the only information that we're going to want is

information that we're going to want is right in here we're going to want these

right in here we're going to want these uh titles or these headers whoops so

uh titles or these headers whoops so we're going to want rank name industry

we're going to want rank name industry Etc and then we are for sure going to

Etc and then we are for sure going to want all of this information let's just

want all of this information let's just scroll down see if there's anything

scroll down see if there's anything tricky in

tricky in here all right that looks pretty good

here all right that looks pretty good and there is another table so there's

and there is another table so there's not just one table in here there are two

not just one table in here there are two tables in this page so that might change

tables in this page so that might change things for us but let's come right back

things for us but let's come right back and let's inspect our page by using this

and let's inspect our page by using this little button right here and let's

little button right here and let's specify in let's see if I can highlight

specify in let's see if I can highlight just this page oh it's not going oh

just this page oh it's not going oh let's do that right there so now we have

let's do that right there so now we have this uh Wiki table sorter now I'm going

this uh Wiki table sorter now I'm going to actually come right here I'm going to

to actually come right here I'm going to copy and I'm just going to say copy the

copy and I'm just going to say copy the outer HTML just just going to paste in

outer HTML just just going to paste in here real quick and that's a ton of

here real quick and that's a ton of information I didn't think it was going

information I didn't think it was going to copy all of it and we're just going

to copy all of it and we're just going to delete that I just wanted to keep

to delete that I just wanted to keep that class uh because I wanted to then

that class uh because I wanted to then come right down here at the bottom and

come right down here at the bottom and just see what this table uh looks like I

just see what this table uh looks like I don't know if it's part of it or if it's

don't know if it's part of it or if it's a if it's its own

a if it's its own table um I can't tell let's look at this

table um I can't tell let's look at this Rank and let's come up so it says uh

Rank and let's come up so it says uh it's under this

it's under this table and it looks like it's its own

table and it looks like it's its own table but it says Wiki table sort

table but it says Wiki table sort sortable jQuery table sorter wikip

sortable jQuery table sorter wikip sortable jQuery table sorter so it looks

sortable jQuery table sorter so it looks like there are two tables with the same

like there are two tables with the same class which shouldn't be a problem if

class which shouldn't be a problem if we're using find to get our text because

we're using find to get our text because we should be taking the first one which

we should be taking the first one which will be this table and this is the table

will be this table and this is the table we want um and if we wanted this one we

we want um and if we wanted this one we could just use find all and since it's a

could just use find all and since it's a list we could use index ing to pull this

list we could use index ing to pull this table right um but I think we're going

table right um but I think we're going to be okay with just pulling in this one

to be okay with just pulling in this one so let's go ahead and let's do our find

so let's go ahead and let's do our find so we'll do

so we'll do soup. find and we could find all or we

soup. find and we could find all or we could just do find uh table let's just

could just do find uh table let's just try this and see what we get and if it

try this and see what we get and if it pulls in the right one that we're

pulls in the right one that we're looking for that' be great now this does

looking for that' be great now this does not look correct at all um I don't know

not look correct at all um I don't know what table it's pulling in oh maybe it's

what table it's pulling in oh maybe it's this right here this might be a table

this right here this might be a table yeah it is so we have this uh box more

yeah it is so we have this uh box more citations so actually we are going to

citations so actually we are going to have to do exactly like what I was

have to do exactly like what I was talking about uh let's pull

talking about uh let's pull this and we well we could do comma class

this and we well we could do comma class uh right here and let's do both you know

uh right here and let's do both you know what this is a learning opportunity

what this is a learning opportunity let's do both so let me go back up to

let's do both so let me go back up to the top because I need these um and what

the top because I need these um and what we're going to do let's come right down

we're going to do let's come right down here I want to add in uh another thing

here I want to add in uh another thing actually I'll just push this one up

actually I'll just push this one up there we go so we're going to say findor

there we go so we're going to say findor all let's run this so now we have

all let's run this so now we have multiple and again we got that weird one

multiple and again we got that weird one first but if we scroll down here's our

first but if we scroll down here's our comma and then here's our wik Wiki table

comma and then here's our wik Wiki table sortable and then we have rank name

sortable and then we have rank name industry all the ones that we were

industry all the ones that we were hoping to see and I guarantee you if you

hoping to see and I guarantee you if you scroll all the way to the bottom

scroll all the way to the bottom um we're going to

um we're going to see potentially Well Fargo Goldman Sachs

see potentially Well Fargo Goldman Sachs I'm pretty sure those are

I'm pretty sure those are um let's see yeah here we go like Ford

um let's see yeah here we go like Ford motor Wells Fargo Goldman Sachs that's

motor Wells Fargo Goldman Sachs that's this table right here so now we're

this table right here so now we're looking at the third table but again

looking at the third table but again this is a list so we can use indexing on

this is a list so we can use indexing on this and we'll just choose not position

this and we'll just choose not position zero because that's this one right here

zero because that's this one right here which we did not like well now we'll

which we did not like well now we'll take position one let's run this let's

take position one let's run this let's go back up to the top and this is our

go back up to the top and this is our table right here rank name industry this

table right here rank name industry this is the information that we were actually

is the information that we were actually wanting just to confirm rank name

wanting just to confirm rank name industry Etc so this is the information

industry Etc so this is the information we're wanting and we're able to specify

we're wanting and we're able to specify that with our findall and this is the

that with our findall and this is the information we want so we now want to

information we want so we now want to make this the only information that

make this the only information that we're looking at so I'm just going to

we're looking at so I'm just going to copy this we didn't need to use our

copy this we didn't need to use our class for this one you could probably

class for this one you could probably could have um but we could so let's

could have um but we could so let's actually um put this right down here

actually um put this right down here this will be our table we'll say equal

this will be our table we'll say equal to but then I'll come right here and I'm

to but then I'll come right here and I'm going to say soup. find this is just for

going to say soup. find this is just for demonstration purposes we do table comma

demonstration purposes we do table comma class underscore is equal to and then

class underscore is equal to and then we'll look at this right here whoops me

we'll look at this right here whoops me do this and let's see if we get the

do this and let's see if we get the correct

correct output and let's run this and looks like

output and let's run this and looks like we're getting a nun type object uh if I

we're getting a nun type object uh if I remember remember looks like the actual

remember remember looks like the actual class is this right here so let's run

class is this right here so let's run this instead and I got to get rid of the

this instead and I got to get rid of the index there we go okay so we were able

index there we go okay so we were able to pull it in just using the find so the

to pull it in just using the find so the find table class and it says Wiki table

find table class and it says Wiki table sortable at least that's the HTML that

sortable at least that's the HTML that we're pulling in right here let me go

we're pulling in right here let me go back because I don't don't know if

back because I don't don't know if that's what I was seeing

that's what I was seeing earlier let's just get this rank let's

earlier let's just get this rank let's go back up

go back up where's the

where's the rank we go rank there we go so here's

rank we go rank there we go so here's our Rank and let's go up to the table

our Rank and let's go up to the table and there's our

and there's our class yeah and and that's just uh to me

class yeah and and that's just uh to me that's a little bit odd so it says Wiki

that's a little bit odd so it says Wiki table sortable jQuery Das table sorder

table sortable jQuery Das table sorder right here but in our

right here but in our actual um in our actual python script

actual um in our actual python script that we're running it was only pulling

that we're running it was only pulling in the wiki table sortable so it wasn't

in the wiki table sortable so it wasn't pulling in the jQuery dot sorter why uh

pulling in the jQuery dot sorter why uh I'm not 100% sure but all things that

I'm not 100% sure but all things that we're working through and we were able

we're working through and we were able to uh we were able to figure out so

to uh we were able to figure out so we're going to make this our table we're

we're going to make this our table we're going to say tables equal to uh soup.

going to say tables equal to uh soup. findall and let's run this and if we

findall and let's run this and if we print out our table we have this table

print out our table we have this table now this is our only data that we are

now this is our only data that we are looking at now the first thing that I

looking at now the first thing that I want to get is I want to get these

want to get is I want to get these titles or these headers right here

titles or these headers right here that's where we're going to get first so

that's where we're going to get first so let's go in here we can just look in

let's go in here we can just look in this information you can see that these

this information you can see that these are with these th tags and we can pull

are with these th tags and we can pull out those th tags really easily let's

out those th tags really easily let's come right down here we're just going to

come right down here we're just going to say th and we can get rid of this let's

say th and we can get rid of this let's run this now these are our only th tags

run this now these are our only th tags because everything else is a TR tag for

because everything else is a TR tag for these rows of data so these th tags are

these rows of data so these th tags are pretty unique which makes it really easy

pretty unique which makes it really easy which is really really great because

which is really really great because then we can just do worldcore titles is

then we can just do worldcore titles is equal to so now we have these titles but

equal to so now we have these titles but uh they're not perfect but what we're

uh they're not perfect but what we're going to do is we're going to Loop

going to do is we're going to Loop through it so I'm going to say worldcore

through it so I'm going to say worldcore titles and I'll kind of walk through

titles and I'll kind of walk through what I'm talking about isn't a list and

what I'm talking about isn't a list and each one is Within These th tags so th

each one is Within These th tags so th and then there's our um string that

and then there's our um string that we're trying to get so we can easily

we're trying to get so we can easily take this list and use list

take this list and use list comprehension and we can do that right

comprehension and we can do that right down here so I'm going to keep this

down here so I'm going to keep this where we can see it um we'll do

where we can see it um we'll do worldcore

worldcore tore titles that's equal to now we'll do

tore titles that's equal to now we'll do our list comprehension should be super

our list comprehension should be super easy uh we'll just say for title in

easy uh we'll just say for title in worldcore titles and then what do we

worldcore titles and then what do we want we want title. text that's it um

want we want title. text that's it um because we're just taking the text from

because we're just taking the text from each of these we're just looping through

each of these we're just looping through and we're getting rank then We're

and we're getting rank then We're looping through getting name looping

looping through getting name looping through getting industry that's that's

through getting industry that's that's it so let's go and print our world table

it so let's go and print our world table titles and see if it worked and it did

titles and see if it worked and it did uh this looks like it needs to be

uh this looks like it needs to be cleaned up just a little bit so let's go

cleaned up just a little bit so let's go ahead and do that while we're here

ahead and do that while we're here before we actually put it into the uh

before we actually put it into the uh P's data frame oops I just wanted uh I

P's data frame oops I just wanted uh I just wanted this actually so what we're

just wanted this actually so what we're going to do is try to get rid of those

going to do is try to get rid of those back slash ends if we do dot strip that

back slash ends if we do dot strip that may actually not work yeah uh because

may actually not work yeah uh because this is a list what we need to do is we

this is a list what we need to do is we can actually do it dot. text. strip

can actually do it dot. text. strip right here let's try to do it in there

right here let's try to do it in there there we go so now we have uh this and

there we go so now we have uh this and now this world tables is good to go now

now this world tables is good to go now I'm actually noticing one thing that may

I'm actually noticing one thing that may be odd yeah so we have rank name

be odd yeah so we have rank name industry goes to headquarters but then

industry goes to headquarters but then in here we're getting rank name industry

in here we're getting rank name industry and then the

and then the profits which is

profits which is from this table right here which we

from this table right here which we don't want uh let's scroll back up let's

don't want uh let's scroll back up let's kind of backtrack this and see where

kind of backtrack this and see where this happened we did find all table

this happened we did find all table we're looking at the first one

we're looking at the first one right and then we're doing

right and then we're doing [Music]

[Music] headquarters uh so we're doing print

headquarters uh so we're doing print table ah okay I think I found the issue

table ah okay I think I found the issue here and let's backtrack again this is

here and let's backtrack again this is we're working through this together

we're working through this together we're going to make mistakes uh the

we're going to make mistakes uh the table is what we actually wanted to do

table is what we actually wanted to do we just did soup. findall th which is

we just did soup. findall th which is going to pull in that secondary table um

going to pull in that secondary table um jeez we were not thinking here um so now

jeez we were not thinking here um so now we need to do find all on the table not

we need to do find all on the table not the soup because now we were looking at

the soup because now we were looking at all of them oh what a rookie mistake

all of them oh what a rookie mistake okay uh let's go back now let's look at

okay uh let's go back now let's look at this now it's just down to headquarters

this now it's just down to headquarters okay okay let's go ahead and run this

okay okay let's go ahead and run this let's run this now we just have

let's run this now we just have headquarters now let's run this now we

headquarters now let's run this now we are sitting pretty okay excuse my

are sitting pretty okay excuse my mistakes Hey listen you know if it

mistakes Hey listen you know if it happens to me it happens to you I

happens to me it happens to you I promise you this is you know this is a

promise you this is you know this is a project this a little U little project

project this a little U little project we're creating here so we're going to

we're creating here so we're going to run into issues and that's okay we're

run into issues and that's okay we're figuring out as we go now what I want to

figuring out as we go now what I want to do before we start pulling in all the

do before we start pulling in all the data is I want to put this into our

data is I want to put this into our Panda's data frame we'll have the uh you

Panda's data frame we'll have the uh you know headers there for us to go so we

know headers there for us to go so we won't have to get that later and it just

won't have to get that later and it just makes it easier uh in general trust me

makes it easier uh in general trust me so we're going to import pandas as PD

so we're going to import pandas as PD let's go ahead and run this and now

let's go ahead and run this and now we're going to create our data frame so

we're going to create our data frame so we'll say PD dot now we have these world

we'll say PD dot now we have these world uh table titles so what we're going to

uh table titles so what we're going to do is pd. data frame and then in here

do is pd. data frame and then in here for our columns we'll say that's equal

for our columns we'll say that's equal to the world table titles and let's just

to the world table titles and let's just go ahead and say that's our data frame

go ahead and say that's our data frame and call our data frame right here let's

and call our data frame right here let's run it there we go so we were able to

run it there we go so we were able to pull out and extract those headers and

pull out and extract those headers and those titles of these columns we're able

those titles of these columns we're able to put it into our data frame so we're

to put it into our data frame so we're set up and we're ready to go we're

set up and we're ready to go we're rocking and rolling the next thing we

rocking and rolling the next thing we need let's go back up next thing we need

need let's go back up next thing we need is to start pulling in this data right

is to start pulling in this data right here so we have to see how we can pull

here so we have to see how we can pull this data in now if you

this data in now if you remember that we had those th tags those

remember that we had those th tags those were our titles as you can see I'm

were our titles as you can see I'm highlighting over it but down here now

highlighting over it but down here now we have these TD tags and those are all

we have these TD tags and those are all encapsulated within a TR tag so these TR

encapsulated within a TR tag so these TR represent the row

represent the row right then the D represents the data

right then the D represents the data within those rows so R for rows D for

within those rows so R for rows D for data so let's see how we can use that in

data so let's see how we can use that in order to get the information that we

order to get the information that we want so let's go back up here just going

want so let's go back up here just going to take this because again we're only

to take this because again we're only pulling from table not soup not soup

pulling from table not soup not soup what were we thinking um and let's go

what were we thinking um and let's go ahead and let's look at TR let's run

ahead and let's look at TR let's run this now when we're doing this TR these

this now when we're doing this TR these do come in with the head so we're going

do come in with the head so we're going to have to later on we're going to have

to have to later on we're going to have to get rid of these we don't want to

to get rid of these we don't want to pull those in um and have that as part

pull those in um and have that as part of our data but if we scroll down

of our data but if we scroll down there's our

there's our Walmart um we have the location these

Walmart um we have the location these are all with these TD tags and then of

are all with these TD tags and then of course it's separated by a comma then we

course it's separated by a comma then we have our td2 so above we had our td1 so

have our td2 so above we had our td1 so Row one row two Row three all the way

Row one row two Row three all the way down now we will easily be able to use

down now we will easily be able to use this right because this is our column

this right because this is our column data and we can even call it that column

data and we can even call it that column underscore data is equal to we'll run

underscore data is equal to we'll run that um and what we're going to do is

that um and what we're going to do is we're going to Loop through that because

we're going to Loop through that because it was all in a list so we're going to

it was all in a list so we're going to Loop through that information but

Loop through that information but instead of looking at the TR tag we're

instead of looking at the TR tag we're going to look at the T D tag so let's

going to look at the T D tag so let's come right down here we'll say for the

come right down here we'll say for the row in column

row in column row and we'll do a colon now we need to

row and we'll do a colon now we need to Loop through this we'll do something

Loop through this we'll do something like row. findor all all and then what

like row. findor all all and then what are we looking for we're not looking for

are we looking for we're not looking for the TR looking for the TD and just for

the TR looking for the TD and just for now let's print this off see what this

now let's print this off see what this looks like apparently I didn't run this

looks like apparently I didn't run this uh column data that's

uh column data that's why and let's run

why and let's run this and what we actually need to do is

this and what we actually need to do is something almost exactly like

something almost exactly like this and I'm going to put it right below

this and I'm going to put it right below it um instead of of printing this off

it um instead of of printing this off because again this is all in a list

because again this is all in a list we're using find all so we're we're

we're using find all so we're we're printing off another list which isn't

printing off another list which isn't actually super helpful um for each of or

actually super helpful um for each of or all these data that we're pulling in

all these data that we're pulling in what we can do is we can call this uh

what we can do is we can call this uh the rowcor data and then we'll put the

the rowcor data and then we'll put the row data in here so we'll say four and

row data in here so we'll say four and we'll say in row data so we'll just say

we'll say in row data so we'll just say for the data in row data and we'll take

for the data in row data and we'll take the data we'll exchange that and now

the data we'll exchange that and now instead of uh World Table titles we can

instead of uh World Table titles we can change this into uh

change this into uh individual row data right and now let's

individual row data right and now let's print off the individual row data so

print off the individual row data so it's the exact same process that we were

it's the exact same process that we were doing up here and that's how we cleaned

doing up here and that's how we cleaned it up and got this and we may not need

it up and got this and we may not need to strip but let's just run this and see

to strip but let's just run this and see what we get there we go um and strip I'm

what we get there we go um and strip I'm sure was helpful let's actually get rid

sure was helpful let's actually get rid of

of this yeah strip was helpful is the exact

this yeah strip was helpful is the exact same thing that happened on the last one

same thing that happened on the last one so let's keep that actually let's run

so let's keep that actually let's run this and now let's just kind of glance

this and now let's just kind of glance at this information let's look through

at this information let's look through it this looks exactly like the

it this looks exactly like the information that's in the table let's

information that's in the table let's just confirm with this first one uh 25

just confirm with this first one uh 25 uh two what am I saying 572 754 2.4

uh two what am I saying 572 754 2.4 2300 57275 2.4 2200 so this looks

2300 57275 2.4 2200 so this looks exactly correct now we have to figure

exactly correct now we have to figure out a way to get this into our table

out a way to get this into our table because again these are all individual

because again these are all individual lists it's not like we're just you know

lists it's not like we're just you know putting all of this in at one time we

putting all of this in at one time we can't just take the entire table and

can't just take the entire table and plop it into um into the data frame we

plop it into um into the data frame we need a way to kind of put this in one at

need a way to kind of put this in one at a time now if you're just here for web

a time now if you're just here for web scraping and you haven't taken like my

scraping and you haven't taken like my panda series that's totally fine that's

panda series that's totally fine that's not what we're here for anyways um but

not what we're here for anyways um but what we can do we'll have our individual

what we can do we'll have our individual row data and we're going to put it in

row data and we're going to put it in kind of one at a time time now the

kind of one at a time time now the reason we have to do that is because

reason we have to do that is because when we had it like this and let's go

when we had it like this and let's go back when we had it like this it's

back when we had it like this it's printing out all of it but what it's

printing out all of it but what it's really doing and let's get rid of it um

really doing and let's get rid of it um what it's really doing is it's kind of

what it's really doing is it's kind of doing it like this it's printing it off

doing it like this it's printing it off one at a time and it's only going to

one at a time and it's only going to save that current row of data this last

save that current row of data this last one it's only going to save that as it's

one it's only going to save that as it's looping through so what we actually want

looping through so what we actually want to do is every time it Loops through we

to do is every time it Loops through we append this information onto the data

append this information onto the data data frame so as it goes through and

data frame so as it goes through and eventually it's going to end up with

eventually it's going to end up with this one but as it goes through let's

this one but as it goes through let's run this as it goes through it puts this

run this as it goes through it puts this one in and then the next time it Loops

one in and then the next time it Loops through it puts this one in and the next

through it puts this one in and the next time it Loops through Etc all the way

time it Loops through Etc all the way down um so let's see how we can do this

down um so let's see how we can do this so we have our data frame right here

so we have our data frame right here let's get rid of this let's bring our

let's get rid of this let's bring our data frame in now again like I just

data frame in now again like I just mentioned if you don't know pandas and

mentioned if you don't know pandas and you haven't learned that uh you know go

you haven't learned that uh you know go take my uh series on that it's really

take my uh series on that it's really good and we do something very similar to

good and we do something very similar to this in that Series so I'm not going to

this in that Series so I'm not going to kind of walk through the entire logic um

kind of walk through the entire logic um but there is something called Lo which

but there is something called Lo which stands for location when you're looking

stands for location when you're looking at the index on a data frame and we're

at the index on a data frame and we're going to use that to our advantage so

going to use that to our advantage so we're going to say the length of the

we're going to say the length of the data frame so we're looking at how many

data frame so we're looking at how many rows are in this data frame and then

rows are in this data frame and then we're going to say that's our length

we're going to say that's our length then we're going to take that length and

then we're going to take that length and use it when we're actually putting in

use it when we're actually putting in this new information pretty um pretty

this new information pretty um pretty cool so we're going to say df.loc then a

cool so we're going to say df.loc then a bracket and we're putting in that length

bracket and we're putting in that length so we're checking the length of our data

so we're checking the length of our data frame each time it's looping through and

frame each time it's looping through and then we're going to put the information

then we're going to put the information in the next position that's exactly what

in the next position that's exactly what we're doing so let's go ahead and put in

we're doing so let's go ahead and put in the individual row data um so let's just

the individual row data um so let's just recap We're looping through this TR this

recap We're looping through this TR this is our column data so these TR that's

is our column data so these TR that's our row of data then we're as

our row of data then we're as as We're looping through it we're doing

as We're looping through it we're doing find all and looking for TD tags that's

find all and looking for TD tags that's our individual data so that's our row

our individual data so that's our row data then we're taking that data each

data then we're taking that data each piece of data and we're getting out the

piece of data and we're getting out the text and we're stripping it to kind of

text and we're stripping it to kind of clean it and now it's in a list for each

clean it and now it's in a list for each individual row then we're looking at our

individual row then we're looking at our current data frame which has nothing in

current data frame which has nothing in it right now we're looking at the length

it right now we're looking at the length of it and we're appending each row of

of it and we're appending each row of this information into the next position

this information into the next position so let's go ahead and run this

so let's go ahead and run this it's working it's thinking and it looks

it's working it's thinking and it looks like we got an issue canot set a row

like we got an issue canot set a row with mismatched columns now we're

with mismatched columns now we're encountering an issue not one that I got

encountering an issue not one that I got earlier but we're going to cancel this

earlier but we're going to cancel this out we're going to figure this out

out we're going to figure this out together so let's print off our

together so let's print off our individual row data let's look at this

individual row data let's look at this this one is empty uh this is I'm almost

this one is empty uh this is I'm almost certain is probably the issue um I

certain is probably the issue um I didn't encounter this issue when I wrote

didn't encounter this issue when I wrote these uh when I wrote this lesson um but

these uh when I wrote this lesson um but I'm almost certain that this is the

I'm almost certain that this is the issue right here so let's do the column

issue right here so let's do the column data but let's start at position um

data but let's start at position um let's try one and not parentheses I need

let's try one and not parentheses I need brackets because this is a list right so

brackets because this is a list right so it should work and there we go so now

it should work and there we go so now that first one's gone so now we just

that first one's gone so now we just have the information I didn't even think

have the information I didn't even think about that um just a second ago but I'm

about that um just a second ago but I'm glad we're running into it in case you

glad we're running into it in case you ran into that uh issue let's go ahead

ran into that uh issue let's go ahead and try this

and try this again and it looked like it worked so

again and it looked like it worked so let's pull our data frame down I could

let's pull our data frame down I could have just wrote DF let's pull our data

have just wrote DF let's pull our data frame down and now this is looking

frame down and now this is looking fantastic now um these three dots just

fantastic now um these three dots just mean there's information in there just

mean there's information in there just doesn't want to display it but it looks

doesn't want to display it but it looks like we have our rank we have our name

like we have our rank we have our name have the industry revenue revenue growth

have the industry revenue revenue growth employees and headquarters for every

employees and headquarters for every single one so this is perfect now this

single one so this is perfect now this is exactly what I was hoping to get now

is exactly what I was hoping to get now you can go in and use pandas and

you can go in and use pandas and manipulate this and change it and you

manipulate this and change it and you know dive into all the information in

know dive into all the information in there but we can also export this into a

there but we can also export this into a CSV if that's what you're wanting so we

CSV if that's what you're wanting so we could easily do that by saying we'll do

could easily do that by saying we'll do DF do2 CSV and then within here we're

DF do2 CSV and then within here we're just going to do R and specify our file

just going to do R and specify our file path so let's come down here to our file

path so let's come down here to our file path then we'll go to our folder for our

path then we'll go to our folder for our output so we're just going to take this

output so we're just going to take this path and let me do it like that so I

path and let me do it like that so I have this path in my one drive documents

have this path in my one drive documents python webscript being folder for output

python webscript being folder for output so you know I already made this um and

so you know I already made this um and I'm just going to put this right down

I'm just going to put this right down here now I do have to specify what we're

here now I do have to specify what we're going to call this um we'll just call

going to call this um we'll just call this companies and then we have to say

this companies and then we have to say CSV that is very important now if we run

CSV that is very important now if we run this I already know just because uh we

this I already know just because uh we have this Rank and this index here we're

have this Rank and this index here we're going to keep this index in the output

going to keep this index in the output not great uh but let's run it let's look

not great uh but let's run it let's look at our

at our output there's our companies and when we

output there's our companies and when we pull this up as you can see this is not

pull this up as you can see this is not what we want because we have this extra

what we want because we have this extra thing right here now if we're automating

thing right here now if we're automating this this would get super annoying so

this this would get super annoying so what we're going to do is go back and

what we're going to do is go back and just say index equals false let's go out

just say index equals false let's go out of here and now we're just going to come

of here and now we're just going to come right down here we're going to say comma

right down here we're going to say comma index equals false and so it's going to

index equals false and so it's going to take this index and it's not going to

take this index and it's not going to import or actually export it into the

import or actually export it into the CSV now let's go ahead and run

CSV now let's go ahead and run this let's pull up our folder one more

this let's pull up our folder one more time and let's refresh just to make sure

time and let's refresh just to make sure should be good and now this looks a lot

should be good and now this looks a lot better so we're able to take all of that

better so we're able to take all of that information and put it into a CSV and

information and put it into a CSV and it's all there so this is the whole

it's all there so this is the whole project so if we scroll all the way back

project so if we scroll all the way back up let's just kind of glance at what we

up let's just kind of glance at what we did here scroll down we brought in our

did here scroll down we brought in our libraries and packages we specified our

libraries and packages we specified our URL we brought in our soup um and then

URL we brought in our soup um and then we tried to find our table now that took

we tried to find our table now that took a little bit of uh testing out but we

a little bit of uh testing out but we knew that the table was the second one

knew that the table was the second one so in position one so we took that table

so in position one so we took that table we were also able to specify it using

we were also able to specify it using find but then we used the class and of

find but then we used the class and of course we just wanted to work with that

course we just wanted to work with that table that's all the data we wanted so

table that's all the data we wanted so we specifi this is our table and we

we specifi this is our table and we worked with just our table going forward

worked with just our table going forward of course uh we encountered some small

of course uh we encountered some small issues user errors on my end but we were

issues user errors on my end but we were able to get our world titles and we put

able to get our world titles and we put those into our data frame right here

those into our data frame right here using pandas then next we went back and

using pandas then next we went back and we got all the row data and the

we got all the row data and the individual data from those rows and we

individual data from those rows and we put it into our Panda data frame then we

put it into our Panda data frame then we came below and we exported this into an

came below and we exported this into an actual CSV file so that is how we can

actual CSV file so that is how we can use webs scraping to get data from

use webs scraping to get data from something like a table and put it into a

something like a table and put it into a panda data frame I hope that this lesson

panda data frame I hope that this lesson was helpful I know we encountered some

was helpful I know we encountered some issues that's on my end and I apologize

issues that's on my end and I apologize but if you run into those same issues

but if you run into those same issues hopefully that helped uh but I hope this

hopefully that helped uh but I hope this was helpful and if you like this be sure

was helpful and if you like this be sure to like And subscribe below I appreciate

to like And subscribe below I appreciate you I love you and I will see you in the

you I love you and I will see you in the next

next [Music]

[Music] lesson so the first thing that we need

lesson so the first thing that we need to do is import our Panda's Library so

to do is import our Panda's Library so we're going to say import we're going to

we're going to say import we're going to say pandas now this will import the

say pandas now this will import the pandas library but it's pretty common

pandas library but it's pretty common place to give it an alias and as a

place to give it an alias and as a standard when using pandas people will

standard when using pandas people will say as PD so this is just a quick Alias

say as PD so this is just a quick Alias that you can use uh that's what I always

that you can use uh that's what I always use and I've always used it because

use and I've always used it because that's how I learned it and I want to

that's how I learned it and I want to teach it to you the right way so that's

teach it to you the right way so that's how we're going to do it in this video

how we're going to do it in this video so let's hit shift enter now that that

so let's hit shift enter now that that is imported we can start reading in our

is imported we can start reading in our files now right down here I'm going to

files now right down here I'm going to open up my file explorer and we have

open up my file explorer and we have several different types of files in here

several different types of files in here we have CSV files text files Json files

we have CSV files text files Json files and an Excel worksheet which is a little

and an Excel worksheet which is a little bit different than a CSV so we're going

bit different than a CSV so we're going to import all of those I'm going to show

to import all of those I'm going to show you how to import it as well as some of

you how to import it as well as some of the different things that you need to be

the different things that you need to be aware of when you're importing so we're

aware of when you're importing so we're going to import some of those different

going to import some of those different file types and I'll show you how to do

file types and I'll show you how to do that within pandas so the first thing

that within pandas so the first thing that we need to say is PD Dot and let's

that we need to say is PD Dot and let's read in a CSV because that's a pretty

read in a CSV because that's a pretty common one we'll say

common one we'll say read

read CSV and this is liter literally all you

CSV and this is liter literally all you have to write in order to call it in now

have to write in order to call it in now it's not going to call it in as a string

it's not going to call it in as a string like it would in one of our previous

like it would in one of our previous videos if you're just using the regular

videos if you're just using the regular operating system of python when you're

operating system of python when you're using pandas it calls it in as a data

using pandas it calls it in as a data frame and I'll talk about some of the

frame and I'll talk about some of the nuances of that so let's go down to our

nuances of that so let's go down to our file explorer we have this countries of

file explorer we have this countries of the world CSV you just need to click on

the world CSV you just need to click on it and rightclick and copy as path and

it and rightclick and copy as path and that's literally going to copy that file

that's literally going to copy that file path for us you don't have to type it

path for us you don't have to type it out manually you can if You' like and

out manually you can if You' like and we're just going to paste it in between

we're just going to paste it in between these parentheses now if we run it right

these parentheses now if we run it right now it will not work I'll do that for

now it will not work I'll do that for you it's saying we have this Unicode

you it's saying we have this Unicode error uh basically what's happening is

error uh basically what's happening is is it's reading in these backs slashes

is it's reading in these backs slashes and this colon and all those back

and this colon and all those back clashes in there and this period at the

clashes in there and this period at the end what we need to do is read this in

end what we need to do is read this in as a raw text so we're just going to say

as a raw text so we're just going to say R and now it's going to read this as a

R and now it's going to read this as a literal string or a literal value and

literal string or a literal value and not as you know with all these back

not as you know with all these back slashes which does make a big difference

slashes which does make a big difference when we run this it's going to populate

when we run this it's going to populate our very first data frame so let's go

our very first data frame so let's go ahead and run it and now we have this

ahead and run it and now we have this CSV in here with our country and our

CSV in here with our country and our region now if we go and pull up this

region now if we go and pull up this file and let's do that really quickly

file and let's do that really quickly let's bring up this country's of the

let's bring up this country's of the world it automatically populated those

world it automatically populated those headers for us in the data frame but we

headers for us in the data frame but we don't have any column for those 0 1 2 3

don't have any column for those 0 1 2 3 so if we go back as you can see right

so if we go back as you can see right here there's this index and that's

here there's this index and that's really important in a data frame it's

really important in a data frame it's really makes a data frame a data frame

really makes a data frame a data frame and we use index a lot in pandas we're

and we use index a lot in pandas we're able to filter on the index search on

able to filter on the index search on the index and a lot of other things

the index and a lot of other things which I'll show you in future videos but

which I'll show you in future videos but this is basically how you read in a file

this is basically how you read in a file now if we go right up here in between

now if we go right up here in between these parentheses and we hit shift tab

these parentheses and we hit shift tab this is going to come up for us let's

this is going to come up for us let's hit this plus button and what this is is

hit this plus button and what this is is these are all the arguments or all the

these are all the arguments or all the things that we can specify when we're

things that we can specify when we're reading in a file and there are a lot of

reading in a file and there are a lot of different opts options so let's go ahead

different opts options so let's go ahead and take a look really quickly really

and take a look really quickly really quickly I wanted to give a huge shout

quickly I wanted to give a huge shout out to the sponsor of this entire Panda

out to the sponsor of this entire Panda series and that is udemy udemy has some

series and that is udemy udemy has some of the best courses at the best prices

of the best courses at the best prices and it is no exception when it comes to

and it is no exception when it comes to pandas courses if you want to master

pandas courses if you want to master pandas this is the course that I would

pandas this is the course that I would recommend it's going to teach you just

recommend it's going to teach you just about everything you need to know about

about everything you need to know about pandas so huge shout out to you me for

pandas so huge shout out to you me for sponsoring this Panda series and let's

sponsoring this Panda series and let's get back to the video the first thing is

get back to the video the first thing is obviously the file path we can specify a

obviously the file path we can specify a separator which there is no default so

separator which there is no default so when we're pulling in this CSV when

when we're pulling in this CSV when we're reading in the CSV it's

we're reading in the CSV it's automatically going to assume it's a

automatically going to assume it's a comma CU it's a comma separated uh file

comma CU it's a comma separated uh file you can choose delimers headers names

you can choose delimers headers names index columns and a lot of other things

index columns and a lot of other things as you can see right here now I will say

as you can see right here now I will say that I don't use almost any of these uh

that I don't use almost any of these uh the few that I'm going to show you

the few that I'm going to show you really quickly in just a second are up

really quickly in just a second are up the very top but you can do a ton of

the very top but you can do a ton of different things and I'm just going to

different things and I'm just going to slowly go through them so that's what

slowly go through them so that's what those are you can also go down here this

those are you can also go down here this is our dock string and you can see

is our dock string and you can see exactly how these parameters work it'll

exactly how these parameters work it'll show you and give you a text and walk

show you and give you a text and walk you through how to do this again most of

you through how to do this again most of these you'll probably never use but

these you'll probably never use but things like a separator could actually

things like a separator could actually be useful and things like a header could

be useful and things like a header could be useful because it is possible that

be useful because it is possible that you want to either rename your headers

you want to either rename your headers or you don't have a header in your CSV

or you don't have a header in your CSV and you don't want it to autop populate

and you don't want it to autop populate that header so that is something that

that header so that is something that you can specify so for example this

you can specify so for example this header one I'll show you how to do this

header one I'll show you how to do this uh the default behaviors is to infer

uh the default behaviors is to infer that there are column names if no names

that there are column names if no names are passed this behavior is identical to

are passed this behavior is identical to header equals zero so it's saying that

header equals zero so it's saying that first row or that first index which is

first row or that first index which is like right here that zero is going to be

like right here that zero is going to be read in as a header but we can come

read in as a header but we can come right over here and we'll do comma

right over here and we'll do comma header is equal to and we can say none

header is equal to and we can say none and as you can see there are no headers

and as you can see there are no headers now instead it's another index so we

now instead it's another index so we have index indexes on both the x- axis

have index indexes on both the x- axis and the Y AIS and so right now we have

and the Y AIS and so right now we have this zero and one index indicating the

this zero and one index indicating the First Column and the second column if we

First Column and the second column if we want to specify those names we can say

want to specify those names we can say the header equals none then we can say

the header equals none then we can say names is equal to and we'll give it a

names is equal to and we'll give it a list and so the first one was country

list and so the first one was country and what's that second one oh region so

and what's that second one oh region so right here that's the first um the first

right here that's the first um the first row but we'll rename it and we'll just

row but we'll rename it and we'll just say country region and when we run that

say country region and when we run that we've now populated the country and the

we've now populated the country and the region uh we're just pretending that our

region uh we're just pretending that our CSV does not have these values in it and

CSV does not have these values in it and we have to name it ourselves that's how

we have to name it ourselves that's how you do it but let's get rid of all that

you do it but let's get rid of all that because we actually do want those in

because we actually do want those in there so we're just going to get rid of

there so we're just going to get rid of those and read it in as normal and there

those and read it in as normal and there we go now typically when you're reading

we go now typically when you're reading in a file what you need to do is you

in a file what you need to do is you want to assign that to a variable almost

want to assign that to a variable almost always when you see any tutorial or

always when you see any tutorial or anybody online or even when you're

anybody online or even when you're actually working people will say DF is

actually working people will say DF is equal to DF stands for data frame again

equal to DF stands for data frame again this is a data frame in the next video

this is a data frame in the next video in this series I'm going to walk through

in this series I'm going to walk through what a series is as well as what a data

what a series is as well as what a data frame is because that's pretty important

frame is because that's pretty important to know when you're working with these

to know when you're working with these data frames but we'll assign it to this

data frames but we'll assign it to this value and then we'll say we'll call it

value and then we'll say we'll call it by saying DF and we'll run it and that's

by saying DF and we'll run it and that's typically how you'll do things because

typically how you'll do things because you want to save this data frame so

you want to save this data frame so later on you can do things like data

later on you can do things like data frame Dot and you can uh you know pass

frame Dot and you can uh you know pass in different modules but you can't

in different modules but you can't really do that it's not as easy to do it

really do that it's not as easy to do it if you're calling this entire CSV and

if you're calling this entire CSV and importing it every time so let's copy

importing it every time so let's copy this because now we're going to import a

this because now we're going to import a different type of file so now we've been

different type of file so now we've been doing read CSV but we can also import

doing read CSV but we can also import text files now you can do that with the

text files now you can do that with the read CSV we can import text files let's

read CSV we can import text files let's look at this one we have the same one

look at this one we have the same one it's countries of the world except now

it's countries of the world except now it's a text file because I just

it's a text file because I just converted it for this video I'll copy

converted it for this video I'll copy that as a path and so now when we do

that as a path and so now when we do this oops let me get

this oops let me get those quotes in there it'll say world.

those quotes in there it'll say world. txt it will still work as you can see

txt it will still work as you can see this did not import properly um we have

this did not import properly um we have this country back SLT region and then

this country back SLT region and then all of our values are the exact same

all of our values are the exact same with this back SLT that's because we

with this back SLT that's because we need to use a separator and I'll show

need to use a separator and I'll show you in just a little bit how we can do

you in just a little bit how we can do this in a different way but with that

this in a different way but with that read CSV this is how we can do it we'll

read CSV this is how we can do it we'll just say sep is equal to we need to do

just say sep is equal to we need to do back SLT now let's try running this and

back SLT now let's try running this and as you can see it now has it broken out

as you can see it now has it broken out into country and region we could also do

into country and region we could also do it the more proper way and this is the

it the more proper way and this is the way you should do it and I'll get rid of

way you should do it and I'll get rid of these really quickly but just want to

these really quickly but just want to keep them there in case you want to see

keep them there in case you want to see that but you can also do read table and

that but you can also do read table and let's get rid of this

let's get rid of this separator and now we have no separators

separator and now we have no separators just reading it in as a table let's run

just reading it in as a table let's run this and it reads it in proper L the

this and it reads it in proper L the first time this read table can be used

first time this read table can be used for tons of different data types but

for tons of different data types but typically I've been using it for like

typically I've been using it for like text files um we can also read in that

text files um we can also read in that CSV so let's change this right here to

CSV so let's change this right here to CSV we can read it in as a CSV but just

CSV we can read it in as a CSV but just like we did in the last one when we read

like we did in the last one when we read in the text file using read CSV this

in the text file using read CSV this read table you're going to need to

read table you're going to need to specify the separator so I'll just copy

specify the separator so I'll just copy this and we'll say comma and now it

this and we'll say comma and now it reads it in properly again you can use

reads it in properly again you can use that for a ton of different file types

that for a ton of different file types but you just need to specify a few more

but you just need to specify a few more things if you don't want to use the more

things if you don't want to use the more specific read uncore function when

specific read uncore function when you're using pandas now let's copy this

you're using pandas now let's copy this again we're going to go right down here

again we're going to go right down here and now let's do Json files Json files

and now let's do Json files Json files usually hold semi structured data um

usually hold semi structured data um which is definitely different than very

which is definitely different than very structured data like a CSV where has

structured data like a CSV where has columns and rows so let's go to our file

columns and rows so let's go to our file explorer we have this Json sample we

explorer we have this Json sample we will copy this in as the

will copy this in as the path let's paste it right here and we'll

path let's paste it right here and we'll do reor Json again these different

do reor Json again these different functions were built out specifically

functions were built out specifically for these file types that's why you know

for these file types that's why you know each one has a different name so now

each one has a different name so now we're reading this in as the

we're reading this in as the Json let's read it in and it read it in

properly now let's go ahead and copy this and take a look at Excel files

this and take a look at Excel files because Excel files are a little bit

because Excel files are a little bit different than other ones that we've

different than other ones that we've looked at

looked at um so let's just do read uncore

um so let's just do read uncore Excel and let's go down to our file

Excel and let's go down to our file explorer and let's actually open up this

explorer and let's actually open up this workbook as you can see we have sheet

workbook as you can see we have sheet one right here but we also have this

one right here but we also have this world population which has a lot more

world population which has a lot more data let's say we just wanted to read in

data let's say we just wanted to read in sheet one we can do that or by default

sheet one we can do that or by default it's going to read in this world

it's going to read in this world population because it's the first sheet

population because it's the first sheet in the Excel file well let's go ahead

in the Excel file well let's go ahead and take a look at that let's get out of

and take a look at that let's get out of here and and let's say oops I forgot to

here and and let's say oops I forgot to copy the file path let's go ahead and

copy the file path let's go ahead and copy as path and we'll put it right

copy as path and we'll put it right here and let's just read it in with no

here and let's just read it in with no arguments or anything in there or no

arguments or anything in there or no parameters when we read it in it's

parameters when we read it in it's reading in that very first sheet so this

reading in that very first sheet so this is the one that has all of the data now

is the one that has all of the data now let's say we wanted to read in that

let's say we wanted to read in that extra sheet name or the second sheet

extra sheet name or the second sheet name we'll just go comma sheet undor

name we'll just go comma sheet undor name say is equal to and then we can

name say is equal to and then we can specify sheet was it sheet one like this

specify sheet was it sheet one like this yes it was so we just had to specify the

yes it was so we just had to specify the sheet name right here and then it

sheet name right here and then it brought in that sheet instead of the

brought in that sheet instead of the default which is the very first sheet in

default which is the very first sheet in that Excel now that definitely covers a

that Excel now that definitely covers a lot of how you read in those files again

lot of how you read in those files again you can come in here and hit shift Tab

you can come in here and hit shift Tab and this plus sign and take a look at

and this plus sign and take a look at all the documentation and you can

all the documentation and you can specify a lot of different things things

specify a lot of different things things that I didn't think were very important

that I didn't think were very important for you guys to know especially if

for you guys to know especially if you're just starting out the ones that

you're just starting out the ones that we looked at today are what I would say

we looked at today are what I would say are like the ones that I use almost all

are like the ones that I use almost all the time so I wanted to show you those

the time so I wanted to show you those but if you're interested in any of these

but if you're interested in any of these other ones or you have very unique data

other ones or you have very unique data and you need to do that um you know it's

and you need to do that um you know it's worth really getting in here and

worth really getting in here and figuring things out a few other things

figuring things out a few other things that I wanted to show you just in this

that I wanted to show you just in this kind of first video or this intro video

kind of first video or this intro video on how to read in files um one thing

on how to read in files um one thing that you may have noticed especially in

that you may have noticed especially in this file right here is we're only

this file right here is we're only looking at the first five and then the

looking at the first five and then the last five so if we wanted to see all the

last five so if we wanted to see all the data all the data is in these like

data all the data is in these like little three dots right here right we

little three dots right here right we want to be able to see that data but

want to be able to see that data but right now we can't and that's because of

right now we can't and that's because of some settings that are already within

some settings that are already within pandas and all we need to do is change

pandas and all we need to do is change that so this one has 234 rows and four

that so this one has 234 rows and four columns so obviously we can see all the

columns so obviously we can see all the columns well let's just change the rows

columns well let's just change the rows all we'll say is pd. set uncore option

all we'll say is pd. set uncore option now what we need to do is we're going to

now what we need to do is we're going to change the rows we're not going to

change the rows we're not going to change the columns at least not on this

change the columns at least not on this one so we'll say quote

one so we'll say quote display. max. rows now if we just run

display. max. rows now if we just run this for whatever data we bring in it's

this for whatever data we bring in it's going to be able to show the max rows

going to be able to show the max rows and then we'll say

and then we'll say 235 although there's 234 rows I'm just

235 although there's 234 rows I'm just going to be safe let's run

going to be safe let's run this and now it has changed it so let's

this and now it has changed it so let's read in this file again and you'll see

read in this file again and you'll see how it's

how it's changed now we have all the numbers and

changed now we have all the numbers and we have this little bar on the right

we have this little bar on the right that allows us to go down all the way to

that allows us to go down all the way to the bottom and all the way to the top so

the bottom and all the way to the top so now we can actually look and kind of

now we can actually look and kind of skim and see our values I like that

skim and see our values I like that better than just having that you know

better than just having that you know shorter version um we can do the exact

shorter version um we can do the exact same thing on columns as well so if we

same thing on columns as well so if we look at this one this is our Json file

look at this one this is our Json file has the same thing right here we have

has the same thing right here we have what was it 38 columns but we can only

what was it 38 columns but we can only see I think it's maybe it's 20 or

see I think it's maybe it's 20 or something like that I can't remember um

something like that I can't remember um but we have 38 we can only see like

but we have 38 we can only see like let's say 15 of them or 20 of them we'll

let's say 15 of them or 20 of them we'll do the exact same thing and we'll just

do the exact same thing and we'll just say pd. set options.

say pd. set options. max.

max. columns and we'll set that to 40 for

columns and we'll set that to 40 for that one when we run this oops let's get

that one when we run this oops let's get over here when we run this one again we

over here when we run this one again we can now scroll over and see every single

can now scroll over and see every single one of our columns now that one is a in

one of our columns now that one is a in my opinion a lot more useful I like

my opinion a lot more useful I like being able to see every single column so

being able to see every single column so definitely something that you should be

definitely something that you should be using especially when you have these

using especially when you have these really large files you want to be able

really large files you want to be able to see a lot of the data and a lot of

to see a lot of the data and a lot of the columns so when you're slicing and

the columns so when you're slicing and dicing and doing all the things that

dicing and doing all the things that we're about to learn in this Panda

we're about to learn in this Panda series you know you know what you're

series you know you know what you're looking at I also want to show you just

looking at I also want to show you just how to kind of look at your data in

how to kind of look at your data in these data frames as well that's also

these data frames as well that's also pretty important so let's go right down

pretty important so let's go right down here and the very last one that we

here and the very last one that we imported was this one right here this

imported was this one right here this read Excel so this data frame is the

read Excel so this data frame is the only one that's going going to read in

only one that's going going to read in let's run it um this is the last one to

let's run it um this is the last one to be run so this variable right here DF uh

be run so this variable right here DF uh it won't be applied to all these other

it won't be applied to all these other ones which we can always go back and

ones which we can always go back and change those typically you'll do

change those typically you'll do something like data frame two you want

something like data frame two you want to do something like that um so let's

to do something like that um so let's keep data Frame 2 oops so what we're

keep data Frame 2 oops so what we're going to do is we're going to bring data

going to do is we're going to bring data Frame 2 right down here and we want to

Frame 2 right down here and we want to take a look at some of this data we want

take a look at some of this data we want to know a little bit more about it

to know a little bit more about it something that you can do is dataframe

something that you can do is dataframe 2. info and we'll do an open parenthesis

2. info and we'll do an open parenthesis and when we run this it's going to give

and when we run this it's going to give us a really quick breakdown of a little

us a really quick breakdown of a little bit of our data so we have our columns

bit of our data so we have our columns right here rank CCA 3 country and

right here rank CCA 3 country and capital it's saying we have

capital it's saying we have 234 values in those columns because

234 values in those columns because there's

there's 234 scroll up here because there's

234 scroll up here because there's 234 uh rows that tells me that there's

234 uh rows that tells me that there's no missing data in here at least not you

no missing data in here at least not you know completely missing like null values

know completely missing like null values there is something something in each of

there is something something in each of those rows the count tells me it's non-

those rows the count tells me it's non- null so there's no null values and it

null so there's no null values and it tells me the data type so it's ringing

tells me the data type so it's ringing in as an integer an object an object and

in as an integer an object an object and an object and it also tells us how much

an object and it also tells us how much memory it's using which is also pretty

memory it's using which is also pretty neat because when you get really really

neat because when you get really really large data types memory usage and and

large data types memory usage and and knowing how to work around that stuff

knowing how to work around that stuff does become more important than when

does become more important than when you're working at these really small You

you're working at these really small You Know sample sizes that we're looking at

Know sample sizes that we're looking at we can also do oops let me get rid of

we can also do oops let me get rid of that can also do data frame two

that can also do data frame two and we'll do shape and for this one we

and we'll do shape and for this one we do not need the

do not need the parentheses and all this is going to

parentheses and all this is going to tell us is we have 234 rows and four

tell us is we have 234 rows and four columns we're also able to look at uh

columns we're also able to look at uh the first few values or rows in each of

the first few values or rows in each of these data frames so we can just say

these data frames so we can just say data frame 2. head and if we do that

data frame 2. head and if we do that it's going to give us the first five

it's going to give us the first five values but we can specify how many we

values but we can specify how many we want we can say head 10 it'll give us

want we can say head 10 it'll give us the first 10 rows right here we can do

the first 10 rows right here we can do the exact same thing and let's go right

the exact same thing and let's go right down here and we'll say tail so they'll

down here and we'll say tail so they'll give us the last 10 rows within our data

give us the last 10 rows within our data frame now let's copy this and let's say

frame now let's copy this and let's say we don't want to actually look at all of

we don't want to actually look at all of these values or all these columns we can

these values or all these columns we can specify that by saying df2 and oops

specify that by saying df2 and oops let's get rid of all of

let's get rid of all of this and we'll say with a quote we'll

this and we'll say with a quote we'll say Rank and now we can take just a look

say Rank and now we can take just a look at the rank data now we can't do that by

at the rank data now we can't do that by doing the index or at least not like

doing the index or at least not like this if we want to use this index that

this if we want to use this index that is right here we can but there's a very

is right here we can but there's a very special function called Lo and IO for

special function called Lo and IO for that and I'm going to have an entire

that and I'm going to have an entire video on this because it does get a

video on this because it does get a little bit more complex but there's

little bit more complex but there's df2 and there's Lo and I stands for

df2 and there's Lo and I stands for location and I location that's only for

location and I location that's only for the indexes whether it's the x axis or

the indexes whether it's the x axis or the Y AIS those are the indexes and for

the Y AIS those are the indexes and for location it's looking for the actual

location it's looking for the actual text the actual string of the index so

text the actual string of the index so if we come up here that data Frame 2 we

if we come up here that data Frame 2 we can specify

can specify 224 and it'll give us this information

224 and it'll give us this information right here in a little different format

right here in a little different format so let's go bracket and we'll say

so let's go bracket and we'll say 224 and when we run this it gives us our

224 and when we run this it gives us our rank CCA country capital with our values

rank CCA country capital with our values over here kind of like a dictionary

over here kind of like a dictionary almost now let's copy this and we'll say

almost now let's copy this and we'll say df2 do IO and right now these look the

df2 do IO and right now these look the exact same but we haven't really talked

exact same but we haven't really talked a lot about changing the index and you

a lot about changing the index and you can change the index to a string or a

can change the index to a string or a different column or something like that

different column or something like that and we'll look at that in future videos

and we'll look at that in future videos the iock looks at the integer location

the iock looks at the integer location so even if these um let's go right up

so even if these um let's go right up here even if this index had changed to

here even if this index had changed to let's say this rank or this CCA 3 or

let's say this rank or this CCA 3 or country or whatever you make this index

country or whatever you make this index the ILO will still look at the integer

the ILO will still look at the integer location so that 224 would still be 224

location so that 224 would still be 224 even if it was usbekistan

even if it was usbekistan so then when we look at this it's going

so then when we look at this it's going to be the exact same but if we had

to be the exact same but if we had changed that Index this Lo is the one

changed that Index this Lo is the one that we could search on and we could

that we could search on and we could search

usuzan is that how you spell usbekistan hey I nailed it so that is how you use

hey I nailed it so that is how you use Lo and IO again I just wanted to show

Lo and IO again I just wanted to show you a little bit about how you can look

you a little bit about how you can look at your data frame or search within your

at your data frame or search within your data frame now in future videos I'm

data frame now in future videos I'm going to dive a lot deeper into a lot of

going to dive a lot deeper into a lot of the concepts that we just looked at

the concepts that we just looked at because I just kind of touched on them I

because I just kind of touched on them I wanted you to have a brief introduction

wanted you to have a brief introduction to them so that in future videos I'm not

to them so that in future videos I'm not just dropping everything on you all at

just dropping everything on you all at once so hopefully this was a good quick

once so hopefully this was a good quick introduction to those topics uh you

introduction to those topics uh you should be able to read in a file now see

should be able to read in a file now see your data frame and kind of look at it

your data frame and kind of look at it in a few different ways that we just

in a few different ways that we just looked at and I hope that that was

looked at and I hope that that was helpful and if it was be sure to check

helpful and if it was be sure to check out all my other videos on Python and

out all my other videos on Python and pandas and if you like this video be

pandas and if you like this video be sure to like And subscribe below and I

sure to like And subscribe below and I will see you in the next

will see you in the next [Music]

[Music] video

video [Music]

[Music] hello everybody today we're going to be

hello everybody today we're going to be looking at filtering and ordering data

looking at filtering and ordering data frames in pandas there are a lot of

frames in pandas there are a lot of different ways you can filter and order

different ways you can filter and order your data in pandas and I'm going to try

your data in pandas and I'm going to try to show you all of the main ways that

to show you all of the main ways that you can do that so let's kick it off by

you can do that so let's kick it off by importing our data set so we're going to

importing our data set so we're going to say data frame is equal to and we'll say

say data frame is equal to and we'll say pandas and I need to import my pandas so

pandas and I need to import my pandas so we'll say import pandas as p

we'll say import pandas as p that's pretty important I think um so

that's pretty important I think um so pd. read CSV and we'll do R and then

pd. read CSV and we'll do R and then we'll say the world population CSV so

we'll say the world population CSV so let's run this all our data frame right

let's run this all our data frame right here and this is the data frame that

here and this is the data frame that we're going to be filtering through and

we're going to be filtering through and ordering in pandas so let's kick it off

ordering in pandas so let's kick it off the first thing that we can do is filter

the first thing that we can do is filter based off of The Columns so the data

based off of The Columns so the data within our columns so Asia Europe Africa

within our columns so Asia Europe Africa or whatever data we may have in that

or whatever data we may have in that column let's go right down here we're

column let's go right down here we're going to say DF and then within it we're

going to say DF and then within it we're going to specify what column we're going

going to specify what column we're going to be filtering on so we're going to say

to be filtering on so we're going to say DF with another bracket and we'll say

DF with another bracket and we'll say rank so we're going to be looking at

rank so we're going to be looking at this rank column right here and we'll

this rank column right here and we'll say in that rank column we want to do

say in that rank column we want to do greater than 10 and that's actually

greater than 10 and that's actually going to be a lot of them let's do less

going to be a lot of them let's do less than so when we run this it's only going

than so when we run this it's only going to return these values that are less

to return these values that are less than 10 we can also do less than equal

than 10 we can also do less than equal to you know all of these um comparison

to you know all of these um comparison operators so less than or equal to so

operators so less than or equal to so now we have all of the ranks 1 through

now we have all of the ranks 1 through 10 now if we look at these countries we

10 now if we look at these countries we can specify by specific values almost

can specify by specific values almost exactly like we did here but instead of

exactly like we did here but instead of doing a comparison operator like we did

doing a comparison operator like we did right here and including those names

right here and including those names let's say Bangladesh and Brazil we can

let's say Bangladesh and Brazil we can use the is in function almost like an in

use the is in function almost like an in function in SQL if you know SQL so let's

function in SQL if you know SQL so let's go right down here and we're going to

go right down here and we're going to say specific underscore countries so

say specific underscore countries so right now we're just going to make a

right now we're just going to make a list of the countries that we want and

list of the countries that we want and then we'll say

Bangladesh and Brazil so let's go right down here and

Brazil so let's go right down here and we'll say okay for these specific

we'll say okay for these specific countries from the data frame let's do

countries from the data frame let's do our bracket we'll say in this country

our bracket we'll say in this country column so we'll do data frame and then

column so we'll do data frame and then another bracket for country so in this

another bracket for country so in this country column we can do do is in and

country column we can do do is in and then an open parenthesis and then look

then an open parenthesis and then look for our specific countries so we're

for our specific countries so we're looking at just this column and we're

looking at just this column and we're saying is in so we're looking at are

saying is in so we're looking at are these values within this column and

these values within this column and we're getting this error and this looks

we're getting this error and this looks very very odd let me um this doesn't

very very odd let me um this doesn't look right there we go I just had some

look right there we go I just had some syntax errors I apologize made it way

syntax errors I apologize made it way more complicated than it needs to be but

more complicated than it needs to be but here's how you use this is in function

here's how you use this is in function so we're looking at Bangladesh and

so we're looking at Bangladesh and Brazil and we return those rows with

Brazil and we return those rows with Bangladesh and Brazil really quickly I

Bangladesh and Brazil really quickly I wanted to give a huge shout out to the

wanted to give a huge shout out to the sponsor of this entire Panda series and

sponsor of this entire Panda series and that is udemy udemy has some of the best

that is udemy udemy has some of the best courses at the best prices and it is no

courses at the best prices and it is no exception when it comes to pandas

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courses if you want to master pandas this is the course that I would

this is the course that I would recommend it's going to teach you just

recommend it's going to teach you just about everything you need to know about

about everything you need to know about pandas so huge shout out to UD me for

pandas so huge shout out to UD me for sponsoring this Panda series and let's

sponsoring this Panda series and let's get back to the video we can also do a

get back to the video we can also do a contains function kind of similar to is

contains function kind of similar to is in except it's more like the like in SQL

in except it's more like the like in SQL as well I'm comparing a lot of this to

as well I'm comparing a lot of this to SQL CU When You're filtering things I

SQL CU When You're filtering things I always my brain always goes to SQL but

always my brain always goes to SQL but in pandas it's called the contains so

in pandas it's called the contains so let's do let's actually copy this

let's do let's actually copy this because I don't want to make the same

because I don't want to make the same mistake again let's do that and we'll do

mistake again let's do that and we'll do the bracket but instead of dot is in

the bracket but instead of dot is in we're going to do string. contains and

we're going to do string. contains and then an open parenthesis so we're going

then an open parenthesis so we're going to going to be looking for a string if

to going to be looking for a string if it contain if it contains let's do

it contain if it contains let's do United almost like United States or or

United almost like United States or or any other United so let's run this and

any other United so let's run this and as you can see we have United Arab

as you can see we have United Arab Emirates United Kingdom United States

Emirates United Kingdom United States United States Virgin Islands so we can

United States Virgin Islands so we can kind of search for a specific string or

kind of search for a specific string or a number or a value within our data or

a number or a value within our data or within that column of country now so far

within that column of country now so far we've only been looking at how you can

we've only been looking at how you can filter on these columns we can also fil

filter on these columns we can also fil filter based off of the index as well

filter based off of the index as well and there's two different ways you can

and there's two different ways you can do it or two of the main ways there's

do it or two of the main ways there's filter and then there's L and IO Lo

filter and then there's L and IO Lo stands for location and IO stands for

stands for location and IO stands for integer location and if you've seen

integer location and if you've seen other previous videos I've kind of

other previous videos I've kind of mentioned those so we can take a quick

mentioned those so we can take a quick look at all of those so really quickly

look at all of those so really quickly we need to set an index because the

we need to set an index because the index right now is uh not the best we'll

index right now is uh not the best we'll set our index to

set our index to Country so let's say

Country so let's say df2

df2 is equal to DF do setor index and we'll

is equal to DF do setor index and we'll say country I'm just doing df2 because

say country I'm just doing df2 because later on I want to use that data frame

later on I want to use that data frame again so I'm just going to assign it to

again so I'm just going to assign it to another data frame so that we can just

another data frame so that we can just easily switch back and forth so now we

easily switch back and forth so now we have this index as the country and what

have this index as the country and what we can do is use the filter function so

we can do is use the filter function so let's go down here we'll say

let's go down here we'll say df2

df2 filter and we'll do an open parenthesis

filter and we'll do an open parenthesis and now we can specify our items so

and now we can specify our items so these are actually going to be

these are actually going to be specifying which columns we want to keep

specifying which columns we want to keep so we're going to say items is equal to

so we're going to say items is equal to then we'll make a list we'll say

then we'll make a list we'll say continent hope that's how we spell

continent hope that's how we spell continent I'm always messing up with my

continent I'm always messing up with my uh my stuff here my spelling then we'll

uh my stuff here my spelling then we'll do CCA 3 because why not you can specify

do CCA 3 because why not you can specify whichever ones you want when we run this

whichever ones you want when we run this it's going to only bring in those two

it's going to only bring in those two columns Now by default it's choosing the

columns Now by default it's choosing the access for us but we can also specify

access for us but we can also specify which axis we want to search on so if we

which axis we want to search on so if we say axis is equal to zero it's actually

say axis is equal to zero it's actually going to search this axis this is the

going to search this axis this is the zero axis this is the one axis so where

zero axis this is the one axis so where our columns are is one so if we go back

our columns are is one so if we go back and do one we're searching on that one

and do one we're searching on that one Axis or those header axises again and

Axis or those header axises again and this is the default but you can specify

this is the default but you can specify that so if you just want to search on uh

that so if you just want to search on uh you know filtering right here you can do

you know filtering right here you can do that and let's actually copy this and do

that and let's actually copy this and do that right down here just you can see

that right down here just you can see what it looks like but let's search for

what it looks like but let's search for Zimbabwe and we'll do Zimbabwe and we'll

Zimbabwe and we'll do Zimbabwe and we'll be looking at the zero axis which is the

be looking at the zero axis which is the up and down on the left hand side and

up and down on the left hand side and when we filter on that we can filter by

when we filter on that we can filter by Zimbabwe by looking just at the country

Zimbabwe by looking just at the country index we can also use the like just like

index we can also use the like just like we did before and I'll show you the

we did before and I'll show you the exact same demonstration that we did

exact same demonstration that we did which you can say like is equal to and

which you can say like is equal to and instead of having to put in a concrete

instead of having to put in a concrete um text text you can just say United

um text text you can just say United just like we did before and we're

just like we did before and we're searching where the axis is equal to

searching where the axis is equal to zero which again is this left-handed

zero which again is this left-handed access so now we're looking for United

access so now we're looking for United and it's going to give us all of the

and it's going to give us all of the countries or all the indexed values that

countries or all the indexed values that have United in it like we were talking

have United in it like we were talking about before we also have l and ILO so

about before we also have l and ILO so we can say data frame 2. L now this is a

we can say data frame 2. L now this is a specific value so we'll do United States

specific value so we'll do United States so location is just looking at the

so location is just looking at the actual name or the value of it not its

actual name or the value of it not its position so if we search for United

position so if we search for United States it's going to give us this right

States it's going to give us this right here where it gives us all of the

here where it gives us all of the columns for United States and then all

columns for United States and then all of the uh values for United States or we

of the uh values for United States or we can

can do the io which is the energ location

do the io which is the energ location which is not the exact same because

which is not the exact same because we're looking at the string for the L

we're looking at the string for the L we're looking at this string but

we're looking at this string but underneath it there still is a position

underneath it there still is a position that's that integer location let's do a

that's that integer location let's do a completely random one let's just say

completely random one let's just say three if we look at the third position

three if we look at the third position it's going to give us ASM which I'm not

it's going to give us ASM which I'm not exactly sure what it is but it still

exactly sure what it is but it still gives us basically the same kind of

gives us basically the same kind of output which is the columns and the

output which is the columns and the values so that's another way that you

values so that's another way that you can search within your index when you're

can search within your index when you're actually trying to filter down that data

actually trying to filter down that data now let's go look at the order bu and

now let's go look at the order bu and let's start with the very first one that

let's start with the very first one that we looked at let's do data frame that's

we looked at let's do data frame that's why I kept it because I wanted to use it

why I kept it because I wanted to use it later now we can sort and order these

later now we can sort and order these values instead of it just being kind of

values instead of it just being kind of a jumbled mess in here we can sort these

a jumbled mess in here we can sort these columns however we would like ascending

columns however we would like ascending descending multiple columns single

descending multiple columns single columns and let's look at how to do that

columns and let's look at how to do that so we'll say data frame and then we'll

so we'll say data frame and then we'll do data frame look at rank again just

do data frame look at rank again just like we were doing above and let's do

like we were doing above and let's do data frame where it's less than 10 I

data frame where it's less than 10 I should have just gone and copyed this I

should have just gone and copyed this I apologize so now we have this data frame

apologize so now we have this data frame that is greater than 10 now we can do do

that is greater than 10 now we can do do sortore values and this is the function

sortore values and this is the function that's going to allow us to sort

that's going to allow us to sort everything that we want to sort so we

everything that we want to sort so we can do buy is equal to and we'll just

can do buy is equal to and we'll just order it by the exact same thing that we

order it by the exact same thing that we were doing or calling it on we'll do

were doing or calling it on we'll do rank so now what this is going to do

rank so now what this is going to do it's going to order our rank

it's going to order our rank column and as you can see it did that

column and as you can see it did that one 2 3 4 5 we can also do it with

one 2 3 4 5 we can also do it with ascending or descending so if you want

ascending or descending so if you want to you can look here and see what you

to you can look here and see what you can do so we'll do

can do so we'll do ascending we'll say that's equal to

ascending we'll say that's equal to true and so that's the automatic default

true and so that's the automatic default so that didn't change anything but if we

so that didn't change anything but if we say false it's going to be descending

say false it's going to be descending from highest to lowest so now we have it

from highest to lowest so now we have it in the opposite direction now we don't

in the opposite direction now we don't have to just order or sort this on one

have to just order or sort this on one single column we can do multiple columns

single column we can do multiple columns and we can do that by making a list

and we can do that by making a list right here whoops make a

right here whoops make a list just like that and we'll input

list just like that and we'll input different ones as well so now let's

different ones as well so now let's input our

input our country and when we run this it will

country and when we run this it will give us rank of

give us rank of 9876 as well as the country of Russia

9876 as well as the country of Russia Bangladesh Brazil now if you noticed the

Bangladesh Brazil now if you noticed the country really didn't change because the

country really didn't change because the rank stayed the exact same that's

rank stayed the exact same that's because there's an order of importance

because there's an order of importance here and it starts with the very first

here and it starts with the very first one if we change this around and we look

one if we change this around and we look at this

at this one and put a com right here

one and put a com right here now the country is going to be descended

now the country is going to be descended and the rank would come second so it's

and the rank would come second so it's not going the rank isn't going to really

not going the rank isn't going to really have any effect here so now we have the

have any effect here so now we have the country United States Russia Pakistan

country United States Russia Pakistan and the rank really didn't get ordered

and the rank really didn't get ordered at all now if we want to see how that

at all now if we want to see how that can actually work let's do continent

can actually work let's do continent right here and actually put it right

right here and actually put it right here and do country here so if we run

here and do country here so if we run this it's first going to come and it's

this it's first going to come and it's going to organize or sort the continent

going to organize or sort the continent then it's going to come come back and go

then it's going to come come back and go to the country and then it's going to

to the country and then it's going to sort the country so keep so keep your

sort the country so keep so keep your eye right here in this Asia area because

eye right here in this Asia area because we're going to sort this differently

we're going to sort this differently than ascending so we have ascending

than ascending so we have ascending false and that applies to both of these

false and that applies to both of these it's false and false but we can specify

it's false and false but we can specify which one we want to do we can do a

which one we want to do we can do a false here and a true here so we'll do

false here and a true here so we'll do false comma true and what this is going

false comma true and what this is going to do is it's going to say false for the

to do is it's going to say false for the continent so the continent right here is

continent so the continent right here is going to stay the exact same and so that

going to stay the exact same and so that is a lot of how you can filter and order

is a lot of how you can filter and order your data within pandas I hope that this

your data within pandas I hope that this was helpful I hope that you enjoyed this

was helpful I hope that you enjoyed this video if you liked it be sure to like

video if you liked it be sure to like And subscribe below check out all my

And subscribe below check out all my other videos on Python and pandas and I

other videos on Python and pandas and I will see you in the next

will see you in the next [Music]

[Music] video hello everybody today we're going

video hello everybody today we're going to be looking at indexing and pandas if

to be looking at indexing and pandas if you remember from previous videos the

you remember from previous videos the index is an object that stores the

index is an object that stores the access labels for all Panda objects the

access labels for all Panda objects the index in a data frame is extremely

index in a data frame is extremely useful because it's customizable and you

useful because it's customizable and you can also search and filter based off of

can also search and filter based off of that index in this video we're going to

that index in this video we're going to talk all about indexing how you can

talk all about indexing how you can change the index and customize that as

change the index and customize that as well as how you can search and filter on

well as how you can search and filter on that index and then we're also going to

that index and then we're also going to be looking at something a little bit

be looking at something a little bit more advanced called multi- indexing and

more advanced called multi- indexing and you won't always use it but it's really

you won't always use it but it's really good to know in case you come across a

good to know in case you come across a data frame that has that

data frame that has that so let's get started by importing pandas

so let's get started by importing pandas import pandas as PD now we'll get our

import pandas as PD now we'll get our first data frame we say DF is equal to

first data frame we say DF is equal to pd. read CSV and I've already copied

pd. read CSV and I've already copied this but we're going to do R and we're

this but we're going to do R and we're going to put this file path so I have

going to put this file path so I have this world population CSV I will have

this world population CSV I will have that in the description just like I do

that in the description just like I do in all of my other videos let's run DF

in all of my other videos let's run DF and let's take a look at this data frame

and let's take a look at this data frame so we have a lot of information here we

so we have a lot of information here we have rank country continent population

have rank country continent population as well as the default index from zero

as well as the default index from zero all the way up to 233 now if you haven't

all the way up to 233 now if you haven't watched any of my previous videos on

watched any of my previous videos on pandas the index is pretty important and

pandas the index is pretty important and it's basically just a number or a label

it's basically just a number or a label for each row it doesn't even necessarily

for each row it doesn't even necessarily have to be a unique number um you can

have to be a unique number um you can create or add an index yourself if you

create or add an index yourself if you want to and it doesn't have to be unique

want to and it doesn't have to be unique but it it really should be unique uh

but it it really should be unique uh especially if you want to use it

especially if you want to use it appropriately for what we're doing the

appropriately for what we're doing the country is actually going to be a pretty

country is actually going to be a pretty great index because the country you know

great index because the country you know is going to be all unique because we're

is going to be all unique because we're looking at every single row as a

looking at every single row as a different um country as well as the

different um country as well as the population so let's go ahead and create

population so let's go ahead and create this country or add this country as our

this country or add this country as our index now we can do this in a lot of

index now we can do this in a lot of different ways but the first way that

different ways but the first way that you can do this if you already know what

you can do this if you already know what you are going to create that index on is

you are going to create that index on is we can just go right in here when we're

we can just go right in here when we're reading in this file and we'll say comma

reading in this file and we'll say comma index underscore oops I I spelled that

index underscore oops I I spelled that completely wrong index uncore column and

completely wrong index uncore column and we'll say that is equal to and then

we'll say that is equal to and then we're going to say quote country so

we're going to say quote country so we're taking this country and we're

we're taking this country and we're going to assign it as the index now

going to assign it as the index now let's read this in and as you can see

let's read this in and as you can see this is our index now it looks a little

this is our index now it looks a little bit different we didn't have this

bit different we didn't have this country header right here which is

country header right here which is specifying that this is still the

specifying that this is still the country but you can tell that this is

country but you can tell that this is the index based off the um bold letters

the index based off the um bold letters as well as it being on the far left and

as well as it being on the far left and all the regular columns for the data is

all the regular columns for the data is over here while the country header is

over here while the country header is right here and it's lower than all the

right here and it's lower than all the others just a quick way that you can see

others just a quick way that you can see that that is the index now before we

that that is the index now before we move on I want to show you some other

move on I want to show you some other ways that you can do this as well but

ways that you can do this as well but I'm going to show you how to reverse

I'm going to show you how to reverse this index before we move on and we'll

this index before we move on and we'll say data frame so we had our data frame

say data frame so we had our data frame right here so we have data frame dot

right here so we have data frame dot we'll say reset index and then we'll say

we'll say reset index and then we'll say in place is equal to True which means we

in place is equal to True which means we don't have to assign this to another

don't have to assign this to another variable and all that stuff it'll just

variable and all that stuff it'll just be true so now when we run that data

be true so now when we run that data frame again the index was reset to the

frame again the index was reset to the default numbers so now let's go down

default numbers so now let's go down here I'll show you how to do this in a

here I'll show you how to do this in a different way you can do DF do we'll say

different way you can do DF do we'll say setor index and then we'll just say

setor index and then we'll just say country so very similar to when we were

country so very similar to when we were reading in that file and we said set the

reading in that file and we said set the index or that index column we said index

index or that index column we said index column equals country if we do this and

column equals country if we do this and we run it in it works but if we say data

we run it in it works but if we say data frame right down here it's not going to

frame right down here it's not going to save that if we want to save it just

save that if we want to save it just like we did above we're going to say in

like we did above we're going to say in place is equal to true that is going to

place is equal to true that is going to save it to where we don't have to assign

save it to where we don't have to assign it another variable so now when we run

it another variable so now when we run this the data frame right here which is

this the data frame right here which is going to populate this the data frame is

going to populate this the data frame is going to say in place is equal to true

going to say in place is equal to true so that country will now be our index

so that country will now be our index again let's run this and there we go

again let's run this and there we go really quickly I wanted to give a huge

really quickly I wanted to give a huge shout out to the sponsor of this entire

shout out to the sponsor of this entire panda series and that is udemy udemy has

panda series and that is udemy udemy has some of the best courses at the best

some of the best courses at the best prices and it is no exception when it

prices and it is no exception when it comes to pandas courses if you want to

comes to pandas courses if you want to master pandas this is the course that I

master pandas this is the course that I would recommend it's going to teach you

would recommend it's going to teach you just about everything you need to know

just about everything you need to know about pandas so huge shout out to UD to

about pandas so huge shout out to UD to me for sponsoring this Panda series and

me for sponsoring this Panda series and let's get back to the video now what's

let's get back to the video now what's really great about this index is we're

really great about this index is we're able to search based off just this index

able to search based off just this index and so we can filter on it and basically

and so we can filter on it and basically look through our data with it and there

look through our data with it and there are two different ways that you can do

are two different ways that you can do that at least this is a very common way

that at least this is a very common way that people who use pandas we'll do to

that people who use pandas we'll do to kind of search through that index the

kind of search through that index the first one is called lock and there's

first one is called lock and there's lock and iock that stands for location

lock and iock that stands for location or integer location let's look at lock

or integer location let's look at lock first let's say df.loc and then we'll do

first let's say df.loc and then we'll do a bracket now we're able to specify the

a bracket now we're able to specify the actual string the label so let's go

actual string the label so let's go right up here and let's say

right up here and let's say Albania so we'll say Albania so again

Albania so we'll say Albania so again this is just looking at the location

this is just looking at the location let's run this now it's going to bring

let's run this now it's going to bring up all the Albania data just like here

up all the Albania data just like here where it's kind of looks like a column

where it's kind of looks like a column in a column and we can get this exact

in a column and we can get this exact same data but using iock right here and

same data but using iock right here and when we ran lock we were searching based

when we ran lock we were searching based off Albania which is in the 01 position

off Albania which is in the 01 position so if we actually pull the one position

so if we actually pull the one position for that

for that integer the iock we can look at the one

integer the iock we can look at the one position and this should give us the

position and this should give us the exact same data now let's take a look at

exact same data now let's take a look at multi- indexing and we'll come back to a

multi- indexing and we'll come back to a little bit of this in a second so multi-

little bit of this in a second so multi- indexing is creating multiple indexes

indexing is creating multiple indexes we're not just going to create the

we're not just going to create the country as the index now we're going to

country as the index now we're going to add an additional index on top of that

add an additional index on top of that so let's pull up our data frame right

so let's pull up our data frame right now we have the country but let's do do

now we have the country but let's do do reset

reset index and we'll say in place equals true

index and we'll say in place equals true oops let's run it so now we have our

oops let's run it so now we have our data frame now let's set our index but

data frame now let's set our index but this time when we set our index we're

this time when we set our index we're going to add the country as the index as

going to add the country as the index as well as the continent as an index so

well as the continent as an index so we'll say data frame. setor index then

we'll say data frame. setor index then we'll do a parenthesis and instead of

we'll do a parenthesis and instead of just doing country like we did before

just doing country like we did before we're going to create a list oops and

we're going to create a list oops and we'll do it like that and then we'll

we'll do it like that and then we'll say oops

say oops continent and separate it by a comma so

continent and separate it by a comma so we have continents and Country let's

we have continents and Country let's just say in place is equal to true now

just say in place is equal to true now when we run this we're going to have two

when we run this we're going to have two indexes and let's see what this looks

indexes and let's see what this looks like and let's run this so now we have

like and let's run this so now we have country as well as continent as our

country as well as continent as our index now you may notice that these

index now you may notice that these indexes are repeating themselves on this

indexes are repeating themselves on this continent index we have Europe right

continent index we have Europe right here and Europe right here as well as

here and Europe right here as well as Asia and Asia and it looks a little bit

Asia and Asia and it looks a little bit funky but we are able to sort these

funky but we are able to sort these values and make they look a lot better

values and make they look a lot better so let's go ahead and try this we'll do

so let's go ahead and try this we'll do DF do sortore index and when we run this

DF do sortore index and when we run this it should sort our index alphabetically

it should sort our index alphabetically and we can also look in here and see

and we can also look in here and see what kind of things we can you know

what kind of things we can you know specify we can specify the axis but it's

specify we can specify the axis but it's automatically going to be looking at the

automatically going to be looking at the zero this is zero and this is one so we

zero this is zero and this is one so we have two axes within our data frame you

have two axes within our data frame you can choose the level whether it's

can choose the level whether it's ascending or not ascending in place kind

ascending or not ascending in place kind string sort remaining all of these

string sort remaining all of these different things the only one that I

different things the only one that I really you know think is worth looking

really you know think is worth looking at is the ascending we already know some

at is the ascending we already know some of these other ones but if we look at

of these other ones but if we look at ascending let's run it now it's sorted

ascending let's run it now it's sorted these and so now it's kind of grouped

these and so now it's kind of grouped together so we have Africa and all the

together so we have Africa and all the African ones as well as South America

African ones as well as South America and all the South American ones let's

and all the South American ones let's really quickly say

really quickly say pd. setor

pd. setor option and we'll say display.

option and we'll say display. max. columns and just like this let's

max. columns and just like this let's run it and I need to specify whoops

run it and I need to specify whoops specify right here let's see how many

specify right here let's see how many rows we

rows we have 235 so let's do

have 235 so let's do 235 let's run this and now when we run

235 let's run this and now when we run this you can see that Africa is all

this you can see that Africa is all grouped together and all the countries

grouped together and all the countries are in alphabetical order under it and

are in alphabetical order under it and then we go all the way down to Asia and

then we go all the way down to Asia and again just all in alphabetical order if

again just all in alphabetical order if we wanted to we could say

we wanted to we could say ascending equals

ascending equals true and then when we run this oh I

true and then when we run this oh I meant to say false and then when we run

meant to say false and then when we run this it's the exact opposite so it

this it's the exact opposite so it starts with South America the last one

starts with South America the last one and then goes in reverse alphabetical

and then goes in reverse alphabetical order we could also say false make it a

order we could also say false make it a list and do comma

list and do comma true and just like this and then it

true and just like this and then it would sort this First Column as false

would sort this First Column as false and this next column as true so you can

and this next column as true so you can really customize it but you know for

really customize it but you know for what we're doing we don't need any of

what we're doing we don't need any of that we just need to be able to see this

that we just need to be able to see this right here so now when we try to search

right here so now when we try to search by our index like we did before we did

by our index like we did before we did data frame. Loke now when we did that

data frame. Loke now when we did that and we said you know let's say Angola

and we said you know let's say Angola when we specified Angola it's not going

when we specified Angola it's not going to work properly because it's searching

to work properly because it's searching in this first index for the first string

in this first index for the first string that we have we can search Africa

that we have we can search Africa let's search for

let's search for Africa and now we have all of the

Africa and now we have all of the African countries and if we want to

African countries and if we want to specify to Angola we can also go down

specify to Angola we can also go down another level oops by doing Ang

another level oops by doing Ang Angola and now we have what we were

Angola and now we have what we were looking at before where we're calling

looking at before where we're calling all the data within those but we

all the data within those but we couldn't do it just based off Africa

couldn't do it just based off Africa because we had an additional Index right

because we had an additional Index right here so once we called both indexes now

here so once we called both indexes now we get this view but let's look at that

we get this view but let's look at that I look really

I look really quick when we run this let's just say

quick when we run this let's just say one because right up here oh we have

one because right up here oh we have Angola zero and then one so you think it

Angola zero and then one so you think it may pull up Angola let's go ahead and

may pull up Angola let's go ahead and run this and it's still pulling up

run this and it's still pulling up Albania let's go right up here if you

Albania let's go right up here if you remember when we didn't have the

remember when we didn't have the multiple indexes it was pulling up

multiple indexes it was pulling up Albania the difference when you're doing

Albania the difference when you're doing these multi- indexes is that the the L

these multi- indexes is that the the L is able to specify this whereas this one

is able to specify this whereas this one does not go based off that multi-

does not go based off that multi- indexing it's going to go based off the

indexing it's going to go based off the initial index or the integer based index

initial index or the integer based index so that's a lot about indexing in pandas

so that's a lot about indexing in pandas we'll cover even a few more things in

we'll cover even a few more things in future videos as we get more and more

future videos as we get more and more into pandas but this is a lot of what

into pandas but this is a lot of what indexing looks like within pandas and

indexing looks like within pandas and again super important to learn how to do

again super important to learn how to do and know how to do because it's a pretty

and know how to do because it's a pretty important building block as we go

important building block as we go through this Panda series so I hope you

through this Panda series so I hope you enjoyed this video on indexing if you

enjoyed this video on indexing if you did be sure to like And subscribe below

did be sure to like And subscribe below and I will see you in the next

and I will see you in the next [Music]

[Music] video hello everybody today we're going

video hello everybody today we're going to be taking a look at the group by

to be taking a look at the group by function and aggregating within panas

function and aggregating within panas group I is going to group together the

group I is going to group together the values in a column and display them all

values in a column and display them all on the same row and this allows you to

on the same row and this allows you to perform aggregate functions on those

perform aggregate functions on those groupings so let's start reading in our

groupings so let's start reading in our data and take a look so we're going to

data and take a look so we're going to do import pandas as

do import pandas as PD and then we're going to say our data

PD and then we're going to say our data frame is equal to and we'll say pd. read

frame is equal to and we'll say pd. read CSV we'll do an open parenthesis R and

CSV we'll do an open parenthesis R and our file path and we're going to be

our file path and we're going to be looking at the flavors CSV right here so

looking at the flavors CSV right here so right here we have our flavor of ice

right here we have our flavor of ice cream we have our base flavor flavor

cream we have our base flavor flavor whether it was vanilla or chocolate

whether it was vanilla or chocolate whether I liked it or not the flavor

whether I liked it or not the flavor rating texture rating and its overall or

rating texture rating and its overall or its total rating now these are all my

its total rating now these are all my own personal scores so you know I've

own personal scores so you know I've spent years researching this so these

spent years researching this so these are all very accurate but this should be

are all very accurate but this should be a low stress environment to learn Group

a low stress environment to learn Group by and the aggregate functions so the

by and the aggregate functions so the first thing that we can do is look at

first thing that we can do is look at our group by now you can't Group by well

our group by now you can't Group by well you can you can Group by flavor but as

you can you can Group by flavor but as you can see these are all unique values

you can see these are all unique values what we need is something that has

what we need is something that has duplicate values or or similar values on

duplicate values or or similar values on different rows that'll group together so

different rows that'll group together so this base flavor is actually a perfect

this base flavor is actually a perfect one to group it on and we'll do that by

one to group it on and we'll do that by saying DF do group by do an open

saying DF do group by do an open parenthesis and we'll just specify base

parenthesis and we'll just specify base flavor and this will then group together

flavor and this will then group together those values and I need to make sure I

those values and I need to make sure I can spell properly this will group those

can spell properly this will group those flavors together so let's run this and

flavors together so let's run this and as you can see it actually is its own

as you can see it actually is its own object so it has a group by data frame

object so it has a group by data frame Group by object so now that we've

Group by object so now that we've grouped them let's give it a variable so

grouped them let's give it a variable so we'll say group underscore byor frame

we'll say group underscore byor frame let's say that's equal to Let's copy

let's say that's equal to Let's copy this we'll run it and now what we need

this we'll run it and now what we need to do is run our aggregations in order

to do is run our aggregations in order to get an output so we're going to say

to get an output so we're going to say mean and that's all we're going to put

mean and that's all we're going to put just for now just to get an output that

just for now just to get an output that we can take a look off and then we'll

we can take a look off and then we'll build from there so let's go ahead and

build from there so let's go ahead and run this and right here we have our base

run this and right here we have our base flavor which is now saying is the index

flavor which is now saying is the index of chocolate or vanilla and then it's

of chocolate or vanilla and then it's taking the mean or the average of all

taking the mean or the average of all the columns that have integers notice

the columns that have integers notice that it did not take the liked column

that it did not take the liked column and it did not take the flavor column

and it did not take the flavor column because those are strings and they

because those are strings and they cannot aggregate those and we'll take a

cannot aggregate those and we'll take a look at that later but it took all the

look at that later but it took all the values that have integers and then it

values that have integers and then it gave us the average of those ratings

gave us the average of those ratings really quickly I wanted to give a huge

really quickly I wanted to give a huge shout out to the sponsor of this entire

shout out to the sponsor of this entire Panda series and that is udemy udemy has

Panda series and that is udemy udemy has some of the best courses at the best

some of the best courses at the best prices and it is no exception when it

prices and it is no exception when it comes to pandas courses if you want to

comes to pandas courses if you want to master pandas this is the course that I

master pandas this is the course that I would recommend it's going to teach you

would recommend it's going to teach you just about everything you need to know

just about everything you need to know about pandas so huge shout out to UD me

about pandas so huge shout out to UD me for sponsoring this Panda series and

for sponsoring this Panda series and let's get back to the video so right off

let's get back to the video so right off the bat as averages with chocolate I

the bat as averages with chocolate I have a much higher rating overall than

have a much higher rating overall than the ones with vanilla bases now we can

the ones with vanilla bases now we can actually combine all of this together

actually combine all of this together into one line and we can do something

into one line and we can do something like this so we'll

like this so we'll say DF do group by and we'll say mean

say DF do group by and we'll say mean just like this and this will actually

just like this and this will actually run it before we didn't have any

run it before we didn't have any aggregating function on there so it

aggregating function on there so it didn't run but now that we combine it

didn't run but now that we combine it all into one it will run properly now

all into one it will run properly now there are a lot of different aggregate

there are a lot of different aggregate functions but I'm going to show you some

functions but I'm going to show you some of the most popular ones or the most

of the most popular ones or the most common ones that you will see so let's

common ones that you will see so let's copy this right here so we can do dot

copy this right here so we can do dot count and when we run this we can look

count and when we run this we can look at the count and this will show us the

at the count and this will show us the actual count of the rows that were

actual count of the rows that were aggregated so for chocolate we have

aggregated so for chocolate we have three so there going to be three all the

three so there going to be three all the way across and for vanilla we had six so

way across and for vanilla we had six so we're looking at a higher count of

we're looking at a higher count of vanilla which if you're comparing it to

vanilla which if you're comparing it to this mean up here that could be a big

this mean up here that could be a big skew towards the chocolate because if

skew towards the chocolate because if you have one or two good chocolates it

you have one or two good chocolates it could really pull the numbers up whereas

could really pull the numbers up whereas if you had two good vanillas but all the

if you had two good vanillas but all the other ones were bad it pulls that

other ones were bad it pulls that average down so knowing the count of

average down so knowing the count of something something is really

something something is really good let's take a look at the next one

good let's take a look at the next one and we can do Min and Max and I'll just

and we can do Min and Max and I'll just run these really quickly we can do Min

run these really quickly we can do Min and when we run this the first thing

and when we run this the first thing that you should notice is that it now

that you should notice is that it now has a flavor and a liked column and

has a flavor and a liked column and that's because Min and Max will actually

that's because Min and Max will actually look at the first letter in the string

look at the first letter in the string or the first set of letters if there are

or the first set of letters if there are um you know chocolate something it'll

um you know chocolate something it'll look at the first and then it'll

look at the first and then it'll actually populate it so chocolate with

actually populate it so chocolate with the CH chocolate is the very first or

the CH chocolate is the very first or the minimum value for that string and

the minimum value for that string and for a cake batter that is the minimum

for a cake batter that is the minimum value in vanilla as well now with the

value in vanilla as well now with the liked it's interesting because

liked it's interesting because apparently I liked all the chocolate

apparently I liked all the chocolate ones I'm going to go take a look so

ones I'm going to go take a look so chocolate I liked chocolate I liked

chocolate I liked chocolate I liked chocolate I lik so there is no no option

chocolate I lik so there is no no option in this liked column so yes was the only

in this liked column so yes was the only option and now let's look at Max

option and now let's look at Max whoops and it should do the exact

whoops and it should do the exact opposite which is going to take the

opposite which is going to take the highest value even if it's a string so

highest value even if it's a string so Rocky Road the letter r comes later in

Rocky Road the letter r comes later in the alphabet so that's what it's looking

the alphabet so that's what it's looking at and so does vanilla and then we have

at and so does vanilla and then we have yes as well and then of course right

yes as well and then of course right here it's taking the max value so before

here it's taking the max value so before when we were looking at Min I just

when we were looking at Min I just focused on those but it still does the

focused on those but it still does the exact same thing to these integer um

exact same thing to these integer um columns as well so for the max value for

columns as well so for the max value for vanilla it was mint chocolate chip that

vanilla it was mint chocolate chip that was our base so I had a rating of 10 for

was our base so I had a rating of 10 for this vanilla row or grouping and then we

this vanilla row or grouping and then we can also look at the sum

can also look at the sum and there are all the sums for these and

and there are all the sums for these and again it only does integer because we

again it only does integer because we can't add the strings here are the sum

can't add the strings here are the sum or the total values for all of them and

or the total values for all of them and for the total values since we had you

for the total values since we had you know six rows that were grouping into

know six rows that were grouping into this vanilla we now have a lot of a much

this vanilla we now have a lot of a much higher score for vanilla now that's a

higher score for vanilla now that's a really simple way to do your

really simple way to do your aggregations but there is actually an

aggregations but there is actually an aggregation function and let's take a

aggregation function and let's take a look at this CU this is um a little bit

look at this CU this is um a little bit more complex although when I write it

more complex although when I write it out or show you hope it makes a lot of

out or show you hope it makes a lot of sense we can do a so this is our

sense we can do a so this is our aggregate function and what we need to

aggregate function and what we need to pass into our aggregate function is

pass into our aggregate function is actually a dictionary so let's do an

actually a dictionary so let's do an open parenthesis and we're going to do a

open parenthesis and we're going to do a squiggly bracket and then we need to

squiggly bracket and then we need to specify what we're going to be

specify what we're going to be aggregating on or what column so let's

aggregating on or what column so let's do this flavor rating let's copy this

do this flavor rating let's copy this we'll do flavor rating and I need to put

we'll do flavor rating and I need to put that as a

that as a string and then we'll do a colon and now

string and then we'll do a colon and now we can specify what what aggregate

we can specify what what aggregate functions we want so we've done sum

functions we want so we've done sum count mean Min and Max all of those and

count mean Min and Max all of those and we can actually put all of those into

we can actually put all of those into here and perform all of those

here and perform all of those aggregations on just one column so let's

aggregations on just one column so let's make a list and then let's say

make a list and then let's say mean

mean Max count and uh what's another one sum

Max count and uh what's another one sum so let's do all four of those only on

so let's do all four of those only on this flavor rating

this flavor rating column and when we run this we have our

column and when we run this we have our base flavor right here chocolate and

base flavor right here chocolate and vanilla but now we don't have multiple

vanilla but now we don't have multiple columns we have one column with multiple

columns we have one column with multiple Columns of our aggregations and it is

Columns of our aggregations and it is possible to pass in multiple columns

possible to pass in multiple columns like that so we'll do texture

like that so we'll do texture rating and we'll just come right here

rating and we'll just come right here and do a comma then we'll say uh uh

and do a comma then we'll say uh uh texture

texture rating and then a colon I don't know why

rating and then a colon I don't know why I spelled it out when I copied it but I

I spelled it out when I copied it but I did and then we'll do the exact same

did and then we'll do the exact same ones and now when we run it we're

ones and now when we run it we're getting the exact same columns mean Max

getting the exact same columns mean Max count and sum for flavor rating then

count and sum for flavor rating then mean Max count and sum for our texture

mean Max count and sum for our texture rating now so far we've only grouped on

rating now so far we've only grouped on one column but we can actually group on

one column but we can actually group on multiple columns let's go back up here

multiple columns let's go back up here to our data and I should have just

to our data and I should have just copied this down here let's go back down

copied this down here let's go back down and just look at this so really we only

and just look at this so really we only grouped it on this base flavor but you

grouped it on this base flavor but you can do multiple groupings or group by

can do multiple groupings or group by multiple columns so let's do our base

multiple columns so let's do our base flavor which we did already as well as

flavor which we did already as well as the liked column so we're going to say

the liked column so we're going to say DF dog Group by then we'll do an open

DF dog Group by then we'll do an open parenthesis and then instead of just

parenthesis and then instead of just passing through one string we're going

passing through one string we're going to do a list and we'll say base

to do a list and we'll say base flavor oops comma and then we'll do

flavor oops comma and then we'll do liked so now when it groups this it

liked so now when it groups this it should put put two groupings and let's

should put put two groupings and let's run this and just see oops I got to say

run this and just see oops I got to say let's just do

let's just do mean so now we have our chocolate and a

mean so now we have our chocolate and a vanilla and remember chocolate only had

vanilla and remember chocolate only had yes so that's the only one that it's

yes so that's the only one that it's going to group on but vanilla had a no

going to group on but vanilla had a no and a yes so if we look at the vanilla

and a yes so if we look at the vanilla we have our base flavor vanilla and then

we have our base flavor vanilla and then within liked we have no and a yes which

within liked we have no and a yes which can show us that within our vanilla when

can show us that within our vanilla when we group on these our NOS were really

we group on these our NOS were really low

low but our yeses were really high we

but our yeses were really high we actually had a pretty similar rating or

actually had a pretty similar rating or very close to the same rating as the

very close to the same rating as the ones we really liked in chocolate and

ones we really liked in chocolate and just like we did above we can take this

just like we did above we can take this doag and I'm going to copy this and

doag and I'm going to copy this and it'll perform it on each of those rows

it'll perform it on each of those rows let me close that and what did I do

let me close that and what did I do wrong oh I need the squiggly

wrong oh I need the squiggly bracket and it'll show us each of those

bracket and it'll show us each of those so the mean Max count and sum for all of

so the mean Max count and sum for all of the chocolate and vanilla as well as the

the chocolate and vanilla as well as the groupings of light yes and no now after

groupings of light yes and no now after we've looked at all that and that's how

we've looked at all that and that's how I usually do it there is one uh shortcut

I usually do it there is one uh shortcut function that can give you some of these

function that can give you some of these things just really quickly and so let's

things just really quickly and so let's go back up here and take this it's just

go back up here and take this it's just called describe um and if you've ever

called describe um and if you've ever done it it's just going to give you some

done it it's just going to give you some high level overview of some of those

high level overview of some of those different aggregations so let's run this

different aggregations so let's run this and it's going to give us our chocolate

and it's going to give us our chocolate and vanilla and within each column it's

and vanilla and within each column it's going to give us our count our mean our

going to give us our count our mean our standard deviation I believe is what

standard deviation I believe is what that is our minimum 25% 50 75 and 100

that is our minimum 25% 50 75 and 100 which is our Max then our count and our

which is our Max then our count and our means so a lot of those aggregate

means so a lot of those aggregate functions but the describe is you know a

functions but the describe is you know a very generalized um function we can't

very generalized um function we can't get as specific as we were with the

get as specific as we were with the previous ones that we were looking at

previous ones that we were looking at but I just wanted to throw this out

but I just wanted to throw this out there in case this is something that

there in case this is something that you'd be interested in because it you

you'd be interested in because it you know technically is showing a lot of

know technically is showing a lot of those aggregate functions just you know

those aggregate functions just you know all at one time so that is our group Buy

all at one time so that is our group Buy and aggregate functions within pandas I

and aggregate functions within pandas I hope that that was helpful I hope that

hope that that was helpful I hope that you understood you know everything that

you understood you know everything that we were working on if you like this

we were working on if you like this video be sure to like And subscribe and

video be sure to like And subscribe and check out all my other videos on python

check out all my other videos on python as well as pandas and I will see you in

as well as pandas and I will see you in the next

the next [Music]

[Music] video hello everybody today we're going

video hello everybody today we're going to be talking about merging joining and

to be talking about merging joining and concatenating data frames in p do this

concatenating data frames in p do this whole video is basically around being

whole video is basically around being able to combine two separate data frames

able to combine two separate data frames together into one data frame these are

together into one data frame these are really important to understand when

really important to understand when we're actually using the merge and the

we're actually using the merge and the join right here we have what's called an

join right here we have what's called an inner join and the Shaded part is what's

inner join and the Shaded part is what's going to be returned it's only the

going to be returned it's only the things that are in both the left and the

things that are in both the left and the right data frames then we have an outer

right data frames then we have an outer join or a full outer join and this will

join or a full outer join and this will take all the data from the left data

take all the data from the left data frame and the right data frame and

frame and the right data frame and everything that is similar so basically

everything that is similar so basically just takes everything we also have a

just takes everything we also have a left join which is going to take

left join which is going to take everything from the left and then if

everything from the left and then if there's anything that's similar it'll

there's anything that's similar it'll also include that and then the exact

also include that and then the exact opposite of that is the right join which

opposite of that is the right join which is going to give us everything from the

is going to give us everything from the right data frame and it's going to give

right data frame and it's going to give us everything that is similar but it's

us everything that is similar but it's not going to give us anything that is

not going to give us anything that is just unique to the left data frame so

just unique to the left data frame so this is just for reference because in a

this is just for reference because in a little bit when we start merging these

little bit when we start merging these these become very important so I just

these become very important so I just wanted to kind of show you how that

wanted to kind of show you how that works visually so let's get started by

works visually so let's get started by pulling in our files so first we're

pulling in our files so first we're going to say import and is aspd we'll

going to say import and is aspd we'll run this and then we'll say data frame

run this and then we'll say data frame one and we'll also have a data frame two

one and we'll also have a data frame two and these are the different data frames

and these are the different data frames the left and the right data frame that

the left and the right data frame that we'll be using to join merge and

we'll be using to join merge and concatenate so we'll say data frame 1 is

concatenate so we'll say data frame 1 is equal to pd. CSV read and we'll do R and

equal to pd. CSV read and we'll do R and here is our file path so we have this

here is our file path so we have this lr. CSV that's our Lord of the Rings CSV

lr. CSV that's our Lord of the Rings CSV and let's call that really quickly so we

and let's call that really quickly so we can see what's in there and I'm having a

can see what's in there and I'm having a dyslexic moment uh because it's supposed

dyslexic moment uh because it's supposed to be reor CSV uh I apologize for that

to be reor CSV uh I apologize for that but this is our data frame this is our

but this is our data frame this is our data frame one we have three columns

data frame one we have three columns it's their Fellowship ID 10001 2 3 and

it's their Fellowship ID 10001 2 3 and four their first name froto Sam wiise

four their first name froto Sam wiise gelf and Pippen and their skills hide

gelf and Pippen and their skills hide and gardening spells and fireworks so

and gardening spells and fireworks so this is our very first data frame that

this is our very first data frame that we're going to be working with let's go

we're going to be working with let's go down a little bit let's pull this down

down a little bit let's pull this down here and we're just going to say data

here and we're just going to say data Frame 2 Data Frame 2 and this is the

Frame 2 Data Frame 2 and this is the Lord of the Rings 2 so let's pull this

Lord of the Rings 2 so let's pull this one in now as you can see it's very

one in now as you can see it's very similar we have Fellowship ID 1 2 6 7 8

similar we have Fellowship ID 1 2 6 7 8 so we have three different IDs here we

so we have three different IDs here we don't have six seven and eight in this

don't have six seven and eight in this upper this First Data frame we also have

upper this First Data frame we also have the first name so froto and Sam or Sam

the first name so froto and Sam or Sam wise are in the very first and the

wise are in the very first and the second data frame but now we have three

second data frame but now we have three new people barir Eland and legalis and

new people barir Eland and legalis and now we have this age column which again

now we have this age column which again is unique to just this second data frame

is unique to just this second data frame really quickly I want to give a huge

really quickly I want to give a huge shout out to the sponsor of this video

shout out to the sponsor of this video and that is zendesk I've been using

and that is zendesk I've been using zenes for my company's customer

zenes for my company's customer analytics and it has been absolutely

analytics and it has been absolutely phenomenal they're going to be hosting a

phenomenal they're going to be hosting a conference called zenes relate on May

conference called zenes relate on May 10th and they're going to talk all about

10th and they're going to talk all about customer analytics chat Bots and AI in

customer analytics chat Bots and AI in this space you can attend in person in

this space you can attend in person in San Francisco or you can attend

San Francisco or you can attend virtually but space is limited so be

virtually but space is limited so be sure to apply if you want to attend so

sure to apply if you want to attend so if you are a business leader and you

if you are a business leader and you want to make most out of your customer

want to make most out of your customer data or you want to learn customer data

data or you want to learn customer data analytics I will leave links in the

analytics I will leave links in the description again huge shout out to

description again huge shout out to zendesk for sponsoring this video now

zendesk for sponsoring this video now the first one that I want to look at is

the first one that I want to look at is merge and I want to look at merge first

merge and I want to look at merge first because I think this one is the most

because I think this one is the most important I use this one more than any

important I use this one more than any of ones that we're going to talk about

of ones that we're going to talk about today the merge is just like the joins

today the merge is just like the joins that we were just looking at the outer

that we were just looking at the outer the inner the left and the right and

the inner the left and the right and there's also one called cross and I'll

there's also one called cross and I'll show you that one although if I'm being

show you that one although if I'm being honest I don't really use that one that

honest I don't really use that one that much but It's Worth showing just in case

much but It's Worth showing just in case you come into a scenario where you do

you come into a scenario where you do want to do that so let's go right down

want to do that so let's go right down here and I want to be able to see these

here and I want to be able to see these while we do it so we're going to say

while we do it so we're going to say data frame one and when we specify data

data frame one and when we specify data frame one as the very first data frame

frame one as the very first data frame we say datf frame. merge this is

we say datf frame. merge this is automatically going to be our left data

automatically going to be our left data frame then if we do our parentheses

frame then if we do our parentheses right here and we say data Frame 2 this

right here and we say data Frame 2 this is our right data frame and let's see

is our right data frame and let's see what happens when we do this

what happens when we do this so what it's going to do and this we

so what it's going to do and this we didn't specify this it's just a default

didn't specify this it's just a default it's going to do an inner join so it's

it's going to do an inner join so it's only going to give us an output where

only going to give us an output where specific values or the keys are the same

specific values or the keys are the same now you can't see this but what is

now you can't see this but what is happening is is it's taking this

happening is is it's taking this Fellowship ID and saying I have 101 here

Fellowship ID and saying I have 101 here a 102 here this is the exact same as up

a 102 here this is the exact same as up here with this Fellowship ID and

here with this Fellowship ID and fellowship ID of 101 and 2 but when we

fellowship ID of 101 and 2 but when we look at 13 and 4 those aren't in this

look at 13 and 4 those aren't in this right right data frame and 678 is not in

right right data frame and 678 is not in this left data frame so the only ones

this left data frame so the only ones that match are this 101 and two and

that match are this 101 and two and that's why they get pulled in down here

that's why they get pulled in down here but because we didn't explicitly say

but because we didn't explicitly say here's what I want to join or merge

here's what I want to join or merge between these two data frames it

between these two data frames it actually is looking at the fellowship ID

actually is looking at the fellowship ID and the first name so it's taking in

and the first name so it's taking in these unique values of froto and Sam

these unique values of froto and Sam wise which are the same in both which is

wise which are the same in both which is why I pulled it over but really quickly

why I pulled it over but really quickly let's just check and make sure that we

let's just check and make sure that we did it on the inner join because again

did it on the inner join because again we didn't specify anything that was just

we didn't specify anything that was just the default so we're going to say how is

the default so we're going to say how is equal to and then we'll say iner and if

equal to and then we'll say iner and if we run this it's going to be the exact

we run this it's going to be the exact same because again the inner is the

same because again the inner is the default but now just to show you how

default but now just to show you how it's kind of joining these two uh data

it's kind of joining these two uh data frames together I'm going to say on is

frames together I'm going to say on is equal to and then I'm only going to put

equal to and then I'm only going to put Fellowship ID so let's run this now the

Fellowship ID so let's run this now the first thing that you make may have

first thing that you make may have noticed is this first name undor X and

noticed is this first name undor X and this first name uncore Y what the merge

this first name uncore Y what the merge does as kind of a default is when you

does as kind of a default is when you were only joining on a fellowship ID we

were only joining on a fellowship ID we have this right data frame with

have this right data frame with Fellowship ID the left data frame with

Fellowship ID the left data frame with the fellowship ID if you're just joining

the fellowship ID if you're just joining on these and you're not joining on the

on these and you're not joining on the first name and the first name then it's

first name and the first name then it's going to separate those into an

going to separate those into an underscore X and an underscore Y and

underscore X and an underscore Y and even though they have the exact same

even though they have the exact same values since we are not merging on that

values since we are not merging on that column it automatically separates that

column it automatically separates that into two separate columns so we can see

into two separate columns so we can see the values within each of those columns

the values within each of those columns if we went into this on and we make a

if we went into this on and we make a list and let's do it like that and we

list and let's do it like that and we say comma and then we write first name

say comma and then we write first name oops first name and then we run this

oops first name and then we run this it's going to look exactly like it did

it's going to look exactly like it did before again it automatically pulled in

before again it automatically pulled in both of these columns when it was

both of these columns when it was merging at the first time even though we

merging at the first time even though we didn't write anything but if we actually

didn't write anything but if we actually write this this it's doing exactly what

write this this it's doing exactly what it was doing when we just had df2 we're

it was doing when we just had df2 we're just now writing it out now there are

just now writing it out now there are other arguments that we can pass into

other arguments that we can pass into this merge function let's hit shift Tab

this merge function let's hit shift Tab and let's scroll down here so within

and let's scroll down here so within this merge function we have a lot of

this merge function we have a lot of different arguments that you can pass

different arguments that you can pass into it first we have this right which

into it first we have this right which is the right data frame which is this

is the right data frame which is this data frame two then we have the how and

data frame two then we have the how and the on which we've already shown how to

the on which we've already shown how to do there's a left on right on left Index

do there's a left on right on left Index right index not something you'll

right index not something you'll probably use that much but you

probably use that much but you definitely can if you want to look into

definitely can if you want to look into that and there's all these doc strings

that and there's all these doc strings which show you exactly how to use all of

which show you exactly how to use all of these so if you're interest in looking

these so if you're interest in looking at the left and the right and the left

at the left and the right and the left index it's all in here the one that is

index it's all in here the one that is really good is the sort and you can sort

really good is the sort and you can sort it saying either it's false or true then

it saying either it's false or true then we have these suffixes now if you

we have these suffixes now if you remember when we took these out what it

remember when we took these out what it automatically did was it put in these

automatically did was it put in these underscore X and underscore y you can

underscore X and underscore y you can customize that and you can put in what

customize that and you can put in what whatever you'd like instead of the

whatever you'd like instead of the underscore X andore Y you can put in

underscore X andore Y you can put in some custom um string for that we also

some custom um string for that we also have an indicator and a validates again

have an indicator and a validates again all things you can go in here and look

all things you can go in here and look at I'm just going to show you the stuff

at I'm just going to show you the stuff that I use the most so these things

that I use the most so these things right here are things that I definitely

right here are things that I definitely use the most so now that we've looked at

use the most so now that we've looked at the inner join let's copy this right

the inner join let's copy this right down here and let's look at the outer

down here and let's look at the outer join and these get a little bit more

join and these get a little bit more tricky I think the inner join is

tricky I think the inner join is probably the easiest one to understand

probably the easiest one to understand well look at the outer is spelled o u t

well look at the outer is spelled o u t e r i I don't know why I always want to

e r i I don't know why I always want to say o t t r but let's run this and see

say o t t r but let's run this and see what we get so now this looks quite

what we get so now this looks quite different the inner join only gave us

different the inner join only gave us the values that are the exact same this

the values that are the exact same this one is going to give us all of the

one is going to give us all of the values regardless of if they are the

values regardless of if they are the same so we have 1 2 3 4 six seven and

same so we have 1 2 3 4 six seven and eight so let's scroll back up here so we

eight so let's scroll back up here so we have 1 2 3 4 1 two and six s and 8 so we

have 1 2 3 4 1 two and six s and 8 so we don't have a 105 and then if you notice

don't have a 105 and then if you notice in this data frame right here if the

in this data frame right here if the value doesn't have so if we can't join

value doesn't have so if we can't join on the fellowship ID or the first name

on the fellowship ID or the first name like legalis wasn't one that we joined

like legalis wasn't one that we joined on or that has a similar value in the

on or that has a similar value in the left data frame it just gives us an N

left data frame it just gives us an N which is not a number and it's going to

which is not a number and it's going to do that for any value where it couldn't

do that for any value where it couldn't find that join or it couldn't match uh

find that join or it couldn't match uh something within that either ID or first

something within that either ID or first name so in age we also have that for the

name so in age we also have that for the ones that weren't in the right data

ones that weren't in the right data frame we only had 101 and 102 so we'll

frame we only had 101 and 102 so we'll have the age for both froto and Sam but

have the age for both froto and Sam but for Gandalf and Pippen we don't have

for Gandalf and Pippen we don't have their corresponding IDs and so it's just

their corresponding IDs and so it's just going to be blank for Gandalf and Pippen

going to be blank for Gandalf and Pippen and you can see that right here so again

and you can see that right here so again outer joins are kind of the opposite of

outer joins are kind of the opposite of inner joins they're going to return

inner joins they're going to return everything from both if there is

everything from both if there is overlapping data it won't be duplicated

overlapping data it won't be duplicated now let's go on to the left join and I'm

now let's go on to the left join and I'm going to pull this down right here and

going to pull this down right here and now we're just going to say how is equal

now we're just going to say how is equal to left and let's run this so what this

to left and let's run this so what this is going to do is it's going to take

is going to do is it's going to take everything from the left table or the

everything from the left table or the left data frame right here so everything

left data frame right here so everything from data frame one then if there is any

from data frame one then if there is any overlap it'll also pull the overlapped

overlap it'll also pull the overlapped or the you know whatever we're able to

or the you know whatever we're able to merge on from data Frame 2 so let's go

merge on from data Frame 2 so let's go back up to our data frame 1 and two so

back up to our data frame 1 and two so it's going to pull everything from this

it's going to pull everything from this left data frame cuz we're specifying

left data frame cuz we're specifying we're doing a left join so everything

we're doing a left join so everything from the left data frame will be in

from the left data frame will be in there we're also going to try to bring

there we're also going to try to bring in everything from the right but only if

in everything from the right but only if it matches or or is able to merge so

it matches or or is able to merge so just this information right here will

just this information right here will come over we weren't able to join on

come over we weren't able to join on 1006 17 or 1008 so really none of that

1006 17 or 1008 so really none of that information is going to come over so

information is going to come over so let's go down and check on this so again

let's go down and check on this so again we have 1 2 3 4 all of the data with

we have 1 2 3 4 all of the data with this first name and skills everything is

this first name and skills everything is in here but then we are trying to bring

in here but then we are trying to bring over the age but we only have matches

over the age but we only have matches with 1,1 and 1002 so only these two

with 1,1 and 1002 so only these two values will come in let's look at the

values will come in let's look at the right join because it's basically the

right join because it's basically the exact opposite let's look at the

exact opposite let's look at the right and this is basically the exact

right and this is basically the exact opposite of the left in the fact that

opposite of the left in the fact that now we're only looking at the right hand

now we're only looking at the right hand and then if there's something that

and then if there's something that matches in data frame one then we will

matches in data frame one then we will pull that in so this this is basically

pull that in so this this is basically just looking like data Frame 2 except

just looking like data Frame 2 except we're pulling in that skills column and

we're pulling in that skills column and since only 101 and 102 are the same

since only 101 and 102 are the same that's why the skills values are here

that's why the skills values are here now those are the main types of merges

now those are the main types of merges that I will use when I'm using a data

that I will use when I'm using a data frame or when I'm trying to merge a data

frame or when I'm trying to merge a data frame but there also is one called a

frame but there also is one called a cross or a cross join uh and let's look

cross or a cross join uh and let's look at this one and this one is quite a bit

at this one and this one is quite a bit different here we go let's run this so

different here we go let's run this so this one is different in that it takes

this one is different in that it takes each value from the left data frame and

each value from the left data frame and Compares it to each value in the right

Compares it to each value in the right data frame so for froto in this left

data frame so for froto in this left data frame it looks at the froto in the

data frame it looks at the froto in the right data frame Sam wise in the right

right data frame Sam wise in the right data frame legalis elron and baromir all

data frame legalis elron and baromir all on the right data frame then it goes to

on the right data frame then it goes to the next value Sam wise and does the

the next value Sam wise and does the exact same thing Roto Sam wise legalis

exact same thing Roto Sam wise legalis Elon baromir and it does that for every

Elon baromir and it does that for every single value so let's go right back up

single value so let's go right back up here so it's taking this this this 101

here so it's taking this this this 101 it's comparing it to 1 2 3 4 5 then it's

it's comparing it to 1 2 3 4 5 then it's taking Samwise it's comparing it to 1 2

taking Samwise it's comparing it to 1 2 3 4 5 Gandalf 1 2 3 4 5 Pippen and then

3 4 5 Gandalf 1 2 3 4 5 Pippen and then you kind of see that pattern and that's

you kind of see that pattern and that's what a cross joint is um there are very

what a cross joint is um there are very few in my opinion reasons for a cross

few in my opinion reasons for a cross join although you'll if you ever do like

join although you'll if you ever do like an interview where you're being

an interview where you're being interviewed on python you will sometimes

interviewed on python you will sometimes be asked on Cross joins but there aren't

be asked on Cross joins but there aren't a lot of instances in actual work where

a lot of instances in actual work where you really use need a cross join now

you really use need a cross join now let's take a look at joins and joins are

let's take a look at joins and joins are pretty similar to the merge function and

pretty similar to the merge function and it can do a lot of the same thing except

it can do a lot of the same thing except in my opinion the join function isn't as

in my opinion the join function isn't as easily understood as the merge function

easily understood as the merge function it's a little bit more complicated um

it's a little bit more complicated um but let's take a look and see how we can

but let's take a look and see how we can join together these data frames using

join together these data frames using the join function so let's go right up

the join function so let's go right up here we're going to say data frame one

here we're going to say data frame one do join and then we'll do data frame two

do join and then we'll do data frame two very similar to how we did it before and

very similar to how we did it before and let's try running this and it's not

let's try running this and it's not going to work um when we did the merge

going to work um when we did the merge function it had a lot of defaults for us

function it had a lot of defaults for us let's go down and see what this error is

let's go down and see what this error is it says the columns overlap but no

it says the columns overlap but no suffix was specified so it's telling us

suffix was specified so it's telling us that it's trying to use the fellowship

that it's trying to use the fellowship ID and the first name just like the join

ID and the first name just like the join did except it's not able to distinguish

did except it's not able to distinguish which is which and so we need to go in

which is which and so we need to go in there and kind of help it out a little

there and kind of help it out a little bit again a little bit more Hands-On

bit again a little bit more Hands-On than the merge but let's see what we can

than the merge but let's see what we can do to make this work let's do comma and

do to make this work let's do comma and we'll say on and let's really quickly

we'll say on and let's really quickly let's open this up and kind of see what

let's open this up and kind of see what we have so this one has less options

we have so this one has less options than the merge does we have other and

than the merge does we have other and that's our other data frame we can do on

that's our other data frame we can do on and we're going to specify you know what

and we're going to specify you know what column do we want to join on and then we

column do we want to join on and then we can look at how do we want it to be a

can look at how do we want it to be a left an inner an outer the same kind of

left an inner an outer the same kind of types of joins as the merge then we have

types of joins as the merge then we have that left suffix right suffix and that's

that left suffix right suffix and that's right here is kind of part of the issue

right here is kind of part of the issue that we were just facing is that those

that we were just facing is that those columns are the same but if we say left

columns are the same but if we say left suffix it'll give us an underscore

suffix it'll give us an underscore whatever we want to specify any string

whatever we want to specify any string four columns that are both in the left

four columns that are both in the left and the right we can give it a unique

and the right we can give it a unique name so we'll no longer have that issue

name so we'll no longer have that issue and then we can also sort it like we did

and then we can also sort it like we did on the other one but anyways let's go

on the other one but anyways let's go back to our on we'll say on is equal to

back to our on we'll say on is equal to and then we'll say

and then we'll say Fellowship ID let's try running this and

Fellowship ID let's try running this and we're still getting an error it's just

we're still getting an error it's just not as simple as the merge so let's keep

not as simple as the merge so let's keep going so now let's specify the type so

going so now let's specify the type so we'll say how is equal to and we'll do

we'll say how is equal to and we'll do an

an outer and if we run this it still

outer and if we run this it still doesn't work we're still getting the

doesn't work we're still getting the exact same issue as the left suffix and

exact same issue as the left suffix and the right suffix so now let's finally

the right suffix so now let's finally resolve it I just wanted to show you how

resolve it I just wanted to show you how a little bit more frustrating it was but

a little bit more frustrating it was but now let's say uh L suffix is equal to

now let's say uh L suffix is equal to and now it automatically when we did the

and now it automatically when we did the merge did an underscore X but we can do

merge did an underscore X but we can do let's do

let's do underscore uh

underscore uh left and then we can do a comma we'll do

left and then we can do a comma we'll do right

right suffix and we'll says equal to and we'll

suffix and we'll says equal to and we'll do underscore right now when we run this

do underscore right now when we run this it should work properly let's run this

it should work properly let's run this so this is our output and obviously

so this is our output and obviously looks quite a bit different over here we

looks quite a bit different over here we have this Fellowship ID then we also

have this Fellowship ID then we also have Fellowship ID left first name left

have Fellowship ID left first name left Fellowship ID right and first name right

Fellowship ID right and first name right so it just doesn't doesn't look right

so it just doesn't doesn't look right now something I didn't specify when I

now something I didn't specify when I first started this cuz I kind of wanted

first started this cuz I kind of wanted to show you is that the join usually is

to show you is that the join usually is better for when you're working with

better for when you're working with indexes before when we were using the

indexes before when we were using the merge we were using the column names and

merge we were using the column names and that worked really well and it was

that worked really well and it was pretty easy to do but as you can see

pretty easy to do but as you can see right here when we're trying to use

right here when we're trying to use these column names it's not working

these column names it's not working exceptionally well let's go ahead and

exceptionally well let's go ahead and create our index and then I can show you

create our index and then I can show you how this actually works and how it works

how this actually works and how it works a little bit better when we're working

a little bit better when we're working with just the index although you can get

with just the index although you can get to work just the same as the merge it's

to work just the same as the merge it's just a lot more work so let's go right

just a lot more work so let's go right down here and let's go and say df4 so

down here and let's go and say df4 so we'll create a new data frame we'll say

we'll create a new data frame we'll say df1 do setor index and we'll do an open

df1 do setor index and we'll do an open parentheses and we'll say we want to do

parentheses and we'll say we want to do this index on the

this index on the fellowship ID and then we're going to do

fellowship ID and then we're going to do the join so now we're going to say join

the join so now we're going to say join so we're setting an index so we're

so we're setting an index so we're setting that index on the fellowship ID

setting that index on the fellowship ID now we're we're going to join it on df2

now we're we're going to join it on df2 do setor index and then we're also going

do setor index and then we're also going to do that on the fellowship ID and I'll

to do that on the fellowship ID and I'll just copy

this oh geez I hate it when I do that okay now we also want to do and specify

okay now we also want to do and specify the left and the right index so I'll

the left and the right index so I'll just copy this as we do need to specify

just copy this as we do need to specify this now let's try running the data

this now let's try running the data frame 4 so really quick just to recap we

frame 4 so really quick just to recap we were setting the indexes we were doing

were setting the indexes we were doing the same thing above right we have this

the same thing above right we have this join we were joining data frame one with

join we were joining data frame one with data Frame 2 now we're joining data

data Frame 2 now we're joining data frame 1 with data frame two except in

frame 1 with data frame two except in both instances we're setting the index

both instances we're setting the index as Fellowship ID so we're joining now on

as Fellowship ID so we're joining now on that index so now let's run this and

that index so now let's run this and this should look a lot more similar to

this should look a lot more similar to the merge than the join that we did

the merge than the join that we did above except now the fellowship ID right

above except now the fellowship ID right here is actually an index so it's just a

here is actually an index so it's just a little bit different but we can still go

little bit different but we can still go in here and do how is equal to

in here and do how is equal to Outer oops let's say outer so we can

Outer oops let's say outer so we can still specify our different types of

still specify our different types of joins or the different way that we can

joins or the different way that we can merge or join these data frames together

merge or join these data frames together we can still specify that again it's

we can still specify that again it's just a little bit different and that's

just a little bit different and that's why for most instances I'm using that

why for most instances I'm using that merge function because it's just a

merge function because it's just a little bit more seamless little bit more

little bit more seamless little bit more intuitive the join function can still

intuitive the join function can still get the job done but as you can see it

get the job done but as you can see it takes a little bit more work now let's

takes a little bit more work now let's look at concatenate concatenating data

look at concatenate concatenating data frames can be really useful and the

frames can be really useful and the distinction between a merge and join

distinction between a merge and join versus the concatenate is that the

versus the concatenate is that the concatenate is kind of like putting one

concatenate is kind of like putting one data frame on top of the other rather

data frame on top of the other rather than putting one data frame next to one

than putting one data frame next to one another which is like the merge and the

another which is like the merge and the join so concatenating them is just a

join so concatenating them is just a little bit different in how it'll

little bit different in how it'll operate but let's actually write this

operate but let's actually write this out and see how this looks let's go up

out and see how this looks let's go up here and we'll say pd. concat we'll do

here and we'll say pd. concat we'll do an open parenthesis and then we're going

an open parenthesis and then we're going to concatenate data frame 1 comma data

to concatenate data frame 1 comma data Frame 2 that's all we have to write and

Frame 2 that's all we have to write and let's run this and so just like I said

let's run this and so just like I said it literally took the First Data frame 1

it literally took the First Data frame 1 2 3 4 and put it on top of the right

2 3 4 and put it on top of the right data frame 1 2 6 7 8 so that is our left

data frame 1 2 6 7 8 so that is our left data frame this is our right data frame

data frame this is our right data frame and they're literally just sitting one

and they're literally just sitting one on top of the other but just like when

on top of the other but just like when we merg either with a left or a right

we merg either with a left or a right when you have these skills and there

when you have these skills and there aren't any values that populate for them

aren't any values that populate for them it is going to say not a number and

it is going to say not a number and since we're not actually joining we're

since we're not actually joining we're not joining on one and two even though

not joining on one and two even though this one and this one is the same rows

this one and this one is the same rows it's not populating that value because

it's not populating that value because again we're not joining these together

again we're not joining these together we're just concatenating and putting one

we're just concatenating and putting one on top of the other now if we go into

on top of the other now if we go into this concat we say shift tab there are a

this concat we say shift tab there are a lot of different things that we can do

lot of different things that we can do which if you remember the zero axis is

which if you remember the zero axis is the leftand index and the axis of one is

the leftand index and the axis of one is the top index which is the columns so

the top index which is the columns so you can specify that and we can also o

you can specify that and we can also o do joins and this is the one that I'm

do joins and this is the one that I'm going to take a look at but there are

going to take a look at but there are other ones that you can um look into as

other ones that you can um look into as well let's look at join let's do comma

well let's look at join let's do comma and we'll say join is equal to and let's

and we'll say join is equal to and let's do an inner join so let's see what

do an inner join so let's see what happens with this as you can see it is

happens with this as you can see it is only taking the columns that are the

only taking the columns that are the same that's what this in is doing it's

same that's what this in is doing it's joining these columns together and the

joining these columns together and the ones that were different they didn't

ones that were different they didn't take because again we weren't able to

take because again we weren't able to combine them they aren't similar between

combine them they aren't similar between both frames Let's do an outer and now

both frames Let's do an outer and now it's going to take all of them and like

it's going to take all of them and like I said that's doing this on these

I said that's doing this on these columns right here but we can also do it

columns right here but we can also do it on this axis as well so let's go ahead

on this axis as well so let's go ahead and say axis is equal to one and when we

and say axis is equal to one and when we run this now it's joining us on this

run this now it's joining us on this Index right here of 0 1 2 3 4 so now

Index right here of 0 1 2 3 4 so now these ones are being joined together and

these ones are being joined together and it's putting it side by side much like a

it's putting it side by side much like a merge wood so that's how concatenate

merge wood so that's how concatenate works and I'm going to show you one more

works and I'm going to show you one more thing and again it's not up here in this

thing and again it's not up here in this you know title because it's not one that

you know title because it's not one that I recommend but is one called append the

I recommend but is one called append the append function is used to append rows

append function is used to append rows from one data frame to the end of

from one data frame to the end of another data frame and then we can

another data frame and then we can return that new data frame and so let's

return that new data frame and so let's do data frame one. aend we'll do an open

do data frame one. aend we'll do an open parenthesis and we'll say data Frame 2

parenthesis and we'll say data Frame 2 very similar to how we've been doing

very similar to how we've been doing other things and let's run this and as

other things and let's run this and as you can see this is almost exactly like

you can see this is almost exactly like how the concatenate did when we first

how the concatenate did when we first did it but if we read kind of this

did it but if we read kind of this warning it's saying the frame append

warning it's saying the frame append method is deprecated and will be removed

method is deprecated and will be removed from pandas in the future version use

from pandas in the future version use pandas do concat instead so it's

pandas do concat instead so it's literally warning us you know a pend is

literally warning us you know a pend is on its way out if you want to do exactly

on its way out if you want to do exactly what you're doing right here go and try

what you're doing right here go and try concat or concatenate because that'll do

concat or concatenate because that'll do the exact same thing so I'm not really

the exact same thing so I'm not really going to show you any other variations

going to show you any other variations of a pend because there's no reason it's

of a pend because there's no reason it's going to be on its way out in the next

going to be on its way out in the next version so that is our video on merge

version so that is our video on merge join and concatenate and aend as well uh

join and concatenate and aend as well uh in panda does and I hope that that was

in panda does and I hope that that was helpful I hope that you learned

helpful I hope that you learned something I mean this stuff is really

something I mean this stuff is really important because often times you're not

important because often times you're not just working with one CSV or one Json or

just working with one CSV or one Json or one text file you're working with

one text file you're working with multiple of them and you need to combine

multiple of them and you need to combine them all into one data frame and so this

them all into one data frame and so this is a really really important concept and

is a really really important concept and thing to understand with that being said

thing to understand with that being said be sure to like And subscribe check out

be sure to like And subscribe check out all my other videos on Python and pandas

all my other videos on Python and pandas and I will see you in the next

and I will see you in the next [Music]

video [Music]

[Music] hello everybody today we're going to be

hello everybody today we're going to be building visualizations in pandas in

building visualizations in pandas in this video we'll look at how we can

this video we'll look at how we can build visualizations like line plots

build visualizations like line plots Scatter Plots bar charts histograms and

Scatter Plots bar charts histograms and more I'll also show you some of the ways

more I'll also show you some of the ways that you can customize these

that you can customize these visualizations to make them just a

visualizations to make them just a little bit better with that being said

little bit better with that being said let's go right over here start importing

let's go right over here start importing our libraries and we'll start with

our libraries and we'll start with importing pandas as PD and this one is

importing pandas as PD and this one is really all you need to actually create

really all you need to actually create the visualizations in pandas but we may

the visualizations in pandas but we may get a little bit crazy uh and so we're

get a little bit crazy uh and so we're going to do a few different ones as well

going to do a few different ones as well like import

like import numpy as NP and then we're going to do

numpy as NP and then we're going to do import Matt plot lib do

import Matt plot lib do pyplot as PLT now I may or may not use

pyplot as PLT now I may or may not use this I just you know when I get into

this I just you know when I get into visualizations I may want to change some

visualizations I may want to change some different things so we're going to at

different things so we're going to at least have them here in case we do want

least have them here in case we do want to use them let's go ahead and run this

to use them let's go ahead and run this so now let's get our data set that we're

so now let's get our data set that we're going to be using so let's say data

going to be using so let's say data frame is equal to pd. read _

frame is equal to pd. read _ CSV and let's get this in right here now

CSV and let's get this in right here now we're going to be doing these ice cream

we're going to be doing these ice cream ratings let's take a look at this really

ratings let's take a look at this really quickly now these values are completely

quickly now these values are completely randomly generated they are not real in

randomly generated they are not real in any way um but that's what we're going

any way um but that's what we're going to be using cuz I just wanted something

to be using cuz I just wanted something kind of generic something that wouldn't

kind of generic something that wouldn't be too crazy confusing just something

be too crazy confusing just something that we could use and you guys can

that we could use and you guys can understand that they're just numerical

understand that they're just numerical values but let's also set that index

values but let's also set that index really quick so we'll say data frame.

really quick so we'll say data frame. setor index and then we'll say date and

setor index and then we'll say date and then we'll say that's equal to the data

then we'll say that's equal to the data frame and we have this date column right

frame and we have this date column right here as our index so we have uh January

here as our index so we have uh January 1st 2nd 3rd 4th and then we have our

1st 2nd 3rd 4th and then we have our ratings right here and again these are

ratings right here and again these are all just integers and they're pretty

all just integers and they're pretty easy or are really easy to demonstrate

easy or are really easy to demonstrate how you can visualize these so that's

how you can visualize these so that's why we're using it today so the way that

why we're using it today so the way that we visualize something in pandas is we

we visualize something in pandas is we use something called plot so let's just

use something called plot so let's just take our data frame we'll do data frame.

take our data frame we'll do data frame. plot and we'll do our parentheses now

plot and we'll do our parentheses now let's go in here really quickly let's

let's go in here really quickly let's hit shift Tab and this is going to come

hit shift Tab and this is going to come up and this is pretty important because

up and this is pretty important because this kind of is going to tell us what we

this kind of is going to tell us what we can do within this plot and

can do within this plot and unfortunately there isn't like a quick

unfortunately there isn't like a quick overview we just have this doc string

overview we just have this doc string but we have our parameters right here

but we have our parameters right here these are what we can pass in to kind of

these are what we can pass in to kind of customize our visualization so the data

customize our visualization so the data is going to be our data frame then we

is going to be our data frame then we have our X and Y labels we can specify

have our X and Y labels we can specify the kind and this one's important

the kind and this one's important because you can specify what kind of

because you can specify what kind of visualization do we want we can do a

visualization do we want we can do a line plot horizontal a vertical bar plot

line plot horizontal a vertical bar plot histogram box plot and then a few others

histogram box plot and then a few others including area Pi density all these

including area Pi density all these other things we can also specify if we

other things we can also specify if we want it to be a subplot and a lot of

want it to be a subplot and a lot of these things that I'm specifying you

these things that I'm specifying you know I'm going to show you how to do you

know I'm going to show you how to do you can use a different indexes you can add

can use a different indexes you can add titles add grids Legends Styles all

titles add grids Legends Styles all these different things I mean you can go

these different things I mean you can go through here CU there are a lot but you

through here CU there are a lot but you can specify and and you know customize

can specify and and you know customize all of these things we won't be going

all of these things we won't be going into all of them but I will show you

into all of them but I will show you some of the ones that I probably use the

some of the ones that I probably use the most and that I think are the most

most and that I think are the most useful to know right away so let's get

useful to know right away so let's get out of here and we're just going to do

out of here and we're just going to do DF do plot and when we run this we'll

DF do plot and when we run this we'll get this right here and that was super

get this right here and that was super super easy created a line plot by

super easy created a line plot by literally doing just about nothing

literally doing just about nothing nothing um but by default it's going to

nothing um but by default it's going to give us a line plot so if we come up

give us a line plot so if we come up here we say kind and let me get that out

here we say kind and let me get that out of the way is equal to line and we run

of the way is equal to line and we run this so by default without us actually

this so by default without us actually having to input anything it's giving us

having to input anything it's giving us that line plot as a default so uh we can

that line plot as a default so uh we can specify it's a line plot as you can see

specify it's a line plot as you can see we already have all of our data right

we already have all of our data right here we didn't have to specify anything

here we didn't have to specify anything it kind of automatically took it in it

it kind of automatically took it in it is visualizing all three of these

is visualizing all three of these columns

columns and it has this little um Legend right

and it has this little um Legend right here and we can specify where we want

here and we can specify where we want that uh there is an argument to be able

that uh there is an argument to be able to do that it also gave us these tick

to do that it also gave us these tick marks of 2 4 6 8 10 again it read in and

marks of 2 4 6 8 10 again it read in and said it's only going from 0.0 to 1.0

said it's only going from 0.0 to 1.0 that is kind of the peak and so it kind

that is kind of the peak and so it kind of automatically gave us these ticks for

of automatically gave us these ticks for us again that's another thing that you

us again that's another thing that you can specify we make it go up to 2 5 10

can specify we make it go up to 2 5 10 1,000 whatever you want it to be and

1,000 whatever you want it to be and then we're doing this based on off of

then we're doing this based on off of this date value right here really

this date value right here really quickly I wanted to give a huge shout

quickly I wanted to give a huge shout out to the sponsor of this entire Panda

out to the sponsor of this entire Panda series and that is udemy udy has some of

series and that is udemy udy has some of the best courses at the best prices and

the best courses at the best prices and it is no exception when it comes to

it is no exception when it comes to pandas courses if you want to master

pandas courses if you want to master pandas this is the course that I would

pandas this is the course that I would recommend it's going to teach you just

recommend it's going to teach you just about everything you need to know about

about everything you need to know about pandas so huge shout out to you me for

pandas so huge shout out to you me for sponsoring this Panda series and let's

sponsoring this Panda series and let's get back to the video if we wanted to

get back to the video if we wanted to break these out by the actual column we

break these out by the actual column we could go in here and say subplot is

could go in here and say subplot is equal to true and it's actually subplots

equal to true and it's actually subplots whoops and now we can run that and then

whoops and now we can run that and then we can see each of those columns being

we can see each of those columns being broken out by themselves instead of them

broken out by themselves instead of them all being in one visualization it's now

all being in one visualization it's now uh three separate visualizations now

uh three separate visualizations now let's go right over here we're going to

let's go right over here we're going to get rid of the subplots I want to show

get rid of the subplots I want to show you just some of the different arguments

you just some of the different arguments that you can use to make this look nice

that you can use to make this look nice uh because I don't want to do this on

uh because I don't want to do this on every single visualization I just want

every single visualization I just want to show you what you can do so we have

to show you what you can do so we have this one right here we can add a title

this one right here we can add a title notice there's no title or anything

notice there's no title or anything really telling us what that is so we can

really telling us what that is so we can say comma title and we'll say ice cream

say comma title and we'll say ice cream ratings if we run this we now have this

ratings if we run this we now have this nice title right here now we can also

nice title right here now we can also customize the labels or the titles for

customize the labels or the titles for the X and Y AIS it automatically took

the X and Y AIS it automatically took this date which is right here this is

this date which is right here this is our date index it automatically took

our date index it automatically took that for us but we can customize that if

that for us but we can customize that if we'd like to all we have to do is comma

we'd like to all we have to do is comma and then we'll say xlabel is equal to

and then we'll say xlabel is equal to and so our X is this date one right here

and so our X is this date one right here and we can say daily

and we can say daily rating and then we can do the Y Lael

rating and then we can do the Y Lael we'll say y label is equal to and for

we'll say y label is equal to and for this one we can say

this one we can say scores hope you cannot hear my dog in

scores hope you cannot hear my dog in the background CU they being insane uh

the background CU they being insane uh but let's go ahead and run this and now

but let's go ahead and run this and now we have these daily ratings on the x-

we have these daily ratings on the x- axis and on the Y AIS we have scores now

axis and on the Y AIS we have scores now let's go right down here and start

let's go right down here and start taking a look at our next kind of

taking a look at our next kind of visualization which is going to be a bar

visualization which is going to be a bar plot so we'll do DF do plot we'll do

plot so we'll do DF do plot we'll do kind is equal to and for this one we're

kind is equal to and for this one we're going to say bar now this is what your

going to say bar now this is what your typical bar plot will look like and a

typical bar plot will look like and a lot of the arguments that we just did on

lot of the arguments that we just did on the line plot you can also apply to this

the line plot you can also apply to this bar plot something that's unique to the

bar plot something that's unique to the bar plot is that you can also make it a

bar plot is that you can also make it a stacked bar plot all we have to do is go

stacked bar plot all we have to do is go in here we'll say comma and we'll say

in here we'll say comma and we'll say stacked is equal to true so now this

stacked is equal to true so now this going to make it a stacked bar chart

going to make it a stacked bar chart instead of just know your regular bar

instead of just know your regular bar chart let's go ahead and run this and as

chart let's go ahead and run this and as you can see this is now stacked on top

you can see this is now stacked on top of one another with each of these

of one another with each of these columns all representing the values that

columns all representing the values that they have now we don't always have to do

they have now we don't always have to do every single column we can also specify

every single column we can also specify the column that we want so let's take

the column that we want so let's take the flavor rating for example we could

the flavor rating for example we could do flavor oops flavor rating good night

do flavor oops flavor rating good night flavor rating and then it's only going

flavor rating and then it's only going to take in that flavor rating column and

to take in that flavor rating column and if you notice we don't have a legend

if you notice we don't have a legend that's only when you have multiple

that's only when you have multiple values which we are only looking at this

values which we are only looking at this one column so all the values are right

one column so all the values are right here now in this bar chart it

here now in this bar chart it automatically defaults to a vertical bar

automatically defaults to a vertical bar chart but you can change it to a

chart but you can change it to a horizontal bar chart let's go ahead and

horizontal bar chart let's go ahead and take a look at how to do that bring back

take a look at how to do that bring back all of them we'll do DF do plot Dot and

all of them we'll do DF do plot Dot and then we'll say

then we'll say barh and I don't know if I can keeping

barh and I don't know if I can keeping that kind equals bar let me run this

that kind equals bar let me run this yeah I need to get rid of that because

yeah I need to get rid of that because the bar. H is its own um this is its own

the bar. H is its own um this is its own function so now I'm going to run this it

function so now I'm going to run this it should just have a stacked bar chart

should just have a stacked bar chart except now it should be horizontal so

except now it should be horizontal so now you can see this worked properly

now you can see this worked properly it's basically the exact same thing as a

it's basically the exact same thing as a vertical bar chart just now horizontal

vertical bar chart just now horizontal which may look better especially

which may look better especially depending on if you have values like

depending on if you have values like this or you know something else that

this or you know something else that just looks better being horizontal now

just looks better being horizontal now the next one that we're going to take a

the next one that we're going to take a look at is the scatter plot so we're

look at is the scatter plot so we're going to say DF do plot do scatter

going to say DF do plot do scatter scatter and if we run this we're going

scatter and if we run this we're going to get an error what we need in order to

to get an error what we need in order to run this properly is we need to specify

run this properly is we need to specify the X and the Y AIS in order for this

the X and the Y AIS in order for this scatter plot to work so let's go here

scatter plot to work so let's go here and we'll say x is equal to and we can

and we'll say x is equal to and we can take any of our columns that we have up

take any of our columns that we have up here so we'll say x is equal to texture

here so we'll say x is equal to texture rating and then oops Y is equal to we'll

rating and then oops Y is equal to we'll do overall rating

do overall rating now when we run this it should work

now when we run this it should work properly let's go ahead and take a look

properly let's go ahead and take a look now if we go in here and we do shift tab

now if we go in here and we do shift tab we can also see some other things that

we can also see some other things that we can specify so let's go right down

we can specify so let's go right down here so we have our X and we have our Y

here so we have our X and we have our Y and those are the ones that we just did

and those are the ones that we just did we can also pass through an S which is

we can also pass through an S which is going to tell us or or change the size

going to tell us or or change the size of the actual dots right here in our

of the actual dots right here in our scatter plot then we can also do a c

scatter plot then we can also do a c which is the color of each point let's

which is the color of each point let's start with the S

start with the S let's say s is equal to let's just do

let's say s is equal to let's just do 100 let's see what that looks like so we

100 let's see what that looks like so we have a much larger number let's do 500

have a much larger number let's do 500 and see what that looks like so we can

and see what that looks like so we can make these much larger on our

make these much larger on our visualization depending on what you're

visualization depending on what you're looking for we can also look at the

looking for we can also look at the color let's put comma C so for color we

color let's put comma C so for color we can say color is equal to and let's do

can say color is equal to and let's do uh yellow let's see if this works so now

uh yellow let's see if this works so now we've changed it to Yellow that looks

we've changed it to Yellow that looks absolutely terrible but it does work now

absolutely terrible but it does work now let's move on to the histogram histogram

let's move on to the histogram histogram is always a good one it's very similar

is always a good one it's very similar to something like a bar chart but what's

to something like a bar chart but what's great about a histogram is you can

great about a histogram is you can specify the bins um so let's go ahead

specify the bins um so let's go ahead and say DF

and say DF dolot doist then we'll do an open

dolot doist then we'll do an open parenthesis and let's go ahead and hit

parenthesis and let's go ahead and hit shift tab in here take a look at this

shift tab in here take a look at this one as well so some of our parameters

one as well so some of our parameters are the actual Columns of the data

are the actual Columns of the data frames that we want to pull in we get

frames that we want to pull in we get you can choose the bins and they have a

you can choose the bins and they have a default of 10 in here and so let's take

default of 10 in here and so let's take a look at how this works so we'll just

a look at how this works so we'll just run this as it is so this is by default

run this as it is so this is by default what this histogram is going to look

what this histogram is going to look like let's go ahead and specify our bins

like let's go ahead and specify our bins we'll just say it was 10 by default

we'll just say it was 10 by default let's just do 20 see what that looks

let's just do 20 see what that looks like so there are smaller columns right

like so there are smaller columns right off the bat and remember histograms are

off the bat and remember histograms are really good for showing distribution of

really good for showing distribution of variables you know that's really what a

variables you know that's really what a histogram is for but of course since

histogram is for but of course since these are completely random numbers this

these are completely random numbers this histogram isn't going to make any sense

histogram isn't going to make any sense at all but you can at least kind of see

at all but you can at least kind of see visually how it works and if I didn't

visually how it works and if I didn't mention it before which I should have

mention it before which I should have the bins represent how many kind of tick

the bins represent how many kind of tick marks are down here so if we just do one

marks are down here so if we just do one only going to be one very large uh you

only going to be one very large uh you know histogram we could even go further

know histogram we could even go further down from 10 and do five so now there's

down from 10 and do five so now there's only one 2 3 four five so the

only one 2 3 four five so the distribution gets smaller and and things

distribution gets smaller and and things get more compact as you spread it out

get more compact as you spread it out again like we did

again like we did 100 it's going to spread it out a lot um

100 it's going to spread it out a lot um and this is what it shows you know it's

and this is what it shows you know it's showing the distribution of those bins

showing the distribution of those bins across however many you want so the 10

across however many you want so the 10 by default you know it usually is pretty

by default you know it usually is pretty good for a lot of different things now

good for a lot of different things now let's go down here and look at the box

let's go down here and look at the box plot and the box plot is a pretty

plot and the box plot is a pretty interesting one let's go ahead and

interesting one let's go ahead and visualize it really quickly and then

visualize it really quickly and then I'll kind of explain how this one works

I'll kind of explain how this one works let's do d boox plot let's run this and

let's do d boox plot let's run this and really what we're looking at is some

really what we're looking at is some different markers within our data this

different markers within our data this line right here is the minimum value

line right here is the minimum value within that column we also have the

within that column we also have the bottom of the box which is the 25th

bottom of the box which is the 25th percentile of all the values within just

percentile of all the values within just this column this is 50% then we have 75%

this column this is 50% then we have 75% and then up here we have our maximum

and then up here we have our maximum value so I can take a glance at this and

value so I can take a glance at this and see that we have a low minimum a high

see that we have a low minimum a high maximum and it definitely skews towards

maximum and it definitely skews towards the lower range whereas if I look over

the lower range whereas if I look over here we have a lower minimum and a

here we have a lower minimum and a higher maximum and you can see that this

higher maximum and you can see that this medium point is at0 6 versus 04 over

medium point is at0 6 versus 04 over here so the skew is a lot higher now

here so the skew is a lot higher now let's go down here and take a look at an

let's go down here and take a look at an area plot we'll do DF do plot. area and

area plot we'll do DF do plot. area and let's just run this this is what we're

let's just run this this is what we're going to get by default now something I

going to get by default now something I wanted to show you earlier I just

wanted to show you earlier I just haven't gotten around to I want to show

haven't gotten around to I want to show you something called Figure size or fig

you something called Figure size or fig size um so for this it's know it's just

size um so for this it's know it's just looks small small looks a little bit

looks small small looks a little bit cramped let's say we want to increase

cramped let's say we want to increase the size of this and we'll say fig size

the size of this and we'll say fig size oops fig size is equal to and let's just

oops fig size is equal to and let's just do a parentheses and say 10 comma 5 that

do a parentheses and say 10 comma 5 that should be pretty large this is going to

should be pretty large this is going to make it a lot larger just something I

make it a lot larger just something I wanted to throw in there I look at these

wanted to throw in there I look at these area charts as pretty similar to like a

area charts as pretty similar to like a line chart if we went and compared those

line chart if we went and compared those be pretty similar um but they're

be pretty similar um but they're different visually and you know you

different visually and you know you absolutely can use these for different

absolutely can use these for different types of visualizations but I don't use

types of visualizations but I don't use this one a lot if I'm being honest

this one a lot if I'm being honest that's why it's kind of towards the end

that's why it's kind of towards the end of the video but you definitely can do

of the video but you definitely can do it let's go on to our very last one of

it let's go on to our very last one of the video that's going to be the

the video that's going to be the beautiful pie chart let's say DF plot.py

beautiful pie chart let's say DF plot.py do an open parenthesis and let's run it

do an open parenthesis and let's run it we're going to get this error that's

we're going to get this error that's because we need to specify what column

because we need to specify what column we're working with here so let's just

we're working with here so let's just say the Y and that's what we need let me

say the Y and that's what we need let me open this up for

open this up for us right here we have our y and this is

us right here we have our y and this is our our label or a column that we're

our our label or a column that we're going to plot that's really all we need

going to plot that's really all we need so we can just say Y is equal to flavor

so we can just say Y is equal to flavor rating oops flavor rating let's run this

rating oops flavor rating let's run this and now we get this visualization right

and now we get this visualization right here let's make this one a little bit

here let's make this one a little bit bigger big size is equal to 10 comma 6

bigger big size is equal to 10 comma 6 now it's a little bit bigger it

now it's a little bit bigger it definitely depends so this Legend is

definitely depends so this Legend is going to autop populate you know you can

going to autop populate you know you can make this as big as you want and

make this as big as you want and obviously it's going to look a little

obviously it's going to look a little bit better if you do it larger and these

bit better if you do it larger and these colors autop populate now you can

colors autop populate now you can customize these colors although I found

customize these colors although I found these ones to be just when you have a

these ones to be just when you have a lot of them it's harder to customize

lot of them it's harder to customize them as easily but you know definitely

them as easily but you know definitely look into it these are things that

look into it these are things that everything in here is almost something

everything in here is almost something that you can customize in some way

that you can customize in some way although it does get a little bit tricky

although it does get a little bit tricky you definitely have to do some research

you definitely have to do some research and some Googling around just to kind of

and some Googling around just to kind of figure out how to do those things now

figure out how to do those things now one last thing that I wanted to show and

one last thing that I wanted to show and something you know I could have probably

something you know I could have probably done at the beginning um is you can

done at the beginning um is you can actually change what visual this is and

actually change what visual this is and we can do that pretty easily within mpot

we can do that pretty easily within mpot lib there are different styles um and so

lib there are different styles um and so let's go right here let's add a new row

let's go right here let's add a new row a new cell and we'll say print and we'll

a new cell and we'll say print and we'll do PLT so that's that map plot lib right

do PLT so that's that map plot lib right here we'll do PLT do style.

here we'll do PLT do style. available and what this is going to do

available and what this is going to do whoops what this is going to do is show

whoops what this is going to do is show us all these different different types

us all these different different types of stylings that you can do to kind of

of stylings that you can do to kind of change up this visualization then once

change up this visualization then once we find the one that we like we'll just

we find the one that we like we'll just do PLT do style. use and then in the

do PLT do style. use and then in the parenthesis we'll just specify which one

parenthesis we'll just specify which one we want now there's all these Seaborn

we want now there's all these Seaborn ones and Seaborn is a really great um

ones and Seaborn is a really great um really great Library let's try Seaborn

really great Library let's try Seaborn deep I haven't tried this one at all

deep I haven't tried this one at all let's go ahead and try this and just

let's go ahead and try this and just changes some of the colors some of the

changes some of the colors some of the visuals we can try something like

visuals we can try something like 538 let's try this that looks quite a

538 let's try this that looks quite a bit different and let's try something

bit different and let's try something like um classic I don't know what this

like um classic I don't know what this one looks like let's just try

one looks like let's just try it so you can try out all these

it so you can try out all these different styles find one that you like

different styles find one that you like find one that you think looks really

find one that you think looks really nice and you can run with it through all

nice and you can run with it through all your visualizations so this has been our

your visualizations so this has been our video on visualizing data in pandas I

video on visualizing data in pandas I think it's is a really good introduction

think it's is a really good introduction on how you can visualize data within

on how you can visualize data within python and in future videos we'll look

python and in future videos we'll look at mpot lib and Seaborn which are some

at mpot lib and Seaborn which are some really great libraries for visualizing

really great libraries for visualizing data which I use a lot so I hope that

data which I use a lot so I hope that you enjoyed this video if you did be

you enjoyed this video if you did be sure to check out all my other videos on

sure to check out all my other videos on Python and pandas and I will see you in

Python and pandas and I will see you in the next

the next [Music]

[Music] video hello everybody today we're going

video hello everybody today we're going to be cleaning data using paint P now

to be cleaning data using paint P now there are literally hundreds of ways

there are literally hundreds of ways that you can clean data within pandas

that you can clean data within pandas but I'm going to show you some of the

but I'm going to show you some of the ones that I use a lot and ones that I

ones that I use a lot and ones that I think are really good to know when you

think are really good to know when you are cleaning your data sets so we're

are cleaning your data sets so we're going to start by saying import pandas

going to start by saying import pandas aspd and we're going to run that and now

aspd and we're going to run that and now we're going to import our file so we're

we're going to import our file so we're going to say data frame is equal to PD

going to say data frame is equal to PD that's pandas do read uncore and we

that's pandas do read uncore and we actually have this in an Excel file so

actually have this in an Excel file so we'll say read oops say read Excel do an

we'll say read oops say read Excel do an open parenthesis eses and we'll do R and

open parenthesis eses and we'll do R and then we'll paste the path right here and

then we'll paste the path right here and now we're just going to call that

now we're just going to call that variable so we'll call data frame and

variable so we'll call data frame and we'll actually read it in and look at

we'll actually read it in and look at the data so let's scroll down here and

the data so let's scroll down here and let's take a look at this data frame or

let's take a look at this data frame or this Excel file that we're reading in so

this Excel file that we're reading in so right off the bat we have this customer

right off the bat we have this customer ID that goes from 101 all the way down

ID that goes from 101 all the way down to

to 1020 we have this first name and

1020 we have this first name and everything looks pretty good here except

everything looks pretty good here except in this last name column uh looks like

in this last name column uh looks like we have some errors we have some forward

we have some errors we have some forward slashes some dots some null values um so

slashes some dots some null values um so definitely going to have to clean that

definitely going to have to clean that up because we don't want that in the

up because we don't want that in the data we have a phone number and it looks

data we have a phone number and it looks like we have a lot of different formats

like we have a lot of different formats um as well as Nas not a number um just

um as well as Nas not a number um just lots of different stuff so we're going

lots of different stuff so we're going to need to standardize that so clean it

to need to standardize that so clean it up and then standardize it to where it

up and then standardize it to where it all looks the same um we also have

all looks the same um we also have address and it looks like on some of

address and it looks like on some of these we just have a street address but

these we just have a street address but on some of the other ones we have like a

on some of the other ones we have like a street address and another location as

street address and another location as well as a zip code in some of them so

well as a zip code in some of them so we'll probably want to split those out

we'll probably want to split those out we have a paying customer uh which is

we have a paying customer uh which is yes and Nos and some of those are not

yes and Nos and some of those are not the same so I have to standardize that

the same so I have to standardize that we have a do not contact kind of the

we have a do not contact kind of the same thing as the paying customer and we

same thing as the paying customer and we have this not useful column which we'll

have this not useful column which we'll probably just want to get rid of okay so

probably just want to get rid of okay so the scenario is is that we got handed

the scenario is is that we got handed this list of names and we need to clean

this list of names and we need to clean it up and hand it off to the people who

it up and hand it off to the people who are actually going to make these calls

are actually going to make these calls to this customer list so they want all

to this customer list so they want all the data in here standardized and

the data in here standardized and cleaned so that the people who are

cleaned so that the people who are making those calls can just make those

making those calls can just make those calls as quickly as possible but they

calls as quickly as possible but they also don't want columns and rows that

also don't want columns and rows that aren't useful to them so things like

aren't useful to them so things like this not useful column we're probably

this not useful column we're probably going to get rid of and then ones that

going to get rid of and then ones that say do not contact if it says yes we

say do not contact if it says yes we should not contact them we probably will

should not contact them we probably will want to get rid of those somehow so

want to get rid of those somehow so that's a lot of what we're going to be

that's a lot of what we're going to be doing to clean this data set normally

doing to clean this data set normally the very first thing that I do when I'm

the very first thing that I do when I'm working with a data set most of the time

working with a data set most of the time except very rare cases when you're

except very rare cases when you're actually supposed to have duplicates is

actually supposed to have duplicates is I actually go and drop the duplicates

I actually go and drop the duplicates from the data set completely all you

from the data set completely all you have to do for that is say DF do

have to do for that is say DF do dropcore duplicates so they make it

dropcore duplicates so they make it super easy for you let's just run it and

super easy for you let's just run it and up here is our original data set we have

up here is our original data set we have this 19 and 20 and those are obviously

this 19 and 20 and those are obviously duplicates they have the exact same data

duplicates they have the exact same data it's just a duplicate row that we need

it's just a duplicate row that we need to get rid of if we look right down here

to get rid of if we look right down here we no longer have that 20 we now just

we no longer have that 20 we now just have one row of Anakin Skywalker and of

have one row of Anakin Skywalker and of course we want to save that so we're

course we want to save that so we're just going to say DF is equal to and DF

just going to say DF is equal to and DF so now it's going to save that to the

so now it's going to save that to the data frame variable again and now when

data frame variable again and now when we run this our data frame Now does not

we run this our data frame Now does not have any duplicates that's definitely

have any duplicates that's definitely one of the easier steps that we're going

one of the easier steps that we're going to look at uh things are going to get

to look at uh things are going to get quite a bit more complicated as we go

quite a bit more complicated as we go but I'm starting out you know kind of

but I'm starting out you know kind of simple so that we can kind of get a feel

simple so that we can kind of get a feel for it and then we'll start getting into

for it and then we'll start getting into the really tough stuff so the next thing

the really tough stuff so the next thing that I want to do is remove any columns

that I want to do is remove any columns that we don't need I don't want to clean

that we don't need I don't want to clean data that we're not going to use so if

data that we're not going to use so if we're just looking through here you know

we're just looking through here you know they may need you know first name last

they may need you know first name last name phone number for sure address might

name phone number for sure address might give them some information of where

give them some information of where they're calling to or time zone so we

they're calling to or time zone so we want that this not useful column looks

want that this not useful column looks like a pretty good candidate to delete

like a pretty good candidate to delete and it's very easy to do that we're

and it's very easy to do that we're going to go right down here and we're

going to go right down here and we're going to say DF do drop and we'll do an

going to say DF do drop and we'll do an open parenthesis drop just means we are

open parenthesis drop just means we are dropping that column and we can specify

dropping that column and we can specify that by saying columns is equal to and

that by saying columns is equal to and then we'll paste in that column that we

then we'll paste in that column that we want to delete so let's run this and see

want to delete so let's run this and see what it looks like and it literally just

what it looks like and it literally just drops that column exactly like we were

drops that column exactly like we were talking about it no longer has that

talking about it no longer has that column again we want to save that we can

column again we want to save that we can always do in in place equals true um if

always do in in place equals true um if you follow this tutorial series you can

you follow this tutorial series you can always do in place equals true and

always do in place equals true and that'll save it as well but just for our

that'll save it as well but just for our workflow most of the time I'm going to

workflow most of the time I'm going to assign it back to that variable um just

assign it back to that variable um just for keeping it the same really quickly I

for keeping it the same really quickly I wanted to give a huge shout out to the