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How to Do Data Exploration (step-by-step tutorial on real-life dataset)

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hey welcome in this video let's learn

how to do data exploration

so this will be the first video in a

series of videos where i will teach you

things about data so how to deal with data

data

and the next one will be about data

cleaning and this series is kind of

going to be like a small version of my

course hands-on data science

so if you're curious about that i will

leave a link in the description for you

to go check that out

i always get so excited when i am

starting a new project because i feel

like data exploration and

cleaning and just generally

understanding the data set is one of the

most fun things that you can do

it also gives me a really good idea to

like while i'm exploring the data set it

gives me a really good idea to how to

clean it later

and also what kind of features that i

can come up with when it is time for

future engineering and

if i you know need to create some new

features so

just to have the just like a small

advice for me

keep these two questions in your mind

like how am i gonna clean it and how am

i going to create new features

in the back of your mind while you're

doing the exploration um

don't have to be very formal just some

things that you need to be

thinking about in the background so but

let's start with the technical part

so the first and foremost again i feel

like the second first and foremost thing

that i said but

in the technical side of things the

first thing that

you need to understand is when you find

a data set in the vial

out wild and out in the open on the

internet you

really can't understand a data set by

itself of course it could be a very

simple like i said a toy data set like a

small one

very easy to understand but most of the

time you're going to be seeing data

that has some column names that you

don't understand that is kind of

or it has values that you don't

understand it could have a

different unit the column column values

might have a different unit

you know the the way that things are

represented could be different so

likely there will be things that you

will not fully understand especially if it's

it's

in a new domain that you've never worked

in before so

how we solve this is there is always

going to be some explanation files

for the data set that you're finding

just want to show you some examples like

you know it might look like a pdf

like this one and it basically tells me

for this specific data set that i found online

online

each field or column name and what they mean

mean

what the do the different values for

this column mean for example it says

it has a raid code id i mean this is a

data set i used for my course hands on

data science

as a new york city taxi data set

so it's basically like all the trips

that happened in the

in new york in like a whole month or

something like that

uh so apparently this rate code you know

and if it me if it's one it means the

standard rate if it's two i guess it

means that

they're going to jfk or newark or you

know similar things or the payment type

you know if you just are looking at the

data you will not be able to understand

this so it's important to

see these explanation files and you're

always going to find them somewhere

so as i said it could be in a pdf format uh

it could be apparently an hdmi

for html format so it will just generate

a website for you or a webpage

where you can see you know what does

this mean what does that mean

etc get some indication of like what

things mean

or it could also be just like random text

text

files so we can see this one for example

so this is just an explanation titled

the database sources

how can you use these etc etc and then here

here

uh the attributes so as i'm saying you know

know

the columns can be named a lot of things

so it could be columns fields

attributes features um yeah we just need

to kind of find this like table that explains

explains

uh what each column is sometimes they

give you the data type

sometimes they give you the um the range

of the

values that's also possible uh

yeah this one is like that or you know

this one

is a different one that also has

information about the columns

so this is the first thing that you need to

find if you are working in a company

or if you're getting the data set from a

different team so you didn't find it

online let's say

in that case you first want to

understand the data set like kind of

open it on a jupyter notebook like this

read it

and try to understand as much as you can

and write down questions

about what you don't understand and then

go and find the people who are

responsible for this data set and ask

your questions

like this happened to me before and what

i did is basically like just reach out

to the person who sent me the data file

and then she was like yeah i'm sure but

i someone else sends me these files i

don't really know much about them so i

was like okay

can you give me the name of the person

who sends you these files and i talked

to that person is like

yeah but i just downloaded from a system

so i don't really know how it's

collected so then i'm like okay i'll

just go to the

people who created the system that

collects the data

let's say it's the engineering team so

then i go and talk to the engineering

team's head

and he points me to the person who set

up the system or who's responsible for

the system

that collects the data and then you know

i ask him my question so sometimes it

takes a little bit of

time to get the data to understand the

data etc and kind of feel comfortable

with it

but this is very important because

otherwise you know you don't know what

you're working with

the data set that we're going to work

with today is called the street three

census data set so i don't know if

you're familiar with what a census is

it's basically when they go well what

they used to do is go from door to door

ask people how many people live in your

household how many of them are adults

how many of them are children

how many of them are working how much

money are you making so basically kind

of like

getting a lay off the land of like who

lives in a certain city or a country

but they did that for trees in new york

so um i might have mentioned this before

but there is a new york city open data platform

platform

where you can download a lot of

different types of data sets uh

and this this is also where i found this

i think it's really amazing it's a

really cute data set

so let me first start with reading it on

my notebook and then we start

all right so this is a data set uh it

has a lot of columns as you can see

there are the three dots it means that

it hasn't even been able to

uh show me all the columns but there is

actually a solution to that

if you change the settings to

you know how many columns that you want

to show i mean this basically says for

panda set the option

to maximum columns that will be shown to none

none

so then it will display all the columns

i don't want to set the

option to show all the rows because that

might be a little bit too much so i'll

change the setting and then

have it show me all the columns and yeah

i can see all the columns now

so as you can see there are a lot of

columns and some of them

are not really obvious what they are so

for example this tree id okay pretty

easy block id

yeah you know i i kind of understand

what it means

created at 3 dbh for example i don't

know what that is

uh so to understand what these things

are as i said we have the file

with us here very tiny font

size but at least we can understand what

things are so for example 3dbh

apparently is a diameter at

breast of the breast height of

tree so i guess the breast height of a

person uh

diameter of the tree measured

approximately 137 centimeters above the ground

ground

okay that's cool uh diameter of the stump

stump

i don't really know what a stump is so

when stuff like that happens

what you need to do is google tree stump

to see what it is tree stump surgery has

okay okay that's good to know

um uh

yeah yeah whether

tree is a long or offset from the curb

tree status in the case of the tree is

alive standing

dead or a stump tree health oh okay so

the tree might be a stump or not a stump

so if a tree is a stump i'm guessing

then we only get the

uh diameter of the stump otherwise we do

not have

that so this is an assumption that we're

making but we'll see if that is the case

or not

tree health indicates the user's

perception of tree health

scientific name common name of three species

species

number of signs of stewardship observed

and that is indicate the number of

unique signs of stewardship observed for

this tree not recorded for stems or dead trees

trees [Music]

[Music] um

so i guess steward stewards or are

people who are taking care of the tree

uh that's what i understand good

presence and type of tree guard sidewalk

damage immediately adjacent to the tree

category of users who collected this

tree point

all right so um this is good information

now we know more or less what these

things are

so let's look into our data set a little

bit in more detail

so at first it might seem a little bit overwhelming

overwhelming

to know where to start because you know

we have a lot of column names

and you're like wow this is going to

take a while to explore this data set to

understand what's going on

but i think one important thing is to

understand what your priorities are

so let's say generally i just want to understand

understand

uh the situation in new york about trees

how much of them are healthy how much of

them are not

i just want to get general feel of the

trees in new york state or near new york

city sorry

um so then i wouldn't really be caring

about their location

so when that happens you know then you

can basically just get rid of

all the columns that are giving you some

information of like which state they're

in which borrow they're in

uh which city which uh street they're in

et cetera

longitude and latitude of the tree etc

etc so

for now i can actually get rid of these

things and

i will make a list of all the columns

and uh to make it easy what i use

is the columns function it will give me

all the columns so it's easier for me to

delete the ones that i'm not interested

all right so this is this is more

manageable um these are all basically

if there is a problem caused by a stone

on the root if there's a problem on the

root caused by a great or other

same with the trunk and same with branches

branches

that's easy to understand the latin name

of the tree

if the person who was collecting the

betas thought it was healthy or not

at the status of the tree if there's damage

damage

on the sidewalk right next to the tree

if there are any other problems

um okay this is good i can work with this

this

so one of the first things that i want

to look at is the numerical

values so the numerical values that we

only have are the

diameter of the tree and diameter of the stump

stump

but as i figured out if this tree is a

stump so if it's cut already

then we do not have the diameter for it

which makes sense

but actually even before that i want to

see if there are any null var

this is how i can see if there are any

missing values or not so it looks like

with health

we have a bunch of missing values so how

many values do we have 683

rows and out of the 683

31 000 is missing

for these columns so i want to see what

it looks like when these values are missing

missing

so i'm going to say show me all the ones where

where

all right so it looks like they're kind

of like none

for the same things i can decide to

remove these values later or not that's

kind of up to me at this point

but you know i'm just exploring i'm not

doing i'm not taking any action so this

is something that you can take note of

you can say hey there are a lot of

missing values

i just want to get rid of these ones

okay this is good

um so let's do describe to get a general feel

feel

okay so as i said we only have two

numerical values well the first one tree

id is

actually a categorical value because

it's the id is not a continuous value

but it currently sees it as numerical

value and we can see that if we do d types

yeah most of them are strings but the

first one is an integer

so as an integer because the id of the

tree but it's actually

uh should be a categorical value so what

i'm looking at is here the tree

diameter and stump diameter so

yeah we have these many values the mean

is 11

centimeters standard deviation is 8 centimeters

centimeters

um okay

these are some tiny trees and maximum is 450

450

that's uh really

the user collected another diameter of

the height of the tree

integer diameters we measured and

close to both living trees um

types are more accessible than forestry

specific let me

make it close because measuring tapes

are more accessible than forestry

specific measuring tapes designed to

measure diameter users originally

measured three circumference in the field

field

to better match other forestry data sets

the circumference value was subsequently

divided by the pi

to transform into diameter both the

field measure

mint and process value were rounded to

the nearest whole inch so okay

it's not a centimeter it's an inch so

this is important knowledge for us

i don't know what an inch is so one inch

in centimeters is 2.54

so that actually is like even more in

centimeters so if it's like a hund

450 inches that's like

a lot of centimeters 450

um okay 1000 centimeters

it doesn't sound likely but let's

take a closer look at this actually so

what i'm going to do is i'm going to create

create

some histograms because i want to see

the distribution of these values so

let's say hist

and i already want to make the bins a

little bit bigger

yeah it spins and also want to make the

figure size

all right it's interesting so again i'm not

not

looking at the tree id as i said it's

not really something that's uh

important stump diameter

uh it's close to zero most of the time

but then

uh i don't know if you watch some other

videos of mine but i think i mentioned

this in the hands on data science course

if you can see this value here that

actually means that there needs to be a

value here because that's how the

histogram histograms are being made so

if the maximum value was 60 the histogram

histogram

range the x-axis range would be from 0

to 60 but if

if it goes to all the way to 140 means

there is a value somewhere here even if

it's just one

uh same thing with the three diameter

so i think something went wrong there

probably when someone was trying to

you know put the value in they

actually want to write like 45 but i

accidentally wrote like 450

so we don't know that so i also want to

see like how many values

are here or how many values are here so

it looks like a logical way to cut this

off is maybe like 40 here because as you

can see there is still some values here

and also maybe like 100 inches here

even that sounds like unreasonable to me

to be honest but

um so let's see let me let me visualize

that a little bit so

give me all the trees where the tree

diameter is

bigger than we're going to say like 50

to be honest

so we have 300 values

that is where the diameter is calculated

or measured to be

bigger than 50 inches uh

interesting so what if i visualize this

okay this is more or less what i

expected but let me make this figure a

yeah there are some here which i guess

expected but like

especially after here it's kind of like

you know they're one or two trees

this uh seems very silly to me that

there will be a tree

whose diameter is 450. i mean

yeah even even the circum circumference

for 250 inches sounds like

ridiculous uh but yeah this is just some

information that we have right now

good to know good to know um we can do

the same thing with

all right similar similar thing i guess

maybe until up until this point it's acceptable

acceptable

maybe they forgot to change it to

diameter they put in the

circumference and yeah and these values

are kind of like

not really correct could be

uh yeah all right i mean i guess 140 inches

inches

is not unreasonable if it's uh

the circumference and they forgot to put

it into diameter otherwise it should be

like a very big tree right if the

diameter is

140 inches and that makes like a lot of

centimeters up makes like three meters

or something

a three three meter diameter is kind of like

yeah that would be a bit too big i guess

i'm just trying to like work it

in my head you know i'm trying to

understand if that's like actually

unreasonable or not

but yeah when we move to the cleaning

bit i guess one thing that we would need

to ask ourselves is

is this really unreasonable is this not

really unreasonable and what can we do

with this data point

or data points um

what else do we want to look at i want

to see what are the possible options for

some of these

other columns so latin name probably

there are a lot of different latin names

but you know it could be interesting to see

see

the distribution of different names so

okay i guess this tree is very common

and then we have less common trees here

and one thing you can do to visualize this

this

is to turn this counts into a data frame

and then plot it or maybe

not histogram i guess plot

let me try a bar chart that should work

yeah okay cool yeah i mean not the most readable

readable

chart in the world but at least it gives

you an understanding of uh you know

how many trees there are and which types

there are and this is kind of expected

right you would expect some trees to be

like very common and then as you go it's

like less and less common

all right what else can we look at so um [Music]

[Music]

i saw that yeah there are stewards right

some of the values for the stewards are missing

missing

uh sidewalk is missing a problem is missing

missing

but i want to see like what are the

options for

stewards so we can see here one or two

or none

but what are what are some options

okay so it's either none one or two

three or four four or more

this is good it looks very um

standardized so you know if everyone had

to write it down by themselves you can

see someone writing one or two

someone else writing like this someone

else riding like this so

it's it's possible it's good to see that

it's clean

uh i want to see the possibilities for sidewalk

and no damage or damage okay good

um i guess for these ones it's either

yes or no

i'm guessing this could be like a you

know website where they fill in a form

so this looks pretty standard

um i want to see the status and curb

location also

let's do it quickly uh-huh

curb lock was it

no what was it curb lock with the underscore

on curb or offset from the curve okay cool

cool

doesn't seem to be neat for uh extra

correction there um

all right one last thing that i want to

look into is

if there is um some mismatch between you

know this tree being a stump

and uh the health of the tree for example

example

if there is any point where it says it's

a stump but it says like health is

good or something like that so

where uh what is the name of that column

uh okay then you know then i can say stumps

[Music]

so this is like a new subset and

it's all the values none for the stumps

no oh so maybe these like stewards

sidewalk problems

health stuff are none for all the stumps

and all the dead trees so let me

961 so you know there are 17

654 stumps it says and 13

9006 dead trees that amounts to 31

615 uh

total dead or stump trees

so basically how many values did we miss

yeah it's more or less the same so i guess

guess

what happened is if the tree is not

alive they didn't

either bother or they didn't think it

was relevant to fill in the information

for the rest for the health latin name steward

steward

sidewalk problems etc and probably for

these ones to just like put zero or

something like that

okay so that's that's good to know

um and also one other thing that i want

to look at is

okay so what i wanted to see is

actually how many yeses and how many

nodes there are for each of these columns

columns

uh of course it's going to seem really

instant to you that i

achieved this but i actually took like

20 minutes or something looking online

to see as you can see from my

searches here um how i can see this

information so basically

uh i found that out so what i need to do

is just

assign this to a data set what should we

call it um

so this is a data set and apparently the

you just apply the value counts function

to each of the series

and then you're able to see the values

for all of them so

okay so let's see it looks like rootstone

rootstone

problems seem to exist a lot of the time

uh yeah no other problem exists that much

much

so yeah so problems caused

on the route by stones is a big problem

uh yeah this is just good information to

have you know these are also things like

when you're starting a project these are

some information some statistics that

you can give to whoever is responsible

for the project or if you just want to

like kind of

show that you are progressing with the

problem or with the project

these are some good information uh to

show to people you can also turn this

into like a visualization and show it

that way

if these are relevant things to you okay

so we what we did is

we first looked into what are the

possible features

columns that we can use we decided that

we don't really want to get into the

details of where the tree is located

which borough is responsible from the

tree which street it's on

uh so we haven't been look look we

didn't look into that because we decided

that's not important for

our purposes um we looked into the

columns that this data set has

we chose the relevant columns only on

this relevant columns we first looked into

into

the missing values uh again we haven't

done anything with these things yet

that will be in the next video and we

looked into

um you know if these missing values

happen all at the same time or not

then we looked into the uh numeric

values and how they're distributed in general

general

we looked into the distribution the

histogram and so that some of the values

are look

are looking a little bit suspicious so

we went deeper in those values

and look how how many of them are this

outrageously high so

if you saw a really um

even distribution here that there are a

lot of dots here and there

then you might think that okay maybe

this is normal to have but when you see

there are only a bunch here the kind of

like outliers

then you can decide okay maybe this was

a mistaken

uh input by whoever was collecting data

same with stumps that we saw this so

uh and then we looked into some names

how the distribution of different types

of trees

is in new york's city and

some just to make sure that all the

categorical values are standard and

there is no

different terminology you know as i said

from one to two there's not like one

dash two or anything like that so

just to make sure that the values are

standardized here we looked at

some of the or most of the columns

and we actually figured out that when it stumps

stumps

or when it's a dead tree the information

on health

left names that whether it has a steward

or not

uh whether it's on the sidewalk or not etc

etc

if there are any problems with the tree

it has not been

recorded this is a good thing to know

about the

data set and we saw that

the basically the distribution or how

many problems what kind of problems

there are

on trees so this is a good place to stop

and from now on what we're going to do

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YouTube Transcript:How to Do Data Exploration (step-by-step tutorial on real-life dataset)

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YouTube Transcript:
How to Do Data Exploration (step-by-step tutorial on real-life dataset)