Hello, welcome to the very first session of this project.
So this is exactly my data set on which I have to do certain kind of analysis on which I have to do
certain kind of privacy, lots of analysis.
And at the end, I have to come up with some beautiful insights, some meaningful insight from this
huge chunk of data.
So you will see these are all different, different features in many different.
So very first, I have to load this data frame in my Jupiter notebook.
So this is exactly my general guide.
And by force, I'm just going to report some basic models over here.
So very first, let's say I'm just going to import my partner's module and I'm going to create Elías
as Espina.
So PARNAS is exactly that library that is extensively used in case of data manipulation, data extraction
and data modification and much, much task as well.
So after it, if you have to perform some numerical task on data, you guys can import no library as
well.
So here I'm going to say import number as ENPI after it for vacation stuff.
I'm going to import my matplotlib and from this matplotlib, I'm going to import my daughter and I'm
going to create a Elías BLT after it for better presentation.
You guys can also import and interlibrary, which is exactly your Seabourne library.
So I'm going to say import Seabourne SSME.
So just executer using all this enter and it will execute this set.
And after what you have to do very first, you have to read your data.
So I'm going to say the speed.
And here I have a function which is exactly my read underscore CSB and here you have to pass your where
exactly your dataset is available.
You will see over here, this is exactly my dataset and it is available at this spot.
So I'm just going to copy this part and I'm just going to paste over here.
And after it, if you will press tab, you will get all the stuff I will will add this particular part.
So this is exactly the data fed that you need.
So I'm just going to append are over it so that it will make us to get rid of all the issue that will
come across after importing this data.
So I'm just going to say it is exactly my div and let's see if I want our preview of it to just call
had ordered that set.
And you will see this is exactly the data frame that you need on this.
You have to perform also preprocessing lots of analysis and at the end you have to conclude your visalo.
So you will see for this particular session, I have this assignment in which I have to perform data
planning on our data, as well as lots of data on our data, because whenever you are going to work
for some real work project, your data will be always raw.
You will always have your massive data.
So you have to make radio data for the analysis, but for the modeling purposes, that's why you have
to perform lots of data, including lots of people sitting on your data.
So let's see if I'm going to check what exactly is the shape of my data frame.
You will see it has that much rules and that much column.
And let's see if I have to check whether I have any null value in the frame or not.
So for this, I'm going to say this is null dot values dot any.
And if I'm going to execute it, you will see it has to it means it has some kind of missing value available
in your data.
So let's say I'm going to say I just want summation of all the missing values over here.
So for this, I'm just going to call this is Nelda's some.
You will see this has all these types of different different values in your data available over here.
You will see this.
This country has that much missing value.
This all these features has huge number of missing valleys over here.
So if you will observe over here in this agent.
So if Ajin is not given, so it means booking was most likely made by a person or you can see booking
was mostly made without one and similarly were here incomplete.
So if none is given or I can say wherever I have missing value, it means it is mostly like private.
Similarly, and all the other features we can explain or I can say we can manage it according to our
need.
So here I'm going to say let's say I'm just going to say they've got Filani and here I'm going to say
just fill it with zero and I'm just going to update my data frame as well.
So here I have to pass my in-place parameter as.
Troops were just executed and after it, if again, I'm going to call this some over there exactly this
summer.
Now, you will observe don't have any missing value in your data.
It means up to some extent you data some already for your analysis purpose for the prepossessing purpose.
So let's say let's say yeah, in this in this meeting feature, let's say in this middle feature, I'm
just going to call ya.
What exactly is account of each and every category in this?
So for this, I'm just going to call this value on a school counselor there.
And if you will execute the cell, you will see backfills and that number of count.
Similarly, each and every category has that much number of count.
So after it, let's say if I'm just going to access these children over there and in these children,
if I'm going to call my unique or now you'll see it has all these unique, unique children available
in your data frame in a similar way.
Let's see if I'm going to call this adults, if I'm going to access this adult had on this, if I'm
going to call my Unicode here, you will see it has this unique number of adults in a similar way.
If I'm going to access my BBS and on this, if I'm going to call my Unicode unit, you will observe
it has that unique number of babies available in your data frame.
And from all the stuff that we have fach for here to form this, we can see in our data my children
and adult and babies can't be zero at the time.
So far, this what I'm going to do, I'm just going to create a filter.
Let's end going to create a filter.
And in this filter, I'm going to say very first I have to exit the children.
For the children, it goes to zero so that if this is exactly my very first condition.
So I'm going to say this is my very first condition and I'd say my second condition is the thing would
be half of adults equally close to zero.
So this is exactly my second condition.
And my third condition is exactly where my babies are equally close to zero.
So this is exactly my condition.
And if I'm going to pass this filter, if I'm going to pass this filter in my data frame now, you will
think over here.
You will see over here, it has that much a number of rules and that it means these are exactly you're
wrong entries in your data because you will observe at a time, at a time these values are going to
be zero.
It means you have to remove all these stuff for four days.
What I'm going to do, let's say let's say I'm just going to store it somewhere else.
Let's say this is the frame that I have to store very first or what you can do.
You guys can call you guys can call negation of this filter.
So I'm just going to copy from here.
Just going to paste over here and if you will call negation of this.
So in bonders, you have to use this operator over here.
So just execute it.
And it has that much number of rules in your data frame that doesn't contain any wrong entry.
So that said, this is my letter.
This is my final data frame.
So I'm going to say this is exactly my data.
So just executed.
And if I'm going to call my hat over there, you will observe over here.
This is a data frame on which you have to perform certain kind of analysis, because this data is almost
I can say it is almost your preprocessed data.
And on this data, you have to come up with some meaningful insight.
You have to come up with some beautiful insights.
So that is exactly what I mean.
So that's all about the session hope of this session very much.
Thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.