Hello and welcome to the very first session of this covid-19 data analysis project.
So this is exactly why Jupiter ID, where we have to do a lot of analysis depending upon what problems
teckman we have.
So this is exactly the very first assignment for this particular session in which I had to do a lot
of data, because before going deep into our analysis, we have to perform lots of analysis on our data.
And you will figure out over here, these are all my datasets on which I have to do a lot of preparation
before doing any sort of analysis.
So let me open my book ID and let me board some basic stuff over here.
Let's say Panda's number by matplotlib, Seabourne floridly and all these validation, liability and
data manipulation.
Labadee So very first, I'm just going to import my bonders, which is extensively used for data mining,
pollution and extraction as well after it for some numerical computations on data, you guys can import
your name by module and it creates its Ania's as an IP or you can say it's shortcode as an IP after
we have to import something known as which is exactly my matplotlib.
And from our play we have to import something which is plot and tusked.
It's Eleazar spelled after it.
I have to import something which is exactly my Ascendis and just create its Aliya's as Ascendis.
Let's say I have to deal with my operating system as well.
So for this I have to import my OS module as well.
That's it.
Just executed all the stuff over here.
And now what I'm going to do, let's say I'm just going to copy this entire thought from here and using
this OS module, I'm just going to call a function which is List Dayak, which is exactly my list of
all the directories available at this particular port.
And this is exactly that path.
And if I'm going to execute it, you will see you have all these stuffs available at this particular
part.
You will get all your data sets available at this particular like let's say I have to store it somewhere
else.
Let's say I'm going to say this is exactly my files and I'm just going to print it as well.
Let's say just execute it.
And this is exactly that list.
Using that list, you can do a lot of preparation, ordering data as you will figure it out over here.
You have multiple datasets over here.
It means you have to read your data again and again.
So why not try to create a function?
Because function will make this task much, much easier over here.
So let me define some function over here that would define a function using this def keyword.
And let's see, my function name is nothing but like to read on this code data.
This is exactly the function name and in this function name, whatever parameter I'm going to pass using
that parameter, I can definitely read my data and I can create its data.
That's what this function will do.
So here what I'm going to do very first, I had to use all I can say.
I have to call a function, which is exactly my read on see, which is exactly this one.
And here you have to pass some part and some filename.
So this function will receive whatever part Hambling to Pastoria and whatever filename I'm going to
pass over here.
So here I have to mention is part and then I have to add my forward slash over here.
So this is exactly my forward slash after it.
I have to say I have to give my findings, so I have to just pass this filename as well and whatever
data frame it will return me.
So I'm just going to say just return.
And if I'm going to execute, all of this stuff gets executed.
Let's say what I'm going to do now.
I have to simply call this function.
And very first in this parameter, if I'm going to test shift plus over here now, you will figure out
what exactly the path, what is your filename?
Let's say I have to create a variable, which is exactly my part.
And in this part, I have to restore my part.
Like I'm just going to paste over there and here.
I'm going to say this is part and let's say my name is the thing.
But very first, let's say I have to read this one so you can just copy from here and just paste where
here.
And if I'm going to execute it, it will return me this amazing data frame by writing just a single
line of code.
You can definitely get your data frame.
I have to store it somewhere else.
Let's say I'm going to say this is exactly my floor and that's it, just executed.
And if, again, I'm going to call had or tell to get a preview how exactly my data looks like.
This is exactly amazing stuff that you have got by calling.
This function, so let's just see what exactly the power of function in a similar way, in a similar
way.
You can read each and every data set available over here.
Let's say I have read some more data.
That's the very first I have to call this function.
And in this function, the first parameter that I have to parse, which is exactly my part that I have
defined over here.
And a second parameter, what exactly is my filename?
Let's say I'm going to read this day, underscore why CSFI.
So here I am going to say this is nothing but files of.
And here I have to pass index of that file, which is exactly secon.
Alexiev whatever it Willetton me, I store it.
Alexei's data family name is nothing but Deerness.
Cari's just executed.
Now what you have to do, you have to just copy all the disturbed space to here.
This time let's say you have to read this full Alesco or Kesby.
So here you have to pass.
The index has three and whatever data frame to lay it on me, I have to simply store.
That's it.
And I have to just execute it.
Now what I have to do, I have to read this USSI country in the score.
I start CSFI and this time I have to pass its index as four and whatever data frame it will return me,
I'm going to name it as let's say, USSI on next call data.
That's it.
That's all up to you.
Whatever name you want to assign after it.
What I have to do next at this time I'm going to read this Koed and the score 19 this one.
So its index is one.
So yeah, I'm just going to pass its index as one and after it what I have to do, I have to provide
some data for me here.
I'm going to say data frame name is nothing, but probings underscore data.
That's it.
And if I'm going to execute all the stuff successfully executed over here, let's see if I'm going to
call shape or not.
You can easily see this data frame has that much number of rule and it has that much number of columns.
So that's all about this.
In all of our upcoming session, we are going to deep diving double analysis and we are going to analyze
this data.
And in all of our upcoming session, we are going to analyze this data up to a greater extent.
So that's all about the session.
Will love it very much.
Thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.