Halaal, let's have a quick overview of what we have done in our previous session, in the previous
session.
We have imported our data and we have collected our entire data so that in all of our upcoming session,
we will do the analysis we will do with our beautiful results.
So that's what we have done in the session.
So let me open the Sandeman for this session, which is exactly this one where we have to prepare our
data for the analysis purple, because you will see this is my entire dataset, but still I have to
do lots of preprocessing, lots of data preparation technique so that Antine, we will end up having
some meaningful insights from this data.
So what I am going to do now, let me copy this data frame, which is exactly my final.
So I'm going to see a final copy because final is exact, actually the frame and whatever modification
I am going to do now, I'm going to do in this D.F. That's why I am going to copy this.
So I'm just going to suggest execute it.
And if I'm going to call this had over there, you will see this is exactly your preview of the data
frame on which you have to do certain kind of analysis.
And if I'm going to say what exactly the data types of each and every variable on each and every column,
you can call this data and you will also help.
This data is still of object data type because Banas by default assign this date time as your objective
to type.
But we know this data and support some timestamp format.
It means we have to convert this.
We have to convert this data into some timestamp format.
So for this, what I'm going to do, I'm just going to say b'day dot to underscore the time.
And here I what I have to say, I have to send this daytimes here.
I'm going to say I have to send this date slash time, which is exactly this one.
And here, if you will shift plus tab, you will get all your documentation of this function, whatever
custom parameters it will receive.
So here you have a barometer, which is exactly my format, but it means in what format you have to
convert this into daytime.
So here I'm going to say I have to mention some format to some custom format here.
I'm going to say my format is nothing.
But let's say I'm going to say Munt very first.
I have to say Munt, then I have to say date.
Then I have to say my total one, which is exactly eer.
So I'm going to say this eer once I have this format, then I have to mention one hour, minute and
second as well.
So now I'm going to see this placenta's capital at it means I will then I have to mention something
my mainard so I'm going to say capital M which refers to my Mynatt that I have to mention something
which is exactly my second.
So here I have to say this is exactly my second one.
I will do all this stuff then I have to update my this date time as well.
So here I'm going to say I have to update it as you can press that as well.
And this is exactly that feature that you have to update.
Once you will perform all these stuff, then what you have to do, you have to just executed.
It will take approx one minute depending upon what specification you have now.
All this stuff gets executed.
And if I'm going to call my data there, you will see.
Now, this feature supports my daytime form and this is exactly what I need.
And if, again, I'm going to call my doctor had over there now you will see over here, this is exactly
my daytime feature.
So from this daytime feature, let's say you have to perform some analysis.
So what you can do, you can fetch your ear, you can fetch your mind, you can fetch your day, you
can fetch our league days and lots of things from this date.
So this is exactly my approach.
I'm basically going to find some I have the attributes from this data because if you have to perform
analysis in that, you have to factor this derived attributes from this data.
So let's say I'm going to say from this D.F. of daytime's, the very first I have to access this data
of data.
You can just press tab as well.
And on this, if I'm going to call this D dot de underscored name and if I'm going to execute it now,
you will see with respect to each and every index you have some reflects it.
I have to store it somewhere.
Let's say very first I have to define that column.
So I'm just going to define as let's say this is exactly my did whatever name you want to assign, it's
all up to you.
So just executed after it.
What I'm going to do, let's say I'm going to say I.
Some days before this, what I can do, I can call this dot, dot and whatever day it will return me,
I have to store it somewhere else.
So I'm going to say deal of, let's say, DFG all day once I will execute this stuff.
So it will exactly return my day with respect to each and every death.
Once I will do all the stuff I need, my Mynatt hour and month, so forth, is what we guys can do to
access your minute.
You can call Dot Minett over here, which is exactly this one.
Once you will executed it, will it.
And enough to let me store very full.
So I'm just going to say it will whatever it will hit on me, I have to store it in minute column.
So this will exactly away how you can define a new column in your data frame.
Now what you have to do, let's say you have to access your money for this.
I'm going to say Dortmunder, so it will exactly return me my money so I have to store it somewhere
else.
So I'm going to say DFI of of month.
Whatever name you want to assign, it's all you want.
You will do all the gist of what I'm going to do.
I'm going to say I need some hours for this.
I'm going to say dot our lunch.
I will do all this stuff here this time.
I just need some hours for this.
I have to just store it.
So here I'm going to say D of all our ones, I will do all the stuff you have to just execute it.
And now what I'm going to do, I'm just going to call ahead to get a preview how exactly my data looks
like.
Now you will see over here, this is exactly what we did.
This is exactly what day.
And these are all these derived features from your data.
It means up to some extent, your data is somehow ready and you can ready to perform lots of analysis
on your data.
And if, again, I'm going to call my dad over here now, you will see with respect to each and every
future, there is some data that also gets assigned.
Now, your data frame almost ready.
Now you can go ahead with your analysis.
In all our upcoming sessions, we are going to do lots of analysis.
We are going to deal with lots of complex problems, statement with this data and in much, much depth.
So that's all about decision.
Hope you love it very much.
Thank you.
How nice to keep learning.
Keep growing, keep motivating.