All before going deep dive into this session, let's have a quick recap of what we have done in this
project.
So doing lots of preprocessing, doing lots of analysis, and we have some various insights from the
data.
And in the previous session, we had basically automate our analysis as well as we have performed this
spatial analysis as well to get a clear cut off at what places we have a maximum.
Right.
That's what we have done in April session.
So in this session, what we have to do.
So let's go ahead with the assignment.
The very first problem statement is we have to do a lot of data preparation.
So very close do have to perform this analysis, this data preparation on this overall data.
Jan, June 15, it means you have to read this data.
So very first, what I have to do.
Let me copy this entire report from here and here.
I'm just going to say I have to read this data ready for the very first.
I'm just going to say, please do not read on this CSV and here you have to just paste it.
And on this, if you are going to pass tab over here, you will get all these stuffs available at this
particular spot.
So you have to basically read this one slide and this will exactly return some data.
So let's say I'm just going to store that data frame.
Let's over underscore 15, whatever.
It's all up to you.
So just executed.
It will definitely take some couple of seconds depending upon what specifications you have.
So all of this stuff gets executed and a fine to call just had so that I will get a preview how exactly
my data looks like you will see your despatching based number, your pick up date.
So this pickup date will definitely play a very major role in your analysis.
The third one, what exactly you're affiliated with?
No, the fourth one.
What is exactly exact location.
So let's say if I'm going to check what exactly is a data type of each and every column name.
So you will see this pickup date by default by now, assign it as the string.
It has an object type, but we know its support timestamp format.
It means we have to convert this.
That's what we have done in our first session.
So let me let me show that what we have done in the first session.
Yeah, this is exactly that code.
This is exactly that code that we have written in our very full session.
So let me just copy.
Let me just paste or here you have to do some modifications for what exactly?
My frame name, which is exactly what it is.
And here I have to say, what exactly is my feature name to my feature name, anything but pick up underscore
date, which is exactly this one.
So after doing all these things, what I have to do, I have to update this column as well.
So here I'm going to say just based all this stuff.
So if I'm going to execute it, it will take a while to showcase all these things.
And if, again, I'm going to call this div, so here you get a atter as this does not support this.
This.
Yeah, because you will see or hear in this you have a different format because here you have a very
first.
What exactly is your ear.
The second one is your month and the third one is your date.
So let me do some modifications over here.
Yeah.
So the very first what exactly might you.
So this is exactly my ear.
After it, I have to assign this hyphen.
The second one is exactly what is my money.
So I have to say this is exactly my month and I have to remove this backslash.
Similarly over here, I have to remove this backslash.
And here I have to say what exactly is my date?
So this is exactly my date and this is exactly my other minute and second.
And again, I'm going to execute it.
So all of this stuff gets successfully executed over here.
And now you will see.
Now, this supports some data for me.
Now from this pick up date, you have to basically access what is your ear, what is your day, what
is a weak day, what is it may not month and hour all these types of time.
So let me access all these things, because these derived features, these Derat attributes will play
a very major role in your analysis.
That's what we have performed in all our seven sessions.
So let me access what exactly the week day, what is a day and all these types of things.
So very first you have to just azziz this.
So I'm just going to copy.
You can type manually as well.
So here you have to say very close.
Let's say I just need what exactly is weekdays here?
I'm going to say Danisco name.
So it will exactly determine my weekday, so I have to store it in a column.
So here I'm going to say very first I have to access my data for.
And then I have to define a column.
Its name is nothing, but lets its name is regarded once after doing all this stuff.
What I have to do, I have to now need a date for this.
I'm just going to call this door David here.
So it will exactly return me my day.
So I have to store it somewhere else.
So here I'm just going to say over underscore 15 and this time I have to store it in, let's say, our
day column.
So here we have to define using this once after doing all these things.
What I have to do now, I just need a minute, month, an hour.
So let me say I have to access my Myners for this dark minute will give you exactly what exactly Illuminati's.
For days now I have to define a column which is exactly this minute, which is exactly this one.
Once after doing all this stuff, what I have to do, I have to access my money.
So Dorte Month will exactly give you a month.
So now you have to store it somewhere else.
Let's say I have to define a column for this.
I'm just going to say it is nothing but column name is that same month after doing all this stuff.
Let's say I'm just going to call this day dot out here and this time I'm just going to say over 15 and
this time I have to define some of it over here because I have to store my data.
So now just execute all this stuff, all the stuff gets successfully executed.
And if I'm going to call ahead how exactly my data frame looks now.
So you figure out you have five new features that will definitely play a very major role in your analysis,
which is exactly my big day.
What is my day?
What is my Mainard, what is my Mond and what are all these features?
So let's go ahead with our next statement in which I have to analyze what exactly is at or because in
monthly device in New York City so far, this is what I have to do.
Very first, I have to excuse my Munt column.
So I'm just going to say over underscore fifteen of month and on this, if I'm going to call this value
in the school counsellor there.
So you will definitely get a count with respect to each and every month.
Now you get easily visualize with respect to each and every month how much number of rush you have.
So you know what you have to do.
You have to simply visualize this data.
So far this either you can use a bar plot or a pie chart.
It's all up to you next time where to use bottled water so I can use bar chart from tautly matplotlib
seabourne, whatever you want.
But I decided to go ahead with Bloche because whenever you are going to work from some real world aspect
at the time, this plays a very important role over there.
So I'm going to say B X dot bar and if you will pass plus tab, you will get entire documentation of
this function.
So I'm just going to say on this X axis, what I need, I just need X index.
I'm just going to say I just need index.
And on Y axis, I have to just copy this content and on why I have to just pasted that set and now I
have to just execute this set.
It will definitely return my beautiful bar chart over here.
And you can easily visualize in this six month we have a higher number of pickups.
And if we have to if we have to conclude, if we have to inference from this, then we can say an area
number of over pickups has been Stoodley increases throughout the first half of the two thousand fifteen
in New York.
So that's the type of analysis.
That's the type of interest, your conclusion, how you can get from your data, how you can set your
visuals.
So that's all about decision.
Hope you'll have the session very much.
Penghu, how nice to keep learning.
Keep going.
Keep practicing.