Halaal, before doing deep dive into this session, let's have a quick recap of what we have done in
the previous session from the previous session.
We are going to continue our analysis with this new data set on which we have to do a lot of preparation.
We have basically fed these five features, these five derived attributes from my pick up date, because
these five features will play a very major role if you have to perform your in-depth analysis on your
data.
That's what we have done in the last session.
Then we have basically wine.
What exactly the kind of tricks in New York in the first half?
That's what we have done in the last session.
So in this session, we have this assignment.
The very first one is we have to analyze what exactly is our rush in New York City?
Or you can say what exactly the rush in each and every hour in New York City.
So for this, you guys can consider your our future for this analysis, which is exactly this one for
this.
I'm just going to say I'm going to use my control over here.
So I'm going to say S.A. dot count plot and hear what I have to mention.
Radical's, I have to mention this over 50 and here I have to mention this over.
That's it.
And let me assign some own figures.
So now I'm going to say plaited or figure.
And this time, let's see, I just need Fugo size of, let's say, 12 Colma six.
So if you are going to execute this set, it will take a while and it will return.
You have very beautiful visuals and you will see your beautiful visors over here.
And this is exactly your distribution, how your rush is going in your New York City.
And if you have to conclude from this visual, then you can definitely see or hear after the morning
rush.
After morning rush.
The number of over pickups doesn't dip much throughout the rest of the morning and early afternoon as
well.
And there is significantly more demand in that evening.
You will see here, here you have a more demand of over gaps in your New York City.
So that's a type of analysis.
That's a type of conclusion you can fetch from your data.
So let's go out with our next statement in which we have to perform this analysis or you can say in-depth
analysis of Rush in New York City and our advice, because here we have performed just our voice.
But now we have to analyze this with respect to each and every day.
So far this what we have to do.
We have to group our day down the basis of the day for this.
I'm just going to say over underscore 15 dot, we have to call this group by on this.
And here very first, you have to group your data on the basis of this here.
I going say on the basis of the ones after doing all this stuff, then I have to group my data on the
basis of obvious ones.
I have all these stuffs.
What I have to do, I have to access my pick up date for this.
I have to access these pick up data, which is exactly pick up on the school date.
And on this I have to basically perform this count of Thaxted and if I'm going to execute it, it will
lead into some beautiful stats over here.
And now you can easily visualize over here with respect to Friday and with respect to that much, you
have that much rush.
It means you have that much number of troops going in.
You knew, OK, I have to create its data frame so that I can easily visualize this data.
So far, this is what I'm going do.
I'm just going to call this reset and index over here and let me store this data somewhere else.
Let's say this will exactly return some data frame.
So I'm just going to store let's say in summary, whatever name you want to assign, it's Ullapool.
Let's say this is exactly my summary and I have to call this summary.
Dot had as well to get a quick overview of how my data frame looks like it's just executed.
It will take a while.
Again, let's say I have to do some modification in this, let's say, rather than this pick.
Did I have to consider this column as number of count number of trips?
Or you can say count whatever name you want.
You swear I'm going to say summary dot columns or you can use your reading function as well.
It's all up to you.
So my very first column name, let's say that is exactly what it is earlier.
And the second one is exactly your, let's say hour.
And the third one is, let's say exactly your let's say count.
And if I'm going to execute it and if again, we.
To call ahead so that to crosscheck whether this modification has done or not, and you can easily with
all these things, has happened successfully now what you have to do, you have to simply visualize
this data because you can easily see or hear.
Here you have a huge chunk of data.
And whenever you have this data and if some data frame, it is how to control that data.
So at the time your data visualization comes into picture selected, I'm going to use our point plot
over here for this.
I'm going to say S.A.S. dot one plot that what we have in our succession.
So if I'm going to shift tab, you will get all the custom parameters, documentation, all of these
stuffs.
So if on this access, let's say I just need my hour, which is exactly this one, let's say on y axis,
I just need my cowens, which is exactly this one.
Let's say I have to split my point on the basis of each and every day is where I have to pass this day
in this parameter.
What's happening, all the stuff I have to say, what exactly minded FEMA is, which is exactly this
summary.
So I have to say this summary is exactly my data from let's say I have to customize my point as well
as I'm going to say figure or figger, just assign some fixie.
So I'm going to say Fixie does nothing.
But let's say to compare it to it's all up to you just executed.
It will take a while, but it will return you some beautiful visualizations over here.
Now you can easily conclude from this visual.
Yeah, definitely this this red line, you can easily see this red, which definitely first to you,
we can it means on Sunday, you have this pattern, you have this pattern.
Whereas with respect to this green line, which exactly stays on Saturday, you have a higher structure
rather than all these Veekuhane.
Let's say you have some money.
Let's say you have this pinkish, which is exactly your rasti.
So basically in your weekdays, you have Ardeche in your mornings, but in your weekend you don't have
that much.
It means because people who are going to office in their early morning or children's choir, going to
their schools have a holiday on that day in a similar way.
You can conclude as much as you can, because here in front of you here, you have your Fabin find blood.
And from each and every point block, you can definitely find some kind of conclusion from this data.
So that's all about the session of the session very much.
Thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.