Hello, all, so let's have a quick recap of what we all have done in our all previous session.
We're basically following lots of people, lots of great cleaning, and this is exactly analysis that
we have both in a previous session.
Yeah.
Basically gaffed going from Richarlison and we have Analise.
Yeah.
The guests are mostly from European and Portugal.
So this is exactly the assignment for the session.
The very first one is how much to gased pay for a room, but might selective say.
I'm going to say my getting a preview of Manager Frame.
You will see this is exactly your preview, how your data frame looks like you will see over here.
These are all the columns and all the rules in your data frame.
So here what you have to do, let's say I'm would use box plot or I can say I'm going to use a distribution
of exactly my this resolved in this court room on a school type room, because I have to need a distribution
of how exactly the prices of each and every room that I thought this.
I'm going to say I have to use this box plot.
And if you will press shift to stab, you will get all the parameters of this box plot, what is X,
Y you data and all these types of things.
So on X axis, on this X axis, I have to say, I just need I just need this feature, so I have to
just copy it.
That's it.
And this is exactly my x axis on Y axis.
And I have to use something known as this, this area, which exactly says, yeah, what exactly is
that price with respect to each and every room.
So on y axis, I just need this one.
Then what exactly is my little frame?
Let's say let's say I'm going to say here I'm going to say my data of is underscore cancel is equal,
equal to zero because I need valid bookings.
So here I'm going to say I have to parse this in my little frame so that I will get my filtered it and
I will say this in my nature to just execute it.
And I have to parse this.
Or here's here I would say data to add.
These are all parameters and formulas.
I have to play with something.
Let's say I need I need us distribution with respect to my resort hotel as well as with respect material
as well.
For this, I have to just put this in my new parameters because Hugh is exactly used whenever you have
to divide your distribution on the basis of some feature, let's say I need my own size, I need my
own window.
Say so for this, you guys can say BLT dot figure, which is exactly this one.
And here you have some parameter, which is exactly a thick side.
So I'm going to say my fixed side is nothing but like so I fix this to Algoma it that's my windows have
this to allow it and after it you can play with some other parameters like say what exactly.
The title, let's say my title is the thing, but what is my price of room times per night and per person.
Or you can say person whatever you want, it's all up to you.
And let's say you have to do some modification.
Your calculator.
I want some own custom font size which is exactly of sixteen after it.
What we have to do, we have to set some own label which is exactly BLT X label.
And on this X label you have to say room underscore that it's all up to you.
Whatever you want to assign, let's say on Y label, I'm going to say it is nothing but let's say price
in terms of some currency, let's say price in euro and after it, what we have to do now you will think
why I'm going to consider euro, because in the in this analysis you will observe more subtle guests
are exactly four from European country.
That's what I would say, price in euro currency.
And after what I have to do, I have to just call this show over there to showcase whatever my vision
will be just executed.
This is exactly that beautiful box thought that you need you will see over here with respect to this
Ekert agree it has its highest price approx.
Five hundred euro.
Whereas with respect to this this gigantic room where my hotel is today, you will see this.
Exactly the distribution.
And definitely these are those rooms that are much, much costlier.
And all of the rooms you will observe over here, there is all these data points that you will see,
these are exact outliers with respect to all the rules in this particular room type.
So this is exactly that analysis that you have performed over here.
So let's say let's let's go ahead with our second analysis.
Second part of the statement.
The second one is how does that price per night?
Were I over here?
It means you need to confirm.
It means you need to column the very first one.
What exactly do you want?
And on the basis of what exactly is the price, let's say I have to do this analysis with respect to
my result and with respect to my city hotel.
For this, I would say this is nothing.
But let's say very first I have to access my result of him.
And on this I have to access my is in the score cancer.
And here I have to say is it's nothing worth it is zero because I need my valid bookings and I have
to just pass this filter in my data frame so that I will have my filter him next.
I have to store it in somewhere else, which is exactly my data and a score resort and just executed
after what we have to do.
Similar operation I have to do for my city.
So just to copy, just paste over here.
This time I have to see my data.
Family name and city similarly were here.
My little fame name is next to City and this time my this one data from let's say data underscore city
data on the school city.
Just executor's.
This is exactly the data frame with respect to your city hotels.
Now let's say what I'm going to do.
Let's say I need overview here, how my data looks like just executed.
You will see this is the data frame with respect to city hotels.
Similarly, in case of your this this data result, it will look something like something like this.
Or let me let me show you let me show you some data underscore resort dot had you will see this is a
data frame with respect to a resort hotel.
Now, what you have to do, you need basically two features.
What is your arrival date month?
And on the basis of month, what exactly do you mean price?
So I'm going to say it means it means you have to group your data on the basis of on the basis of this
column.
So here I'm going to say let's say very first I have to access my data and as a result, data frame.
And on this, I have to basically call my group back.
And here I have to group my data on the basis of this column.
And after it, what I have to do, I have to access my ADR, which is exactly my price feature.
And on this, if I'm going to call my meeting or just executed, this is that this is that is stuff
that you need.
Let's say I have to convert it into some data frame for this.
I have to just call this reset and index.
Let's I have to store it somewhere.
I would say I have to store it in, let's say resort, underscore hotel, just execute it.
And after it, what I have to do, let's say I have to perform the similar operations for my city hotel
as well.
So here I'm going to say data underscore city and similarly all these operations.
Similarly on here, my data family name a city and a score to just execute it.
And if I'm going to visualize my city and it's got a hotel data frame, you will see this is the data
frame with respect to City Hotel.
You will see in both the data frame, in both the data frame, you have a common column.
It means you can merge this both digital frame for this.
What I'm going to do, I'm going to use my Banda's much function, like I'm going to say the very first
Netafim, which is exactly my resort hotel, not much.
And what I have to model, I have to merge it to my city and a score hotel.
And if you will shift plus tab, you will get all the documentation, all the custom parameter of this
function.
This is a very handy function opener's.
And here you have something on parameter.
On what parameter?
You have to merge it.
So I have to merge it basically on this parameter.
So I have to just paste over there after it.
What I have to do, say I'm just going to execute it.
You will see this is exactly the data that you need.
But still you have to do some modifications in this data.
Let's say.
I'm going to say I have to store it somewhere else, which is exactly final.
And after it, I have to say I'm going to say final column in this column, the very first column name.
I have to assign it exactly my month, the second column that I have to assign, which is exactly my
price for resort.
And after it, what I have to do, I have to say my.
Price for City Hotel, which is exactly this one, just like you ordered, and if I again going to call
my hat over there, you will see this is the data frame that you need.
But but here, there, the hack or whatever, because you will see my this month go along isn't in a
proper hierarchy.
And if I am going to visualize this data, you will get some improper conclusion.
It means you have to you have to make this data in a proper hierarchy.
Either you can use your own logic or you can use some handy modules or python, but using your own logic
will be definitely very complex.
So let me use some some handy modules of Python.
So if you haven't installed it, you guys can install using this paper.
So I'm going to say pip install short data, frame it frame by month or week, month or week.
And this this library has definitely some dependency.
So it means if you haven't installed this dependency as well, you can install using PIP install shorted
month, month, week days.
So this is exactly the dependency that you have to install for I have already installed in my environment,
so I'm not going to install it.
So I'm just going to import this one, which is exactly this one to just copy, just paste to just do
some modification as short a data frame on this score, this one month a week.
And I had to create a Eliade, let's say Steet just executed and and after eight, what will you do
from this S.T. Alyosha's I have to call a function which is exactly short and screw the frame by month
and if you will, by Chispa step, it needs a data frame and month column name.
So my data is a but my final and my my column name on which I have to make this data and a proper hierarchy
is exactly my money.
Just a sign over there.
Let's say this is exactly my final to date frame that it will return me just executed.
Now what I have to do, let's say I'm going to realize my data frame just executed this.
Is that the frame that you have to consider for your visual exit?
I'm going to use I would use my landlord over here.
Yeah, I can definitely go ahead with my landlord because this data frame is in proper hierarchy, Gen
Fab, March, April, May and all these types of different environment.
So I can definitely go ahead with my life chart, if you will.
Plus tab, you will get all the documentation, all the different different custom parameters for him.
The very first.
What is exactly your data frame, which is my final two then what I want on X-axis, which is exactly
my month, then what exactly I want on Y-axis for the very first one is exactly my price for resort.
Just to copy, just paste.
Just, you can just copy from here and you have to just paste over here.
That said, that's like a piece of cake after it.
What you have to do, let's add it and subtitles as well.
So here I am going to say my title is nothing but let's say room price for night over the over the year.
It's all up to you.
Whatever you want to assign now, what you have to do, you have to just execute the self.
So this is exactly you're beautiful with the additions that you need.
And if you are thinking, yeah, I can definitely go out with my previous claim.
So let me show you what.
If you have your previous data, let me show you you will see this is that in proper controls and this
is as improper with that you have finally work here that we just remove it.
And this is that exactly the frame?
This is the exact visual that you need.
Now, see, I have to conclude this this Veysel, you will see this clearly shows that this this resort
hotel prices are much, much higher during the summer, whereas the prices of a city hotel doesn't get
that much and it is most expensive during the summer.
Or I can see during the summer, during spring and autumn, autumn season.
So it's all about the session.
It's all about our analysis for this particular session.
You will love it very much.
Thank you.
Have a nice day.
Keep learning.
Keep going.
Keep practicing.