Well, before going deep dive into the session, let's have a quick overview of what we all have done
from all our initial session.
So from doing lots of analysis, doing lots of preprocessing, doing lots of cleaning on our data,
we have represented some of the meaningful inside the scorable at this box plot by the line chart.
And definitely we have found what exactly that relationship between our special request and in this
is canceled feature.
So we have analyzed all these things.
So in this particular session, we have this assignment.
The very first problem statement is which are a busy month or I can see which are the busiest month
in which I can say our engagement.
My guests are high.
So you have to analyze it or you can say you can find some in data so that you can come up with some
conclusion.
So let's see what I'm going to do, let's say in the data frame that we have already fetch, which is
exactly my data on the school resource.
I'm just going to call ahead over there so that I will have some preview of my data frame.
So you will see this is exactly the data frame.
And on this, if I'm going to say let's say here I have a feature, which is exactly my arrival in the
school during the school month.
So I'm just going to say in this data underscore resort.
So very first I have to exit this data frame and in these very close, I have to say I have to exit
this one.
And here I'm just going to call my counselor here and just execute.
You will see with respect to all, you have that much going on.
Similarly, with respect to all of that, once you have that much year, I have to convert that into
a bit of fame.
So I'm just going to call my research on a school index just executer.
This is that beautiful little frame that you need, but still, you have to do a lot of manipulation
in your data.
You have to do a lot of reporting in this data frame because, you know, this this index that is not
my exact column.
It means you have to modify your column as well.
And after that, you have to provide right hierarchy in your data because you will observe here, you
will have all this July, October, all these data, which is not an appropriate hierarchy.
The thought is very first.
I have to store this data from somewhere else.
So I'm just going to name, let's say, Rush underscore result.
It's all up to you, whatever name you want to assign after I have to assign my own column names for
this, I'm going to say dot columns.
It goes to nothing but the very first column that I want to assign exactly my month.
And with this arrival statement, I'm going to say to nothing but my number of guest or whatever you
want to assign.
And after that, what I'm going to do, I'm just going to print my data, suggest executer this is a
date of indefinite, but still you have to do a lot of calculation of what your data say.
Similarly, what you can do, you can do similar operations for use data, underscore city data frame
because you have to find a trend for you have to find a trend for the city data frame as well.
So I'm just going to copy this.
Let let me just copy all the cells.
Let me page to it there and here.
I'm just going to say this is nothing but my city.
And here I'm going to say this is nothing but my city.
Similarly, over here, this is nothing but my city and this is also just my city.
And just executer this is added of with respect to your city data from city hotel data after it, what
you have to do, you have too much what the data on the basis of this common column.
So I'm just going to say it underscore resort dot much and if you will, past shift plus tab over here.
Let me correct this function name.
And if you address shift plus time, you will see all the documentation of this function.
So my right the frame is nothing but my rash on this score.
And here you have on parameter on what column.
This says you have to merge.
So here I'm going to say I have to merge it on the basis of my column after it.
Let's say I have to store it somewhere.
Selected is nothing like my final on the score, so just execute it.
And if I'm going to print this final score, you'll see this is exactly the data frame that you need.
Let's say I have to manipulate my column then.
So here I'm going to say it is I think what let's say don't columns equal to the very first column,
starting with mine a month.
The second goal, anything but my number of guest end result.
Whatever you want, it's all up to you.
The second that.
Yeah, this.
The third.
And there's nothing but my number of guest in, let's say, City Hotel, and if, again, I'm just going
to print this in a slow rush.
So this is that manacle attitude.
But still, you have to provide right hierarchy to this month, because if you are trying to fetch a
conclusion from this set of frames, you will get some improper conclusory.
So you're ready for whatever else I have created earlier with respect to this one, which is exactly
this.
Ezzedine.
So I have to use this.
I have to use this model so that you can copy it or you can use your earlier Elísabet.
It's all up to you, so let me execute it.
And now using this as I have to call a function, which is my short underscore it of fame by this month.
And here I am going to say my deal is nothing but my final underscore rush and my Munt column name is
nothing.
But on what column basis I have to go either right hierarchy.
So I have to provide that hierarchy in my month column.
And here, let's say I have to update this to time would say nothing but my final rush to suggest executed.
And if I'm going to print this windedness coalition is going to you will see this.
Is that right?
Had I called it a frame that you need over here now what you have to do, you have to just you have
to just call your line over here to, like, say what I'm going to do.
I'm just going to say B X dot line because I'm going to use my plotty model.
You can also go ahead with some advanced stuff, like, look, are many other resolutions.
And definitely you can also go out with McClarty and see what they are.
If you are looking for some real world scenarios, is matplotlib and Seabourne are not going to be very
handy because they didn't provide you with some deployment level result.
That's why I try to go ahead with all always deployment level visual.
Same with Brockley book and many others as well.
So here I'm going to say, if you were start a very first parameter is exactly what is your data frame.
So I'm just going to say my data frame is nothing but.
My final underscore rush to yeah, and here on X-axis, I have just this month, so I'm going to say
it is my money.
And here on Y-axis, basically, let me just call our columns over years.
I'm going to say fine and rush on the school to dot columns over there.
And you will need these both columns in your data frame or in your this line functional Plotnick.
So here I have paste all these things after what you have to do if you have to assign some titles so
you can assign it as well.
So I'm going to say the title is nothing but my, let's say, total number of guest per month, total
number of gas per month.
So just executed.
And this is a beautiful trend that you exactly need that you all are waiting for.
And if you have to conclude, if you have to conclude from this vision, you can see the city hotel,
which is my this this blue, which is the trend of this blue line.
You can see it has more guest during the spring and autumn when the prices are also highest in July
and August, there are less visitors you will observe overhead that have less visitors.
And although prices are also lower, whereas with this back to this this resort, you will also gas
number for the resort hotel go down slightly from almost June to September, which is also when the
prices are highest due to October here.
So you would also bought hotels, have the fewest guests during the winter ahead or so that's a conclusion
you can get from this beautiful trend, from this beautiful Zeisler.
So that's all about decision.
Hope you love it very much.
Thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.