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Well, before going deep dive into the session, let's have a quick overview of what we all have done

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from all our initial session.

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So from doing lots of analysis, doing lots of preprocessing, doing lots of cleaning on our data,

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we have represented some of the meaningful inside the scorable at this box plot by the line chart.

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And definitely we have found what exactly that relationship between our special request and in this

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is canceled feature.

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So we have analyzed all these things.

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So in this particular session, we have this assignment.

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The very first problem statement is which are a busy month or I can see which are the busiest month

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in which I can say our engagement.

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My guests are high.

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So you have to analyze it or you can say you can find some in data so that you can come up with some

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conclusion.

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So let's see what I'm going to do, let's say in the data frame that we have already fetch, which is

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exactly my data on the school resource.

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I'm just going to call ahead over there so that I will have some preview of my data frame.

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So you will see this is exactly the data frame.

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And on this, if I'm going to say let's say here I have a feature, which is exactly my arrival in the

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school during the school month.

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So I'm just going to say in this data underscore resort.

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So very first I have to exit this data frame and in these very close, I have to say I have to exit

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this one.

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And here I'm just going to call my counselor here and just execute.

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You will see with respect to all, you have that much going on.

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Similarly, with respect to all of that, once you have that much year, I have to convert that into

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a bit of fame.

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So I'm just going to call my research on a school index just executer.

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This is that beautiful little frame that you need, but still, you have to do a lot of manipulation

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in your data.

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You have to do a lot of reporting in this data frame because, you know, this this index that is not

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my exact column.

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It means you have to modify your column as well.

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And after that, you have to provide right hierarchy in your data because you will observe here, you

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will have all this July, October, all these data, which is not an appropriate hierarchy.

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The thought is very first.

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I have to store this data from somewhere else.

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So I'm just going to name, let's say, Rush underscore result.

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It's all up to you, whatever name you want to assign after I have to assign my own column names for

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this, I'm going to say dot columns.

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It goes to nothing but the very first column that I want to assign exactly my month.

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And with this arrival statement, I'm going to say to nothing but my number of guest or whatever you

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want to assign.

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And after that, what I'm going to do, I'm just going to print my data, suggest executer this is a

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date of indefinite, but still you have to do a lot of calculation of what your data say.

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Similarly, what you can do, you can do similar operations for use data, underscore city data frame

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because you have to find a trend for you have to find a trend for the city data frame as well.

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So I'm just going to copy this.

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Let let me just copy all the cells.

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Let me page to it there and here.

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I'm just going to say this is nothing but my city.

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And here I'm going to say this is nothing but my city.

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Similarly, over here, this is nothing but my city and this is also just my city.

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And just executer this is added of with respect to your city data from city hotel data after it, what

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you have to do, you have too much what the data on the basis of this common column.

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So I'm just going to say it underscore resort dot much and if you will, past shift plus tab over here.

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Let me correct this function name.

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And if you address shift plus time, you will see all the documentation of this function.

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So my right the frame is nothing but my rash on this score.

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And here you have on parameter on what column.

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This says you have to merge.

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So here I'm going to say I have to merge it on the basis of my column after it.

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Let's say I have to store it somewhere.

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Selected is nothing like my final on the score, so just execute it.

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And if I'm going to print this final score, you'll see this is exactly the data frame that you need.

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Let's say I have to manipulate my column then.

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So here I'm going to say it is I think what let's say don't columns equal to the very first column,

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starting with mine a month.

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The second goal, anything but my number of guest end result.

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Whatever you want, it's all up to you.

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The second that.

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Yeah, this.

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The third.

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And there's nothing but my number of guest in, let's say, City Hotel, and if, again, I'm just going

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to print this in a slow rush.

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So this is that manacle attitude.

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But still, you have to provide right hierarchy to this month, because if you are trying to fetch a

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conclusion from this set of frames, you will get some improper conclusory.

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So you're ready for whatever else I have created earlier with respect to this one, which is exactly

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this.

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Ezzedine.

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So I have to use this.

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I have to use this model so that you can copy it or you can use your earlier Elísabet.

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It's all up to you, so let me execute it.

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And now using this as I have to call a function, which is my short underscore it of fame by this month.

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And here I am going to say my deal is nothing but my final underscore rush and my Munt column name is

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nothing.

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But on what column basis I have to go either right hierarchy.

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So I have to provide that hierarchy in my month column.

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And here, let's say I have to update this to time would say nothing but my final rush to suggest executed.

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And if I'm going to print this windedness coalition is going to you will see this.

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Is that right?

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Had I called it a frame that you need over here now what you have to do, you have to just you have

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to just call your line over here to, like, say what I'm going to do.

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I'm just going to say B X dot line because I'm going to use my plotty model.

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You can also go ahead with some advanced stuff, like, look, are many other resolutions.

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And definitely you can also go out with McClarty and see what they are.

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If you are looking for some real world scenarios, is matplotlib and Seabourne are not going to be very

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handy because they didn't provide you with some deployment level result.

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That's why I try to go ahead with all always deployment level visual.

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Same with Brockley book and many others as well.

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So here I'm going to say, if you were start a very first parameter is exactly what is your data frame.

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So I'm just going to say my data frame is nothing but.

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My final underscore rush to yeah, and here on X-axis, I have just this month, so I'm going to say

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it is my money.

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And here on Y-axis, basically, let me just call our columns over years.

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I'm going to say fine and rush on the school to dot columns over there.

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And you will need these both columns in your data frame or in your this line functional Plotnick.

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So here I have paste all these things after what you have to do if you have to assign some titles so

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you can assign it as well.

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So I'm going to say the title is nothing but my, let's say, total number of guest per month, total

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number of gas per month.

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So just executed.

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And this is a beautiful trend that you exactly need that you all are waiting for.

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And if you have to conclude, if you have to conclude from this vision, you can see the city hotel,

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which is my this this blue, which is the trend of this blue line.

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You can see it has more guest during the spring and autumn when the prices are also highest in July

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and August, there are less visitors you will observe overhead that have less visitors.

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And although prices are also lower, whereas with this back to this this resort, you will also gas

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number for the resort hotel go down slightly from almost June to September, which is also when the

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prices are highest due to October here.

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So you would also bought hotels, have the fewest guests during the winter ahead or so that's a conclusion

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you can get from this beautiful trend, from this beautiful Zeisler.

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So that's all about decision.

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Hope you love it very much.

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Thank you.

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Have a nice day.

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Keep learning.

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Keep growing.

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Keep practicing.