Halaal, before going ahead in this session, let's have a quick recap of what we have done in this
particular project.
So from collecting the data, are doing lots of analysis, doing lots of preparation on data.
And in the previous session, we have basically performed this trend.
Yeah.
How exactly room price per night were I over the moon?
So this is exactly my train for the resort and this is exactly my train for the city hotel as well.
And apart from we have had some meaningful insight as well.
So in this session, we have again do some lots of analysis on data, the very first one, which are
the most busiest month and all you can see on which month, my guests are highest.
So this is exactly you have to understand in what month guests are coming from.
It means you have to consider both the data framed.
It is back to your resort hotel as well as with respect to City Hotel.
So let me give you a quick overview of how exactly mine data looks like.
So you will figure out over here this is with respect to your resort hotel.
Now, in this data and the ski resort, what you have to do, you have to count in each and every month
what exactly the total number of count of guest far is what I have to do.
Very first, I have to access my data and resort, which is exactly this one, so that if I have to
exit this feature and on this I have to just call my value on the school council, just execute this
stuff over now.
And this is amazing stats simply you have to just convert it into some data frames.
I'm just going to say reset underscore index again, executed.
This is your amazing stats.
Lexapro, boosterism, modification.
Let's say this is exactly my thought on a scale resort.
And now what I have to do, I have to simply rename my column name.
So I'm going to say that Gollum's is close to nothing more.
The very first column name is exactly my mom.
The second column name is nothing but number of guest after eight.
What I have to do, I have to simply print my russianness resided a framed exit.
So this is that amazing data frame that you have to consider for you.
And after it, what I have to do, I have to simply consider my data, underscore city.
So here I'm just going to say it is exactly my rush on this score city.
It is also my rush on a score city.
It is also my rush on the city just to execute the set.
And you will see the difference with respect to this resort hotel.
You have that much number of guests in August, but again, you have a similar problem.
Again, you don't have a proper hierarchy in your data.
It means whenever you are going to conclude from this data, you get some improper conclusion.
It means you have to assign a proper hierarchy to this month column.
So for this, we have defined a function which is exactly my short data, which is exactly this one.
But very first, you have to merge both these data frames.
So far, this is what I have to do.
I'm just going to say Rush, underscore, resort, not much.
And here are my second data.
Feinglass Rush, underscore city.
Now, I have to say on what column basis I have to merge it.
So basically on the basis of my month column, that's it.
So after having all this stuff, I'm going to say its name is nothing but fine and a school trash,
so just executed.
And if I'm going to print my fun and rush, you will see these are all my data frame, Aleksi.
I have to do manipulation is still let me rename my column name so far and as Goldrush dot columns,
nothing but the very first one is exactly my next month.
The second one is, let's see my number of Gastón results.
I'm going to say number of guest in resort.
And after the third column name is exactly my number of guest in, let's say, City Hotel so that these
are exactly all the stuff after it.
I have to just print my final score.
So I'm just going to bring this frame now.
You will see over here all your stuff gets modified over here.
What you have to do, you have to just assign proper hierarchy over here for this.
I have to just call my short on a data function that what I have defined in the previous session.
So here, if you will, press shift goosestep.
The very first is your data frame.
The second is what column name to which you have to assign you a proper header.
So my data frame is nothing but underscore.
Rush, my column name is nothing, but which is exactly a month, so I have to just execute this function,
it will it is this amazing stretch.
Let me just save as finite and as gold rush, which is exactly this one.
And if I am going to print my final rush, so this is exactly my final data frame that I have to consider.
So now I exactly need.
Yeah.
Which are my most busiest month.
It means up to some extent I need some kind of trend.
So whenever you need some kind of trend, always go for your landlord.
So here I'm just going to say pics dot line.
So here I'm going to start line press, postop data frame and all these custom parameters.
You have to just assign some value.
That's a piece of collecting.
So I'm going to say, which is nothing more final and a rush on X-axis.
What I have to assign I have to basically assign my month next simple on Y-axis, what I have to assign.
So here I'm going to save on this gold rush dot column.
So you will figure out these are exactly both the columns that you have to assign on your Y-axis.
So I'm going to just paste over there and let me assign some title as well.
So I'm going to say my title is nothing.
But let's say total number of guest for one of these are exactly my title.
So just execute the set.
It will return as this amazing statistics.
You'll see this is with respect to your city hotel, this one, whereas with respect to your resort
hotel, you have that much changed because I have to conclude from this, then I can see the City Hotel
has more guest during obviously spring and autumn in July and August.
There are less visitors you will observe, although prices are lower.
Whereas with respect to this resort hotel, you can say Ghast, member for this resort hotel, go down
slightly from June to September.
You will figure out this one and definitely which is also when the prices are highest.
That's what we have analyzed in the previous session, which is exactly this one.
So here you can also analyze in the zone.
Obviously, both hotels have the fewest guest during this winter.
So that's the type of analysis, that's the type of influence you can extract from your visual.
So that's all about the session.
I hope you love it very much.
Thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.