Hello, before going at it in the last session of this project, let me have a quick overview of what
we have done in our project and not so from importing the data that we have done lots of costing.
Then we have packed some meaningful insight from this budget.
Then we have some surplus than we have in France from it as well.
Then after it, we have performed some kind of this point cost analysis.
After it, we have performed this heat map.
Then we have automate all this stuff after we have performed some kind of a speech analysis on our data.
And this is exactly that, a spatial analysis after we have automated all these things.
And in the very last session, what we have done, we have done what exactly that has ongoing in our
New York City with respect today, with respect to our all these things.
So in this session, what I have to do, I have to read this data, this one, and I have to perform
analysis on this one, which is exactly Ortberg, Jan five.
While so very first I have to read this to let me copy this, but we are exactly right.
It is available over here.
So here I'm going to say B'Day Dot read Six Feet.
And here very first we have to mention that particular part just passed and this is exactly the data
set that you have to read.
So let me append roasting over here so that to get rid of all your errors so it fit exactly it.
And we need a frame object.
So I'm just going to store it in, let's say over underscore while so it's all up to you, whatever
name you want to assign.
And if I'm going to call this head over there to get how exactly my data frame looks like.
So it just executed.
And this is exactly your new little frame on which you have to do certain kind of analysis.
And if you are going to like lexical shape over there to check the number of rows and call it, have
to have almost three hundred fifty rows in your data.
So here you will see over here, this is exactly your assignment very forcefully statement.
You have to analyze which base number, which based on what exactly this one has most active cases.
You have to analyze this for this what I am going to do here.
So let me show you how many unique piece number we have so that it shows I have to exit this over and
a square file.
So in this I have to exit the column, which is exactly this one number.
And on this, if I'm going to call this unique and if I'm going to execute it now, here you will see
you have that look this this you need piece number available in your data.
So now what you need, your problem statement is nothing.
But you have to analyze which base number has most active basis.
It means you just need a distribution of activities with respect to each and every base number.
So far days, what we guys can do, we guys can simply use our box plot or because here you have a multiple
with no and whenever you have multiple was always try to go ahead with box part.
That's a trick.
So here I would say Asness dot box, but you can definitely use your block box score as well.
It's already OK.
So in this X-axis, I have to just copy this, despatching this number from here and just paste over
here and on this Y-axis, I definitely need my activators, which is exactly this feature.
So here I am going to say I have to say here and after it I have to say what exactly is I'm going to
say data is equal to nothing but over underscore file, which is exactly this one.
So if I'm going to execute this cell, you will see with respect to each and every piece number, you
have some kind of distributions over here.
And if you want to customize it, you can zoom, zoom or whatever you want.
It's all up to you.
So from this, you can definitely come up with conclusion.
Yeah.
This base number, which is exactly the zero two seven six four, has a maximum number of activists.
So that's the type of interest that you can fetch from your data.
So let's go ahead with our next policy statement in which we have to analyze which base number has maximum
troops to four days.
Again, you need your box plot.
So here I'm just going to say you here, you have to do some modification.
That's it.
So on y axis, you just need your grips here.
I'm going to say on y axis, I just need Kraatz.
So just executed.
And now you can definitely see over here.
Again, this these two, seven, six, four is still fine.
It means it has a maximum number of activities as well as it has a maximum number of trips as well.
So that's the type of conclusion you can get from this board, these vessels.
So let's go ahead with our next analysis.
Let's go ahead with the next statement in which you have to analyze how average trips per vehicle increases
or decreases with respect to date with each of the base number.
But here in this data frame, you will observe you don't have any future as trips per vehicle.
It means you have to create these features, like we create that feature very full.
I am going to say over an escort while and let's say my future name is nothing but trips per week.
So here first you have defined this feature and this feature is nothing but which is exactly your divisor
of your trips for activities.
So here I am going to say very first, you have to access your very first, which is exactly what trips
after you have to use this operator.
And I'm just going to use this one and here I have to access this activity.
So that's it.
And if, again, I'm going to call had over there.
So I'm just going to say you were on this call for I'll head over there now.
You can easily visualize with respect to each and have the best number.
You'll have that much number of trips per week.
Now, what you need over here, according to a problem statement here you have how your average cruise
per week increases or decreases with respect to date.
It means you have to create this date, this date as your index.
So what I'm going to do, I'm just going to say over underscore one dot set on the gold index and I
have to set my date as index.
And if I'm going to execute, it all just gets executed over here.
So once you have this is just what you have to do.
Very first, you have to group your data, because now you have this data frame.
Now you have to group your data on the basis of your base number.
So here I'm going to say dot group by and I have to basically group it on the basis of this despatching
based number.
Once I have this group, I have to access this.
I have to access this vehicle, so I have to access this vehicle.
And on this I have to simply call this plot that sit and let me customize my own window side or phagocytes
here.
I'm going to say VLT, Dot Fagre.
And here I have a parameter which is exactly Fixit and it triggers.
I just like to say to all six and if you want to assign some X levels unvaluable, you can assign it
as well.
So let's say my Y label is nothing but X in my right label is that the average crib's for Weyco and
after assigning available, I have to assign my title.
So here I'm going to say BLT dot type does.
So my title is nothing but let's say on demand versus supply chart.
So demand was a supply chart, longsuffering, all this stuff just executed.
And now you will see what this is with respect to each of your base number.
So let me add on Leegin over here.
So here I'm going to say BLT dot e again and again.
If I'm going to execute it now, you will see with respect to each other based number, you have your
own plot for each of the base number.
So that's the type of analysis how you can perform on your data and you can definitely see or hear this
orange plot, which is exactly this one, and this purple, which is exactly this be zero two seven
six four.
They definitely are.
Turns your attention and you can definitely conclude as yeah.
With respect to these d did you have that much demand and supply?
It means both these base number, which is the zero two seven six four, and this orange which is exactly
zero two five nine eight, they definitely performed better.
Whereas in case of blue chart, this blue plot, which is with respect to the zero two, five and two,
it doesn't have that much good attention comparing to all other space number.
So that's the type of inference your conclusion you have patch on the data.
So that's all about this project.
Hope you love this project very much.
Thank you.
Have a nice day and try to explore this data as much as you can because it's still you have managed.
Does a available over here to try to create his data and try to explore as much as you can, and that's
it said just focus on your resume and secure your job as whatever rules you want.
So I hope you will love this session and this project very much.
Thank you.
How nice to keep learning.
Keep growing, keep motivating.