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Hello, before going at it in the last session of this project, let me have a quick overview of what

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we have done in our project and not so from importing the data that we have done lots of costing.

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Then we have packed some meaningful insight from this budget.

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Then we have some surplus than we have in France from it as well.

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Then after it, we have performed some kind of this point cost analysis.

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After it, we have performed this heat map.

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Then we have automate all this stuff after we have performed some kind of a speech analysis on our data.

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And this is exactly that, a spatial analysis after we have automated all these things.

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And in the very last session, what we have done, we have done what exactly that has ongoing in our

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New York City with respect today, with respect to our all these things.

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So in this session, what I have to do, I have to read this data, this one, and I have to perform

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analysis on this one, which is exactly Ortberg, Jan five.

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While so very first I have to read this to let me copy this, but we are exactly right.

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It is available over here.

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So here I'm going to say B'Day Dot read Six Feet.

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And here very first we have to mention that particular part just passed and this is exactly the data

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set that you have to read.

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So let me append roasting over here so that to get rid of all your errors so it fit exactly it.

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And we need a frame object.

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So I'm just going to store it in, let's say over underscore while so it's all up to you, whatever

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name you want to assign.

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And if I'm going to call this head over there to get how exactly my data frame looks like.

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So it just executed.

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And this is exactly your new little frame on which you have to do certain kind of analysis.

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And if you are going to like lexical shape over there to check the number of rows and call it, have

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to have almost three hundred fifty rows in your data.

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So here you will see over here, this is exactly your assignment very forcefully statement.

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You have to analyze which base number, which based on what exactly this one has most active cases.

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You have to analyze this for this what I am going to do here.

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So let me show you how many unique piece number we have so that it shows I have to exit this over and

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a square file.

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So in this I have to exit the column, which is exactly this one number.

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And on this, if I'm going to call this unique and if I'm going to execute it now, here you will see

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you have that look this this you need piece number available in your data.

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So now what you need, your problem statement is nothing.

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But you have to analyze which base number has most active basis.

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It means you just need a distribution of activities with respect to each and every base number.

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So far days, what we guys can do, we guys can simply use our box plot or because here you have a multiple

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with no and whenever you have multiple was always try to go ahead with box part.

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That's a trick.

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So here I would say Asness dot box, but you can definitely use your block box score as well.

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It's already OK.

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So in this X-axis, I have to just copy this, despatching this number from here and just paste over

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here and on this Y-axis, I definitely need my activators, which is exactly this feature.

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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

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say data is equal to nothing but over underscore file, which is exactly this one.

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So if I'm going to execute this cell, you will see with respect to each and every piece number, you

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have some kind of distributions over here.

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And if you want to customize it, you can zoom, zoom or whatever you want.

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It's all up to you.

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So from this, you can definitely come up with conclusion.

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Yeah.

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This base number, which is exactly the zero two seven six four, has a maximum number of activists.

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So that's the type of interest that you can fetch from your data.

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So let's go ahead with our next policy statement in which we have to analyze which base number has maximum

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troops to four days.

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Again, you need your box plot.

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So here I'm just going to say you here, you have to do some modification.

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That's it.

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So on y axis, you just need your grips here.

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I'm going to say on y axis, I just need Kraatz.

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So just executed.

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And now you can definitely see over here.

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Again, this these two, seven, six, four is still fine.

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It means it has a maximum number of activities as well as it has a maximum number of trips as well.

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So that's the type of conclusion you can get from this board, these vessels.

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So let's go ahead with our next analysis.

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Let's go ahead with the next statement in which you have to analyze how average trips per vehicle increases

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or decreases with respect to date with each of the base number.

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But here in this data frame, you will observe you don't have any future as trips per vehicle.

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It means you have to create these features, like we create that feature very full.

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I am going to say over an escort while and let's say my future name is nothing but trips per week.

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So here first you have defined this feature and this feature is nothing but which is exactly your divisor

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of your trips for activities.

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So here I am going to say very first, you have to access your very first, which is exactly what trips

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after you have to use this operator.

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And I'm just going to use this one and here I have to access this activity.

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So that's it.

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And if, again, I'm going to call had over there.

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So I'm just going to say you were on this call for I'll head over there now.

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You can easily visualize with respect to each and have the best number.

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You'll have that much number of trips per week.

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Now, what you need over here, according to a problem statement here you have how your average cruise

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per week increases or decreases with respect to date.

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It means you have to create this date, this date as your index.

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So what I'm going to do, I'm just going to say over underscore one dot set on the gold index and I

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have to set my date as index.

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And if I'm going to execute, it all just gets executed over here.

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So once you have this is just what you have to do.

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Very first, you have to group your data, because now you have this data frame.

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Now you have to group your data on the basis of your base number.

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So here I'm going to say dot group by and I have to basically group it on the basis of this despatching

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based number.

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Once I have this group, I have to access this.

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I have to access this vehicle, so I have to access this vehicle.

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And on this I have to simply call this plot that sit and let me customize my own window side or phagocytes

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here.

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I'm going to say VLT, Dot Fagre.

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And here I have a parameter which is exactly Fixit and it triggers.

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I just like to say to all six and if you want to assign some X levels unvaluable, you can assign it

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as well.

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So let's say my Y label is nothing but X in my right label is that the average crib's for Weyco and

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after assigning available, I have to assign my title.

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So here I'm going to say BLT dot type does.

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So my title is nothing but let's say on demand versus supply chart.

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So demand was a supply chart, longsuffering, all this stuff just executed.

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And now you will see what this is with respect to each of your base number.

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So let me add on Leegin over here.

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So here I'm going to say BLT dot e again and again.

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If I'm going to execute it now, you will see with respect to each other based number, you have your

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own plot for each of the base number.

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So that's the type of analysis how you can perform on your data and you can definitely see or hear this

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orange plot, which is exactly this one, and this purple, which is exactly this be zero two seven

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six four.

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They definitely are.

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Turns your attention and you can definitely conclude as yeah.

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With respect to these d did you have that much demand and supply?

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It means both these base number, which is the zero two seven six four, and this orange which is exactly

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zero two five nine eight, they definitely performed better.

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Whereas in case of blue chart, this blue plot, which is with respect to the zero two, five and two,

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it doesn't have that much good attention comparing to all other space number.

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So that's the type of inference your conclusion you have patch on the data.

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So that's all about this project.

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Hope you love this project very much.

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Thank you.

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Have a nice day and try to explore this data as much as you can because it's still you have managed.

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Does a available over here to try to create his data and try to explore as much as you can, and that's

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it said just focus on your resume and secure your job as whatever rules you want.

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So I hope you will love this session and this project very much.

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Thank you.

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How nice to keep learning.

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Keep growing, keep motivating.