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Hey everyone.

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So we are now done with the point thoughts and now we will move to distribution plots.

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So there's something a little important one and nearly all of the cases are analyzed by using these

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plots.

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So here we are also going to need another dataset that is dependent CSP because that how much variable

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parameters like these are all closed values in depth.

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We have different one.

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So he'll just change this nine months into tips and here tips also.

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Now if you print the head you will have this dataset in which we have total bill.

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That is the bill paid by them in any particular place they are.

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Then we have the tape.

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Maybe they provide to the waitress extra.

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Then we have the agenda here.

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They're smoking or not the date that is weekly the time and the size.

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And if you print this one completely you will get that these are also wedding like here we have Sunday

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then we have such a day here then Friday the lunch and dinner also bedding and smoking status also wedding.

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So don't just look at that one like in head.

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This is showing only this.

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So now let me move to the distribution plots so here I have all these plots and most of these are distribution

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plots.

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So plotting the distribution process we have two different methods that is first is just by directly

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plotting the plot.

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Second is by using these subplots.

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What I mean by that one like if you look at here and see this this one you will get a joint plot.

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And if you go for this one you will also get a joint plot.

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And here also a joint.

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So what that mean.

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But if you look at them that all they are different.

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So this is a plot joint plot that contained different plots.

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You can just involve the joint plot and change according to what you need.

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Secondly just basically use them.

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So first let me show you an example of using a directly that is I'm going to use a plot that is known

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as president plot.

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So for that I just need a seedy and then plot then pass the parameters like I have X is equal to first

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one total B and then I have the second one white and this is equal to the tip.

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You can also just directly pass them.

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But passing with these is a better way.

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I will show you why.

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So here I have tips.

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If you shift on that one you will directly get the plot.

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So here we have this one a total bill and the variations according to that one legged tip.

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How total is vetting.

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And you can have different parameters in this one also like if you add low s that will here be true

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you will get a line also this one showing the lower distribution.

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Now this is the plot in which you will directly get the output based on the parameters you are using.

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And if I talk about the joint plot that is just removed that one and then I settle in to joint you will

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get up Lord like this one like a get a here.

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If you press this shift then you will get kind and which is by default scatter.

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Next why you get the hairs are scattered plot.

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This one is just get a plot like this and this one here if you notice this is a scatter plot and this

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is also scatter plot scattered plots are just generally in which you have these dots scattered on MTV

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and every of the points that contain or blow plots inside it like the joint plot that I am going to

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tell you here is always by default somewhat that is given by the seaborne like here scatter and if I

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do something here like kind inside this this one kind and change this one thing to clearly you will

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get a KDE plot here we have dispatched giddy.

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This one is different from the K.T. line because that one is for only one parameter here we are using

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two barometers so we have a closed call if you go for the gallery you will notice this one is clearly

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blue these close cuffs.

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So these are generally defined to like five or six categories bar plots both explodes violent thoughts

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hex KDE scattered like that.

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So here we are done with the kiddie pool.

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And here is the one I prefer most.

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That is the hex block.

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This one here you will easily able to analyze the data like this one these darker parts.

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This is the area we have the more data these are the parts we do not have.

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And one more thing these things that are shown here about in blue these are the point plots of particular

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parameter like this one is the total bill parameter informal Baja.

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This one is the and if you also notice here we did not have any point on you in last and just this one

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on the last second so here we have light parts here.

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This one is maximum.

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And this one.

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And if you encounter them one here like the intersection point then this one is very dark.

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So this is the explode here.

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You can also make this one like this one just go to that one.

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Check the color like this is the color here and this color subscript that is hash tag for S.B. 3 9 1.

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This is the color for every color has a particular code with a hash tag followed by six and character.

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No it is a condition of both numbers and characters also.

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And you can get the color code just by Googling here.

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If I write this one and on that one I will get this scene like this one.

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So this is about this one that how they work.

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Also be focused while you are writing these things that I have like I am telling you in the rest abroad

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that why is this important.

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Like if you do only this thing here just a spacebar menu then you will get an error.

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And most of the time you do not get this one that way I am getting at here.

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It also shows in total bill my total bill is correct.

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I have this total bill the added haze could not interpret input totally because this base is also considered

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in strings here.

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So make sure you are properly using the parameters.

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Also with the joint plot we have this one that is in last.

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This regression plot if you just make this one are origin and run that one you will get a regression

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to this is a regression pudding which we have a line common line denoting and above and below some parameters.

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And I will tell you more about this one later in the course.

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So just don't focus on that one right now.

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So we are on the hex plot and here we have hex.

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So this is how you can work on distribution blocks and how you can analyze them.

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You can get any one of these just by checking that which one is the distribution one and can make them

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done here.

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So I hope you got the idea that how this works and understand this perfectly.

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Now in the next video we will learn a little more about this one and a similar two joint plot known

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as being blue.

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So if you are feeling excited then go and check the pier plot before checking the video.

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And that we will continue in the next video.

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So thanks for watching.

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See you later.