Hey everyone.
So we are now done with the point thoughts and now we will move to distribution plots.
So there's something a little important one and nearly all of the cases are analyzed by using these
plots.
So here we are also going to need another dataset that is dependent CSP because that how much variable
parameters like these are all closed values in depth.
We have different one.
So he'll just change this nine months into tips and here tips also.
Now if you print the head you will have this dataset in which we have total bill.
That is the bill paid by them in any particular place they are.
Then we have the tape.
Maybe they provide to the waitress extra.
Then we have the agenda here.
They're smoking or not the date that is weekly the time and the size.
And if you print this one completely you will get that these are also wedding like here we have Sunday
then we have such a day here then Friday the lunch and dinner also bedding and smoking status also wedding.
So don't just look at that one like in head.
This is showing only this.
So now let me move to the distribution plots so here I have all these plots and most of these are distribution
plots.
So plotting the distribution process we have two different methods that is first is just by directly
plotting the plot.
Second is by using these subplots.
What I mean by that one like if you look at here and see this this one you will get a joint plot.
And if you go for this one you will also get a joint plot.
And here also a joint.
So what that mean.
But if you look at them that all they are different.
So this is a plot joint plot that contained different plots.
You can just involve the joint plot and change according to what you need.
Secondly just basically use them.
So first let me show you an example of using a directly that is I'm going to use a plot that is known
as president plot.
So for that I just need a seedy and then plot then pass the parameters like I have X is equal to first
one total B and then I have the second one white and this is equal to the tip.
You can also just directly pass them.
But passing with these is a better way.
I will show you why.
So here I have tips.
If you shift on that one you will directly get the plot.
So here we have this one a total bill and the variations according to that one legged tip.
How total is vetting.
And you can have different parameters in this one also like if you add low s that will here be true
you will get a line also this one showing the lower distribution.
Now this is the plot in which you will directly get the output based on the parameters you are using.
And if I talk about the joint plot that is just removed that one and then I settle in to joint you will
get up Lord like this one like a get a here.
If you press this shift then you will get kind and which is by default scatter.
Next why you get the hairs are scattered plot.
This one is just get a plot like this and this one here if you notice this is a scatter plot and this
is also scatter plot scattered plots are just generally in which you have these dots scattered on MTV
and every of the points that contain or blow plots inside it like the joint plot that I am going to
tell you here is always by default somewhat that is given by the seaborne like here scatter and if I
do something here like kind inside this this one kind and change this one thing to clearly you will
get a KDE plot here we have dispatched giddy.
This one is different from the K.T. line because that one is for only one parameter here we are using
two barometers so we have a closed call if you go for the gallery you will notice this one is clearly
blue these close cuffs.
So these are generally defined to like five or six categories bar plots both explodes violent thoughts
hex KDE scattered like that.
So here we are done with the kiddie pool.
And here is the one I prefer most.
That is the hex block.
This one here you will easily able to analyze the data like this one these darker parts.
This is the area we have the more data these are the parts we do not have.
And one more thing these things that are shown here about in blue these are the point plots of particular
parameter like this one is the total bill parameter informal Baja.
This one is the and if you also notice here we did not have any point on you in last and just this one
on the last second so here we have light parts here.
This one is maximum.
And this one.
And if you encounter them one here like the intersection point then this one is very dark.
So this is the explode here.
You can also make this one like this one just go to that one.
Check the color like this is the color here and this color subscript that is hash tag for S.B. 3 9 1.
This is the color for every color has a particular code with a hash tag followed by six and character.
No it is a condition of both numbers and characters also.
And you can get the color code just by Googling here.
If I write this one and on that one I will get this scene like this one.
So this is about this one that how they work.
Also be focused while you are writing these things that I have like I am telling you in the rest abroad
that why is this important.
Like if you do only this thing here just a spacebar menu then you will get an error.
And most of the time you do not get this one that way I am getting at here.
It also shows in total bill my total bill is correct.
I have this total bill the added haze could not interpret input totally because this base is also considered
in strings here.
So make sure you are properly using the parameters.
Also with the joint plot we have this one that is in last.
This regression plot if you just make this one are origin and run that one you will get a regression
to this is a regression pudding which we have a line common line denoting and above and below some parameters.
And I will tell you more about this one later in the course.
So just don't focus on that one right now.
So we are on the hex plot and here we have hex.
So this is how you can work on distribution blocks and how you can analyze them.
You can get any one of these just by checking that which one is the distribution one and can make them
done here.
So I hope you got the idea that how this works and understand this perfectly.
Now in the next video we will learn a little more about this one and a similar two joint plot known
as being blue.
So if you are feeling excited then go and check the pier plot before checking the video.
And that we will continue in the next video.
So thanks for watching.
See you later.