Hello, everyone, and welcome to this patented Odille here we will understand different plots.
Let us begin, first of all, understand scatterplot import delay.
But at the.
Then we have to define these four variables YouTube views, Facebook views, Twitter views and number
of dates execute.
Great.
To understand the different plots, we will use functional method, a BLT dart scatter.
Did versus you two views add label?
YouTube views.
Then Ademar.
Good.
Execute.
Great.
So this is the scatterplot in scatterplot.
All the data points are represented by a dot.
So these are the all data points.
No copy.
This line based.
Data versus Facebook views.
Execute.
The points in Orange are the Facebook views, Facebook data points now based again.
Days versus Twitter viewed.
Execute.
Great.
The green points are the two other data points.
Let us add X label via label and title, a BLT dot X label.
Dade's.
Then vilely, Abul
views.
Then added the title.
Social media viewed execute.
Dade's views and social media views X label via label and title.
To see these labels, YouTube views, Facebook views and Twitter views, v, how to add one more line
PLDT Dot Legen.
Execute.
You can see legenda here, you two views, Facebook views and Twitter views.
Blue, orange and green in scatterplot.
So we can add other parameters.
Also, go to matplotlib documentation and type here.
Scatter.
Matplotlib dirt bike plot dots get.
So these are the details about scatter plot.
These are all lot of the parameters.
And at the end, these are the examples.
Let us add one more barometer, BLT dot greed.
Lord read.
And Long-stay.
Execute.
Great.
Now degreed lines are added here.
For more details about this scatter plot, you can defer, Matt, alertly documentation here.
You will get all the details.
And these are the examples.
Let us understand one more plot.
Bottom blog.
We use bar plots to represent numerical values in a dataset to show how different data points vary from
each other.
Let us understand this type PLDT Dot Bar.
Dade's.
Versus you two views, then add label.
YouTube views.
Execute.
So this is debark plot with Dubah plot.
We can compare the numerical values.
Copy this line based.
Data versus Facebook views here also.
Execute.
Great.
Then add X label VI label and title scroll up.
COPD, Trillanes.
Based execute.
Days versus views, social media views.
To see the legend at this line.
Great.
You two views in blue and Facebook views in orange.
To understand more about debar plots, go to matplotlib documentation.
A bot and hit on ndr matplotlib dot by plot dot board.
Here you will get all the details.
These are deep barometers.
These are the proper deeds.
And at the end, examples.
So this is all about debark plot.
Let us understand one more blot.
He's still Graham.
He still, Graham, is a graphical representation of the distribution of numerical data.
Let us understand this to understand these two Ground V how to define two variables, Boynes and Binz
execute.
To plot these two type PLDT Dot Heyst.
Points versus beans.
Execute.
So this is this program here, you can see comparison, comparison of numerical values at X level.
Binz.
The inviolable.
Frequency.
Then the title.
Beans versus frequency execute.
Great beans versus frequency.
And this is the title to remove this line.
Take BLT Dot Shoo.
Great.
Now you can see only the graph.
For more details about this program, go to my blog, Talibe Documentation and Typist.
So like this one, matplotlib dirt by plot, not least here, you will get all the details.
Barometer's.
And examples.
So this is all about these two grame.
Let us understand.
By Jared.
To understand it by Jack defined two variables labeled underscore to one and viewed.
Great.
Then plot the pie chart BLT taught by views versus labels.
Specify labels is equal to variable labels.
Underscore one.
Great.
So this is the pie chart to disable all these details.
B.
B dot to.
Great.
Let us add more parameters defined, one more variable explored.
Zero zero zero zero point two.
Then go to matplotlib documentation.
Bye.
Scroll down.
Open example Basic by Jahed.
And copy this barometer.
Auto PTC.
Execute.
Now you can see the percentage with this barometer, you can see the percentage.
Then add one more parameter explored.
It's equal to.
Specify this variable explored, underscore the one.
Execute.
So here we have highlighted area linked in and to do that.
We have defined a variable explored under score one zero for Facebook.
Zero for Instagram.
Then zero for YouTube.
And zero point two for the LinkedIn.
And V.L. specified explode is equal to this variable explored Vun Hayne's we have highlighted linked
here we can add a shadow.
Also type shadow is equal to two.
You can see shadow is added.
So this is all about the pie chart.
For more details.
You can go to matplotlib documentation.
DFAT department does.
Nodes and examples.
So despite an ordeal on different blogs, NCEA do understand more about the matplotlib library, use
this matplotlib documentation.
Here you will get all the details, User's Guide, EFIC, you and so on.