WEBVTT

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Hello, everyone, and welcome to this patented Odille here we will understand different plots.

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Let us begin, first of all, understand scatterplot import delay.

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But at the.

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Then we have to define these four variables YouTube views, Facebook views, Twitter views and number

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of dates execute.

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Great.

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To understand the different plots, we will use functional method, a BLT dart scatter.

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Did versus you two views add label?

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YouTube views.

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Then Ademar.

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Good.

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Execute.

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Great.

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So this is the scatterplot in scatterplot.

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All the data points are represented by a dot.

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So these are the all data points.

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No copy.

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This line based.

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Data versus Facebook views.

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Execute.

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The points in Orange are the Facebook views, Facebook data points now based again.

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Days versus Twitter viewed.

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Execute.

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Great.

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The green points are the two other data points.

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Let us add X label via label and title, a BLT dot X label.

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Dade's.

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Then vilely, Abul

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views.

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Then added the title.

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Social media viewed execute.

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Dade's views and social media views X label via label and title.

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To see these labels, YouTube views, Facebook views and Twitter views, v, how to add one more line

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PLDT Dot Legen.

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Execute.

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You can see legenda here, you two views, Facebook views and Twitter views.

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Blue, orange and green in scatterplot.

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So we can add other parameters.

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Also, go to matplotlib documentation and type here.

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Scatter.

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Matplotlib dirt bike plot dots get.

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So these are the details about scatter plot.

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These are all lot of the parameters.

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And at the end, these are the examples.

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Let us add one more barometer, BLT dot greed.

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Lord read.

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And Long-stay.

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Execute.

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Great.

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Now degreed lines are added here.

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For more details about this scatter plot, you can defer, Matt, alertly documentation here.

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You will get all the details.

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And these are the examples.

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Let us understand one more plot.

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Bottom blog.

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We use bar plots to represent numerical values in a dataset to show how different data points vary from

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each other.

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Let us understand this type PLDT Dot Bar.

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Dade's.

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Versus you two views, then add label.

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YouTube views.

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Execute.

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So this is debark plot with Dubah plot.

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We can compare the numerical values.

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Copy this line based.

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Data versus Facebook views here also.

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Execute.

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Great.

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Then add X label VI label and title scroll up.

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COPD, Trillanes.

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Based execute.

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Days versus views, social media views.

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To see the legend at this line.

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Great.

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You two views in blue and Facebook views in orange.

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To understand more about debar plots, go to matplotlib documentation.

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A bot and hit on ndr matplotlib dot by plot dot board.

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Here you will get all the details.

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These are deep barometers.

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These are the proper deeds.

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And at the end, examples.

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So this is all about debark plot.

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Let us understand one more blot.

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He's still Graham.

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He still, Graham, is a graphical representation of the distribution of numerical data.

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Let us understand this to understand these two Ground V how to define two variables, Boynes and Binz

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execute.

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To plot these two type PLDT Dot Heyst.

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Points versus beans.

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Execute.

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So this is this program here, you can see comparison, comparison of numerical values at X level.

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Binz.

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The inviolable.

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Frequency.

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Then the title.

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Beans versus frequency execute.

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Great beans versus frequency.

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And this is the title to remove this line.

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Take BLT Dot Shoo.

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Great.

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Now you can see only the graph.

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For more details about this program, go to my blog, Talibe Documentation and Typist.

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So like this one, matplotlib dirt by plot, not least here, you will get all the details.

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Barometer's.

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And examples.

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So this is all about these two grame.

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Let us understand.

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By Jared.

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To understand it by Jack defined two variables labeled underscore to one and viewed.

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Great.

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Then plot the pie chart BLT taught by views versus labels.

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Specify labels is equal to variable labels.

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Underscore one.

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Great.

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So this is the pie chart to disable all these details.

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B.

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B dot to.

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Great.

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Let us add more parameters defined, one more variable explored.

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Zero zero zero zero point two.

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Then go to matplotlib documentation.

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Bye.

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Scroll down.

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Open example Basic by Jahed.

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And copy this barometer.

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Auto PTC.

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Execute.

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Now you can see the percentage with this barometer, you can see the percentage.

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Then add one more parameter explored.

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It's equal to.

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Specify this variable explored, underscore the one.

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Execute.

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So here we have highlighted area linked in and to do that.

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We have defined a variable explored under score one zero for Facebook.

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Zero for Instagram.

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Then zero for YouTube.

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And zero point two for the LinkedIn.

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And V.L. specified explode is equal to this variable explored Vun Hayne's we have highlighted linked

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here we can add a shadow.

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Also type shadow is equal to two.

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You can see shadow is added.

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So this is all about the pie chart.

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For more details.

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You can go to matplotlib documentation.

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DFAT department does.

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Nodes and examples.

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So despite an ordeal on different blogs, NCEA do understand more about the matplotlib library, use

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this matplotlib documentation.

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Here you will get all the details, User's Guide, EFIC, you and so on.