WEBVTT

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Hello, everyone, and welcome to this deal from this Teito deal with loan data visualization with matplotlib

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liablity.

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Let us begin.

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The Matplotlib Library is an amazing visualization library for two dimensional plotting.

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And it is built on numpty at Ade's for the data, which Chen.

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There are two methods in matplotlib library.

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First one is the functional method.

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And second one is the object oriented method.

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Let us understand.

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Functional method fast.

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First, we have to import the library, read import.

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No, B, as.

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And B, then import matplotlib

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dot by plot as PLDT.

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And at the end, Veoh, to import random integer class to do that day from Numpty Dot random.

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Import random integer that is around and be great.

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No, the cube.

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For the data visualization V, how to define X and Y variable, A X is equal to ENPI Dot Lin space.

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Here we have to specify three arguments, zero, 10, 20, zero is the lower bound.

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Pain is the upper bound and 20 is number of intervals, delay in space returns, evenly spaced numbers

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or a specific interval.

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And this is the specific interval.

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Zero to ten.

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Now execute.

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Take X.

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Great.

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So these are the 20 evenly spaced numbers from the interval zero to ten now defined by variable.

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VI is equal to.

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Random integer zero 50 20 in the cube.

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No gick buy variable.

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Great here, a little detail is the lower bound.

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50 is the upper bound.

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And we're selecting 20 random numbers from the interval zero.

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250.

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No, no, down here in X and Y variable.

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These two are the added ENVI variable.

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These are the random numbers.

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Let us start them define white is equal to ENPI dot method sort.

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And in parentheses, again, vi execute.

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No, take Devi.

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Great.

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Now, these are random numbers, add in ascending order the.

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Let us visualize the data.

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They built the dot plot.

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And embattled tiffy's ex Guama Vij.

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So this is the output excesses, why axes?

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And these are the data points here.

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We can add X label, VI label and title.

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Let us see how it don't enter a BLT dot x label.

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Specify X label as x axes.

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You can specify any string.

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Then PLDT dot vilely.

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But.

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Why exs?

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Then specify title BLT dot title.

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X versus Y Axes argued.

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Great.

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You can see X-axis VI, XII's X versus Y axes.

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This is X level.

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This is Viall label.

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And this is the title here.

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We can hide these details.

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To do that, a BLT dart shoe.

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It's cute.

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Again.

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Great.

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When we use dot show method, then it shows deep graph like these.

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And before that, it is only printing.

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In this graph, we can add more details.

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Let us see how.

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Go to any browser and search matplotlib documentation.

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Click on different link.

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So this is the matplotlib documentation.

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No search blocked.

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Scroll down.

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Click on these matplotlib dot by plot dot blog.

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Great.

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Here we are selecting matplotlib, not by plot dot plot, because we're plotting the graph using this

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method dot plot.

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You can see here this method dot plot.

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And that the details about this matter.

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Scroll down.

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These are the details about barometer's.

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We can specify X VI then all other parameters.

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And these are the proper deeds.

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Property and description, we will see some of these properties.

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First one is the color.

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No, scrawled on.

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These out of the character names for these Gallas.

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Specify Galut.

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I will specify M.

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This one, Magin Top.

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In the Gude.

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Great.

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Now, the data points are in this gala, Magin Dot.

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Similarly, we can specify other Golos.

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And these are the options.

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Now scroll up.

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We will specify this property lines.

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And these are the options, dash, double dash and all other options.

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These are the options.

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Lifestyle is equal to double dash executed.

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This is the output.

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You can try other options.

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Also, let us see one more parameter marker.

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Scrawled on.

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So do that of the characters.

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And this is the description, Fotomat, because.

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We will specify this marker.

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Star.

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Monica is equal to Star.

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A cute.

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So these are the markers we can increase marker sites.

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Also.

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This one marker, Sayed's.

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Market decides is equal to.

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We have to specify a number.

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Increase the number 10.

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Marker size is increased.

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Similarly, we can increase line weight.

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This one line vid.

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I will specify one point for you.

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Landward is increased.

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So there are all other properties you can take all of these for the property line.

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We can specify land with our LW.

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Like this, instead of line vid, we can type LW.

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Same result.

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So these are the basics about depleting.

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Let doesn't understand multiple plots on same canvas.

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For the multiple plotting B, how to use a metal pipe PLDT dart subplot here, we will specify three

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arguments, one to one number of rows.

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Number of columns.

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And the plot that we add offering plot something here.

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BLT Dot Plot X and VI specify color

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read execute.

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This is the output.

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I know it is a little bit confusing if you haven't understood anything.

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Then don't worry.

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Add one more subplot, BLT Dodd subplot.

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Number of photos is equal to one number of columns is equal to two.

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And the canvas that we are lifting is to previously be where they are fitting first canvas.

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Now, plourde BLT dot plot.

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X vye.

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And.

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And that is Magin Dopp.

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Great.

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Now, you can understand clearly one drew here, also one drew and two columns.

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These are the two columns.

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He had a very offering canvas one then Vieri offering canvas to similarly, you can specify a different

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number of rules, different number of columns, and you can offer canvas accordingly.

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So this is all about deep plotting, multiple plots on same canvas.

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To do that, we have to use this method subplot method.

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For more details, you can type here in matplotlib documentation.

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Subplot.

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Matplotlib don't buy plot, dots upload.

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Here to will get all the details.

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These are the parameters.

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Then property and description.

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And all other details.

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So this data visualization tutorial here in this tutorial VLT understood how to plot digraphs.

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To do that, we have to use this matter dot plot method.

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And after that, we help unless to multiple plotting on same canvas.

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And to do that, we have to use this method dots, a blood method.

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I will see you in the next data visualization ordeal.

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Then, happy learning.