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So let's get started with map plot lib.

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We're going to start on the desktop and do what we always do and hit the terminal.

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Beautiful.

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I'll zoom in a little bit on here.

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Excellent.

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Now we're going to go through a little workflow like we always do.

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So we're going to see these for me and get into the sample project folder which contains our environment.

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We can always see our environment by using Condit and list so this will show me my environments.

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This is the one I'm after here.

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Sample project the folder we've been working through before going to seed a I'm on the desktop right

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now.

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We can see that there.

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So go into email course we can see that little folder up here but we're gonna use a command line and

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then see it again into sample project.

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Beautiful.

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And then we can run content activate.

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Now you could just copy and paste this and do that there.

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But remember we're in the habit of typing out code.

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That's a best habit you can develop that way you'll get used to writing things out for yourself and

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practicing see what it feels like on your fingers to write code.

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So if you conduct activate that should be all correct.

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Wonderful.

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And so we've had base change from this to this.

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We've seen all this before.

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I'm going to load up a tube but a notebook.

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Our favorite workspace

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we've got introduction to num pi pan is we've been through those we're gonna start a new one so now

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we're going to retitle this one to be introduction to Matt bot lib.

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Beautiful.

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Well I have a title introduction to Matt pot lib.

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Just so we know what's going on we're in the habit of communicating what we're doing.

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There we go.

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Beautiful.

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And so now we're in a Jupiter notebook.

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The first step for always using that plot lib is we might import naptime pandas as well so we import

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all the tools we need right at the top of the notebook.

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This is kind of a regular workflow.

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So when first one order was Matt.

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Plot lib in line.

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Now if you've never seen this before.

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This little percentage sign you might have in a previous video but this is a magic command.

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So this percentage sign is telling Jupiter hey we want all of our map plot lib plots our graphs and

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visualizations to appear within the notebook.

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So that'll make more sense once we run it.

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So let's import map plot lib dot pi plot.

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Now pi plot is a python plotting module of map plot lib and the convenient name is as penalty will also

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get pandas as PD and import num pi as MP and we'll hit shift enter.

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This might take a little while depending on what's going on.

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Beautiful.

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So now we should have all these tools accessible now.

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Simplest way to create a plot in map plot lib is with BLT dot plot.

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So if we go shift tab I have a look at what this says.

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Plot X vs. Y as lines and or markers.

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What happens if we just run this huh.

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Okay we'll get kind of that empty box that we saw before that was in the map plot lib workflow.

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So this is how create a plot.

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This is our figure that we've just created.

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So we go back to our group a notebook.

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This is our figure that we've just created.

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But for some reason the output it's giving us these empty brackets so to get rid of those you can put

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a semicolon at the end of your plot call.

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So let's do that same thing.

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We just don't have that nasty little output there.

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This is what map plot they have in line means is that this plot is in the notebook so we can see it

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visually right here.

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That's all that plot Libyan line means.

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If we didn't call this it would just output something like this with no plot.

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So that's what this line of code is doing does the ideal scenario to always run that when you want to

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run map properly it plots.

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And now we didn't want to use the semicolon.

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We wanted to be a bit more communicated.

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We could go plot and then P L T dot show that's gonna do the exact same thing.

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We got a nice and blank plot here.

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Now how about we add some data so they go BLT dot plot and then hit shift tab.

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Plot X vs. Y as lines and or markers and if we click the plus sign gives us a little example here of

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plot x y.

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So how about we pass it X as a list of some sort.

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So maybe we'll just keep it nice and simple one two three four and we'll put the semicolon at the end

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and see what happens.

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Here we go.

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We've got a line so this is on the x axis.

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We should have one two three four.

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Okay.

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Beautiful.

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So what can we try next.

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Well let's create some x and y data x 1 2 3 four wonderful Y equals.

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Let's mix it up a little bit.

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11 twenty to thirty three forty four.

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Wonderful.

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Now let's see what these look like plot on plot x y and we put a semicolon at the end.

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There we go.

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We notice that our axes have changed so now we've gone from 1 to 4 on the X and from 11 to 44 on the

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y.

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Okay let's start a make a bit more sense.

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This is looking good now this way of plotting and map plot lib is referred to as this stateless way

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of plotting.

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And if you've ever used Matlab it works like that but if you haven't used Matlab before not to worry

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Let's have a quick look at that plot lib documentation because in there it tells us a little bit about

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different types of plotting in map plot lib what do you want documentation to tall rails ply plot tutorials

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so this is the type of plotting that we're using at the moment not map plot layer.

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Pipeline is a collection of command style functions that make map plot layer work like matlab.

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So we just went through each pie pop function make some change to a figure creates a figure.

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So I've been calling these plots.

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They're also referred to as figures creates a plotting area in the figure plot some lines in a plotting

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area decorates the plot with labels etc. In my plot lived up high plot various states are preserved

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across function calls.

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Okay.

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Where does reading the documentation here.

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So this is the one we're paying attention to.

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Note the pipeline API is generally less flexible than the object orientated API.

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So remember how I said there's two ways of plotting in that plot lib.

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Well we've just looked at the pipeline API and we're going to look at the object orientated API in a

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second but this is a while we're gonna be focused on most the object orientated API because we want

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as much flexibility as we can get right.

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There's one more thing I want to show you.

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So if we go map plot lib lifecycle plot the lifecycle of a plot.

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There we go and you can get to this in the documentation as well.

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So this is just map plot there.

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Dot org tutorials lifecycle of a plot.

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There we go.

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This is the same thing in general trying to use the object orientated interface over the PI plot interface.

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Okay so the documentation is saying these two things in bold notes we might listen to it right because

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the people who write these documentation I really know what they're doing.

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So let's see this is the first method will go fig Eagles now figure short for figure the LTE dot figure.

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This creates a figure

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axe is short for axes.

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So remember this whole thing is a figure but these two lines here are axes so ax equals Fig.

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Don't add subplot.

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This adds some axes plot don't show where we go so this is just replicated exactly what we did we did

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like a kind of a long version of going P ulti felt for same sort of thing here right.

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So this is the first method and then you've got the second method

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so you want vague equals plot BLT.

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And when I say plot I'm referring to.

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P L T but because there's no I was just easier for me to say plot.

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So creates a figure of being communicated here.

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You don't need these comments ax Eagle's fig dot add axes.

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And then within here we're gonna add some sample data 1 1 1 1

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sorry is going to add axes so these add accesses to the figure.

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So there we go.

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You've got another figure everyone acts dot plot.

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Let's get out x and y on their and then BLT dots show

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add some data.

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There we go.

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We're just creating the same plot.

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And now finally we're going to go with the third method and this is the one that we're going to be using.

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So we've put in here recommended the reason why I'm showing you all these is because if you look up

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Mac plot lib online and you find some documentation or some Stack Overflow answers you might come across

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these styles of plotting.

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So we're going to use a third method nurses recommended of course.

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You can choose your own but this is what we're going to be going through throughout the rest of the

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map Gottlieb section.

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So this is big comma acts this time.

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So we've saved a line of code equals penalty dot subplots.

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Now let's see what this says subplots create a figure and a set of subplots.

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This utility wrapper makes it convenient to create common layouts of subplots including the enclosing

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figure object in a single call.

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Now what is this subplot you could think of a subplot as having more than one plot come out at the same

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time.

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That's all it really is.

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And then we're going to go ax dot plot x y

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and some data and we'll put a little semicolon at the end instead of calling plot don't show this time.

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There we go.

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Just re created the same graph three times three different methods but we're gonna be focused on the

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third method and you might notice that every time I call something like fig eagles or ax eagles or fig

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comma ax Eagles it resets the figure even though we're plotting the same data.

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Maybe if we went here and we go 50 100 200 250 it's gonna change it up so every time we call something

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like this it's going to reset the figure and plot a new set of data.

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So let's wrap it up there.

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But in short the main takeaway from this lecture is that we're going to be focused on this method.

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There's two different types of methods plotting in that plot lib the pipeline API but that's generally

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less flexible it is quicker to get plots up and running sometimes but the object orientated API is going

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to allow us to do a bit more fancy stuff in the future so that's what we'll focus on.

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I'll see you in the next video.