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All righty then.

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So in the past few section we've been looking at data in the form of lists of numbers and arrays and

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data frames.

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But in this section we're going to start looking into matte plot lib.

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And so the first question may have and so fairly good question to have is what is map plot lib.

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Well as you might have inferred by the name it's a plotting library more specifically a python plotting

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library.

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So it'll fit in with what we've been using before and what map plot lib does is it allows us to turn

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our data into some pretty visualizations also known as plots or figures.

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So let's get started to refer back to our machine learning modeling framework.

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We go through Problem Definition Data Evaluation features modelling experiments.

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Then if we go back to where our tools fit in We're going to be covering that plot lib which is a part

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of the data analysis section but can also as you'll see with all of these tools can also be part of

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the experiment section we'll get to that in a future video don't you worry.

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But this is going to complete the trifecta of pandas num pi and map plot lib.

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And once you start to see these three in action it really makes sense.

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The synergy these three provide in doing data analysis and exploring different datasets so why map what

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lib.

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Well first of all it's built on an umpire res and Python.

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As we've discussed before a lot of data and numerical information are built upon num player res including

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panders data frames and it's the same with map plot lib and because all of this is written in Python.

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We won't have to learn another coding language.

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So now we can work with our data and create visualizations as you'll see shortly with the same coding

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language.

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It integrates directly with pandas through the map plot led pandas API.

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You can create basic or advanced plots.

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And again like the other libraries write map plot lib if you can imagine a visualization if you can

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imagine a plot you can probably create it using map plot lib but we're going to be focused on the most

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useful types of plots.

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And it has a simple to use interface.

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Of course once you get the foundations.

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But that's what we'll be covering what are we going to cover.

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Well it's a little bit of a workflow that happens within map plot lib.

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You start with the data you create a plot also known as a figure which is kind of just like an empty

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square really.

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And then number three is you plot data accesses on figure.

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So there are the two main data classes of map plot lib access and figure.

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But we'll look into these shortly.

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And so once you've plotted some data you'll have something that looks like this in X and a Y axis with

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some information on here.

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Then you can customize the plot and add in something like a little bit of a title here and add another

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title here and a title here so in this case you've got the 0 2 mastery machine learning and over time

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your email knowledge is increasing.

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Well that sounds great.

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That's kind of similar to what we're doing now.

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And then finally you'll save or share the plot because as you could imagine looking at a big table of

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data full of numbers they can get quite confusing.

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Humans were visual creatures right.

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We want to see things visually.

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So that's where these plots come in handy so you can save or share them to visually communicate your

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work rather than just having it in a table full of numbers so more specifically once we've gone through

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the map plot lib workflow we're going to use that workflow throughout all of these little dot points

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here.

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We'll focus on importing that port lib and see the two different ways of plotting.

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Yeah I know there's two different ways but when I look at them both and then we're going to pick the

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one that is most useful the most adaptable.

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I'm going to plot data directly from num pi arrays so that'll build upon the NUM pi section and then

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we'll look at our map plot lib integrates directly with Panda's data frames through the map plot lib

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pan is API and we'll have a look at customizing our plots so adding a different color.

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Putting in some different titles naming our things what we want and then finally we'll finish up the

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workflow by saving and sharing our plots so we can communicate our work with others.

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Now where can you get help.

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The first step is to always follow along with the code if you can remember our motto.

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If in doubt run the code so we'll be going again through another Jupiter notebook.

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It'll be available for you to go through at the end of the session or in their extra information section.

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Always try it for yourself.

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If in doubt run the code try another thing.

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Keep going.

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If you're still stuck.

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Search for it.

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You'll probably come up against Stack Overflow.

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We've seen that in the past or the map plot lib documentation which again is very vast.

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So if you have a specific problem you might try search for it first and then read through the documentation

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if you don't really understand what's on Stack Overflow.

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Try again.

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And then finally ask a question.

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Remember if in doubt run the code I have to keep reminding myself of this too you know all right.

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Well if you're ready to plot give me a wife.

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I'll start with the jokes now.

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I'll see you in the next video.