All righty then.
So in the past few section we've been looking at data in the form of lists of numbers and arrays and
data frames.
But in this section we're going to start looking into matte plot lib.
And so the first question may have and so fairly good question to have is what is map plot lib.
Well as you might have inferred by the name it's a plotting library more specifically a python plotting
library.
So it'll fit in with what we've been using before and what map plot lib does is it allows us to turn
our data into some pretty visualizations also known as plots or figures.
So let's get started to refer back to our machine learning modeling framework.
We go through Problem Definition Data Evaluation features modelling experiments.
Then if we go back to where our tools fit in We're going to be covering that plot lib which is a part
of the data analysis section but can also as you'll see with all of these tools can also be part of
the experiment section we'll get to that in a future video don't you worry.
But this is going to complete the trifecta of pandas num pi and map plot lib.
And once you start to see these three in action it really makes sense.
The synergy these three provide in doing data analysis and exploring different datasets so why map what
lib.
Well first of all it's built on an umpire res and Python.
As we've discussed before a lot of data and numerical information are built upon num player res including
panders data frames and it's the same with map plot lib and because all of this is written in Python.
We won't have to learn another coding language.
So now we can work with our data and create visualizations as you'll see shortly with the same coding
language.
It integrates directly with pandas through the map plot led pandas API.
You can create basic or advanced plots.
And again like the other libraries write map plot lib if you can imagine a visualization if you can
imagine a plot you can probably create it using map plot lib but we're going to be focused on the most
useful types of plots.
And it has a simple to use interface.
Of course once you get the foundations.
But that's what we'll be covering what are we going to cover.
Well it's a little bit of a workflow that happens within map plot lib.
You start with the data you create a plot also known as a figure which is kind of just like an empty
square really.
And then number three is you plot data accesses on figure.
So there are the two main data classes of map plot lib access and figure.
But we'll look into these shortly.
And so once you've plotted some data you'll have something that looks like this in X and a Y axis with
some information on here.
Then you can customize the plot and add in something like a little bit of a title here and add another
title here and a title here so in this case you've got the 0 2 mastery machine learning and over time
your email knowledge is increasing.
Well that sounds great.
That's kind of similar to what we're doing now.
And then finally you'll save or share the plot because as you could imagine looking at a big table of
data full of numbers they can get quite confusing.
Humans were visual creatures right.
We want to see things visually.
So that's where these plots come in handy so you can save or share them to visually communicate your
work rather than just having it in a table full of numbers so more specifically once we've gone through
the map plot lib workflow we're going to use that workflow throughout all of these little dot points
here.
We'll focus on importing that port lib and see the two different ways of plotting.
Yeah I know there's two different ways but when I look at them both and then we're going to pick the
one that is most useful the most adaptable.
I'm going to plot data directly from num pi arrays so that'll build upon the NUM pi section and then
we'll look at our map plot lib integrates directly with Panda's data frames through the map plot lib
pan is API and we'll have a look at customizing our plots so adding a different color.
Putting in some different titles naming our things what we want and then finally we'll finish up the
workflow by saving and sharing our plots so we can communicate our work with others.
Now where can you get help.
The first step is to always follow along with the code if you can remember our motto.
If in doubt run the code so we'll be going again through another Jupiter notebook.
It'll be available for you to go through at the end of the session or in their extra information section.
Always try it for yourself.
If in doubt run the code try another thing.
Keep going.
If you're still stuck.
Search for it.
You'll probably come up against Stack Overflow.
We've seen that in the past or the map plot lib documentation which again is very vast.
So if you have a specific problem you might try search for it first and then read through the documentation
if you don't really understand what's on Stack Overflow.
Try again.
And then finally ask a question.
Remember if in doubt run the code I have to keep reminding myself of this too you know all right.
Well if you're ready to plot give me a wife.
I'll start with the jokes now.
I'll see you in the next video.