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Well you've come a long way.

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Check out this we've created in the past few videos we have worked towards creating our own customized

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plot.

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Now of course the final step in a map plot lib workflow is to share this with someone else.

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Now of course if you've been working through your Jupiter notebook you may put some communication up

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here like you might create a sounds like this plot shows some information about the heart disease data

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set something like that of course you might go a little bit more in-depth than that to communicate your

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work and then you could share your notebook.

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But what if you didn't want to share notebooks are doing it like a presentation where you can't really

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just run through all the code and show someone what you've done.

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So what you may need to do is just export your figure.

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Now probably the easiest way to do this and it's the way I've done it in the past is just to save the

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image as you write click on it.

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You could save it to downloads or I could save it in my Schein learning course sample project.

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I'm gonna save it.

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When I go.

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Heart disease analysis plot.

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Let's see what happens is gonna be a format as a pen image.

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We'll click save.

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So that's going to download it.

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But then if we go back to here we can see here we've got heart disease analysis plot beautiful.

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So that's something that you could put into a keynote presentation.

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It's exactly what I've done to do this but I've just added some of my own custom drawings on the side

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here.

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So if you had to go into say a meeting or say a project presentation you need to show what you'd been

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working on in a visual format because that's often how most people will understand things pretty quickly.

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You can take something like this with you.

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Another way to do it via code is with the Save fig method.

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So again when we first created this up here we've created a fig a.k.a. a figure.

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So this figure is gonna be stored in Python memory at the moment.

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And so what you can do is call fig not save fig on this.

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And then we can go to heart disease analysis plot saved with Code Pink.

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So this is gonna save it with code so potentially in the future if you were creating a lot of these

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style plots you may put this code here instead of re typing it out all the time you may put something

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like this into a python function and that way you could put this line of code at the end of it to export

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your figure after the code is run after the function is being called.

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So let's see what happens if we call this fig not say fig it's not really going to output anything but

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if we go back to here we've got heart disease analysis plot saved with code Doc Pinky beautiful.

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And that is the default style so if you export it with save fig I believe PMG is the default style so

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you save the current figure.

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All right.

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So I think congratulations is in order.

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You've made it to the end of the map plot lib section we've come a very long way right from the top.

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If we go right back up let's see far out.

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We've covered a lot here.

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You've curated from creating an empty plot to making a full blown subplot with customization from a

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data set that we've manipulated.

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So that's a big step.

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If you've been following along if everything seems a little confusing at the moment don't worry this

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kind of work takes a little bit of practice a little bit of going through getting used to a little bit

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of rewriting code a little bit of making mistakes and making errors.

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But don't forget the best way to learn is to play around and practice on your own as a start.

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Maybe you could make a data frame with an umpire Ray and then start creating some plots of your own.

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And just get as creative as you can right.

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Try a different style try different data.

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Really the sky's the limit.

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In the meantime take a little break if you need and I'll see you in the next section.