Well you've come a long way.
Check out this we've created in the past few videos we have worked towards creating our own customized
plot.
Now of course the final step in a map plot lib workflow is to share this with someone else.
Now of course if you've been working through your Jupiter notebook you may put some communication up
here like you might create a sounds like this plot shows some information about the heart disease data
set something like that of course you might go a little bit more in-depth than that to communicate your
work and then you could share your notebook.
But what if you didn't want to share notebooks are doing it like a presentation where you can't really
just run through all the code and show someone what you've done.
So what you may need to do is just export your figure.
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
image as you write click on it.
You could save it to downloads or I could save it in my Schein learning course sample project.
I'm gonna save it.
When I go.
Heart disease analysis plot.
Let's see what happens is gonna be a format as a pen image.
We'll click save.
So that's going to download it.
But then if we go back to here we can see here we've got heart disease analysis plot beautiful.
So that's something that you could put into a keynote presentation.
It's exactly what I've done to do this but I've just added some of my own custom drawings on the side
here.
So if you had to go into say a meeting or say a project presentation you need to show what you'd been
working on in a visual format because that's often how most people will understand things pretty quickly.
You can take something like this with you.
Another way to do it via code is with the Save fig method.
So again when we first created this up here we've created a fig a.k.a. a figure.
So this figure is gonna be stored in Python memory at the moment.
And so what you can do is call fig not save fig on this.
And then we can go to heart disease analysis plot saved with Code Pink.
So this is gonna save it with code so potentially in the future if you were creating a lot of these
style plots you may put this code here instead of re typing it out all the time you may put something
like this into a python function and that way you could put this line of code at the end of it to export
your figure after the code is run after the function is being called.
So let's see what happens if we call this fig not say fig it's not really going to output anything but
if we go back to here we've got heart disease analysis plot saved with code Doc Pinky beautiful.
And that is the default style so if you export it with save fig I believe PMG is the default style so
you save the current figure.
All right.
So I think congratulations is in order.
You've made it to the end of the map plot lib section we've come a very long way right from the top.
If we go right back up let's see far out.
We've covered a lot here.
You've curated from creating an empty plot to making a full blown subplot with customization from a
data set that we've manipulated.
So that's a big step.
If you've been following along if everything seems a little confusing at the moment don't worry this
kind of work takes a little bit of practice a little bit of going through getting used to a little bit
of rewriting code a little bit of making mistakes and making errors.
But don't forget the best way to learn is to play around and practice on your own as a start.
Maybe you could make a data frame with an umpire Ray and then start creating some plots of your own.
And just get as creative as you can right.
Try a different style try different data.
Really the sky's the limit.
In the meantime take a little break if you need and I'll see you in the next section.