‫Well, you've come a long way.
‫Check out this we've created in the past few videos we've 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 Jupyter Notebook, you may put some communication
‫up here, like you might create a cell.
‫It's 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 a notebook?
‫Are you 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 Shane Learning Course sample project.
‫I'm going to save it there.
‫I'm going to go.
‫Heart disease analysis.
‫Plot.
‫Let's see what happens going to be a format as a PNG 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 in to say a meeting or say a project presentation, you needed 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 going to be stored in part of memory at the moment.
‫And so what you can do is call fig, dot, save fig on this and then we can go heart disease analysis
‫plot.
‫Saved with code dot PNG.
‫So this is going to 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 retyping 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 has been called.
‫So let's see what happens if we call this fig, dot, save 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 PNG.
‫Beautiful and that is the default style.
‫So if you export it with save fig, I believe PNG is a default style.
‫So yeah, save the current figure.
‫All right.
‫So I think a 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.
‫We go right back up.
‫Let's see.
‫Far out.
‫We've covered a lot here.
‫You've created 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 dataframe with an umpire Ray and then start creating some plots
‫of your own and 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.