Python Data Visualization Transcripts
Chapter: Matplotlib
Lecture: Object Oriented API

Login or purchase this course to watch this video and the rest of the course contents.
0:00 I wanted to highlight a couple of changes I made to the notebook just to indicate
0:06 the difference between the two interfaces that we've been talking about. So I have updated the notebook out of the field,
0:12 say this is the state based interface that we talked about. And then down here is the object oriented interface,
0:19 which is what we recommend. And you can always do a kernel restart and run off, get us back to the same spot.
0:28 Now I'll show how to actually use the object oriented API. So now we have the same histogram that we did before, but instead of doing plot.hist,
0:46 we did ax.hist. And on the surface it looks like we didn't really accomplish a whole lot,
0:52 but by creating the figure and the axes we have a lot more control over it and it's a lot more consistent pythonic,
0:59 API and we'll walk through some more examples of that. The other thing I wanted to talk about remember we did matplotlib inline
1:08 appear so that the figures would automatically display. There is another approach I wanted to call out called matplotlib notebook.
1:20 This is going to give a more interactive example and I'm gonna walk through and show it so that you're aware of it.
1:26 I personally don't use it very often, but I think it is helpful to see.
1:29 So I've enabled this notebook interface and I'm gonna do a little more complex example where we will let's copy and paste that.
1:38 So we don't have to re type everything. So we'll create that histogram. But now we want to set the X Label, the Y Label and the title.
1:46 Then we'll show the figure. Let me code that for you.
2:03 So now what we've done is we've established that axis put the histogram based on the combined 08 column that we've been using set the X label and the Y
2:15 label and title on that axis. And then showing this interactive figure that you can move and adjust and different plot types
2:26 is maybe a little more useful than others. And then when you're done interacting with it, you can turn it off. Like I said,
2:35 I don't tend to actually use this format very often I'm going to convert back to using matplot lib in line.
2:46 I'm also going to comment this out because sometimes it gets a little confused when you make multiple changes in the same notebook,
2:56 we start and run it all again. So when I ran it all again, I got this warning here because I have disabled matplot lib notebook.
3:05 The figs show it doesn't like that so I can rerun it without that, it will automatically display and everything's okay.
3:13 So just wanted to kind of walk through that a little bit more.
3:17 So let's give another example of using the object oriented interface where we have a different approach to setting the X Label,
3:26 the Y label and the title. So it's going to start the same way. So we defined our figure to find the axis object,
3:35 put the histogram on that axis and then instead of setting the doing three separate lines, we can use ax.set and pass X label,
3:44 Y label and title as parameters to it.
4:06 And now we have the same histogram with x label, Y label, and title Set. But we have used a slightly different API.
4:13 To do this and one pointed out because you'll see examples of both and it's a little bit of personal preference.
4:19 But I do think using Set is a little more easy to understand and grasp as you're getting started with that matplot lib and just a reminder.
4:29 If you want to get rid of the extra text. We had that semi colon. Rerun it and we have our plot So I'm going to restart and run all again.

Talk Python's Mastodon Michael Kennedy's Mastodon