Python Data Visualization Transcripts
Lecture: Customizing Seaborn summary
0:00 Let's summarize the options you have for customizing your Seaborn plots, first If you want to customize all plots,
0:07 you can use the Seaborne theme API. This consists of set style and set theme and is easy to use and great for high level adjustments to the style of
0:17 your visualization. If you want to get into more detail for the axis level plots you can use the matplot lib axes level API.
0:25 In this example we would set up the figure in the axis and then using that ax1 variable, we can set the X label,
0:34 the Y label, pretty much any customization that we could do in matplot lib We can do, it is very powerful but it is only available for the
0:42 axis level plots for the figure level plots. There are facet grid methods that we can apply. We get a facet grid as a return object.
0:52 When we call one of these plots. In this example, the displot and then we can use set or set access
0:58 labels, set titles, save the figure or add our reference line using these facet grid methods. This is very simple and streamlined API.
1:07 That works well when you create multiple plots but it does have limited customization options outside of the ones that are predefined.
1:16 It's important to understand the distinction between these and as you start to use Seaborn, you can play around and find what works best for you.