Python Data Visualization Transcripts
Chapter: Course Conclusion
0:00 The next library we talked about was Seaborn and like pandas, it's built on top of matplot lib but provides a very powerful interface.
0:10 The key thing to keep in mind with Seaborn is that there are two high level APIS. The figure level plots and the access level plots.
0:19 I recommend that you start with the figure level plots, The relplot, the displot and the catplot are really going to get
0:26 you far for doing quick exploratory analysis. And then if you need to go into more detail and build custom plots,
0:34 you can go to the axis level plots for more customization. So in summary, I really like Seaborn.
0:40 It's a great tool for sophisticated exploratory analysis. I recommend you spend some time mastering the
0:47 API. So that you can apply to your own data sets. And then when you need to customize it, use the themes and potentially drop down the mat plot
0:55 lib for customization. If you do need high level interactivity then you may need to consider some of the other tools that we've talked about,
1:04 such as Altair or plotly or potentially combining Seaborn or Stream lit for more custom interactivity.