Visualising data with Python
summary
Live demos use the NEISS injury dataset to compare libraries: matplotlib for low-level control and nicer defaults, Seaborn for quick aggregated static plots that show basketball ranks surprisingly high among causes and stairs as the top home hazard, Bokeh for zoom/hover linked plots exposing incident narratives, and HoloViews for one-line interactive exploration that auto-creates widgets. Observations: men get more basketball injuries, women more stair injuries, youth injuries drop over Christmas, and Bokeh/HoloViews interactivity works smoothly in the notebook.
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chapters (11)
- 0:00Introduction
- 0:30Visualization ecosystem
- 2:15Library architecture
- 3:51Dataset overview
- 5:25Matplotlib and Seaborn
- 7:18Seaborn demos
- 12:42Bokeh basics
- 15:35Interactive Bokeh features
- 16:52HoloViews demos
- 22:59HoloViews features
- 25:05Summary and conclusions
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