Using Jupyter notebooks to develop and share interactive data displays
summary
Notebooks produce a working prototype that becomes a browser dashboard: charts come from matplotlib (familiar but barely interactive), Bokeh (powerful but awkward API), and Plotly (intuitive, good browser interactivity); UI is wired with ipywidgets so Python callbacks update plots on demand. The team builds a JupyterHub + VM deployment that serves notebook-based dashboards—one example exposes 327000 plots via dropdowns—works for small-to-medium audiences but requires a live kernel and doesn’t scale to thousands of anonymous viewers.
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chapters (12)
- 0:00Introduction
- 2:58Multidimensional data
- 3:26Requirements
- 4:25Interactive examples
- 9:03Comparison
- 12:21Security concerns
- 12:40Who do we share our results to
- 13:11Business KPIs and metrics
- 13:31Drupada dashboards
- 14:36Technology
- 15:28Drawbacks
- 16:47Demo
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