Data Visualization with Python II: Making Interactive Plots and Dashboards

A static figure answers the question you chose in advance, while an interactive one lets readers explore the data and ask their own: zooming into a region, filtering to a subgroup, or hovering to see underlying values. The second part of this series moves from static graphics to interactive visualization, introducing common Python libraries for the task: Plotly and Altair for building interactive charts, and Streamlit for assembling those charts into dashboards that others can use. We’ll also briefly survey where these tools reach their limits and what to use instead: Dash for production dashboards that need more control over layout and callbacks, and the HoloViews, Panel, and Datashader ecosystem for datasets too large to render point by point. Finally, we’ll touch on how to share the results, from self-contained HTML files that need no server to applications deployed on shared infrastructure. We will go through several interactive exercises so that attendees can gain direct experience in using these libraries and will be prepared to learn more on their own after the workshop. Part I is useful background but not required; basic familiarity with Python and Pandas will be useful.