If you're using another visualization library e.g. Pyvis for networks, try saving your chart as a local HTML file and wrapping that in a dp.HTML block.
Altair is a declarative statistical visualization library for Python, based on Vega and Vega-Lite. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite visualization grammar. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code.
To get started using Altair to make your visualizations, begin with Altair's Documentation
Matplotlib is the original Python visualisation library, often supported and used with Jupyter Notebooks. Matplotlib plots are not interactive in Datapane Reports, but are saved as SVGs so can be viewed at high fidelity.
Higher-level matplotlib libraries such as Seaborn are also supported, and can be used in a similar way to the matplotlib example below,
Folium makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing rich vector/raster/HTML visualizations as markers on the map.
The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys.
If your folium map consumes live data which expires after a certain time, you can automate it to refresh the map on a cadence. See Automation.