Bokeh integrates well with a wide variety of other libraries, allowing you to use the most appropriate tool for each task.
hvPlot is a concise API that lets you plot in Bokeh with the pandas
.plot() function and a wide variety of data containers. This API is
particularly convenient for working with data interactively and lets
you quickly produce common types of plots.
Bokeh offers you a lot of versatility when it comes to developing complex data visualizations for the web. Even so, a higher-level API can make day-to-day visualization tasks easier and less verbose.
HoloViews is a concise declarative interface that helps you build Bokeh plots. It is a separately maintained package that focuses on interaction with Jupyter notebooks and enables quick prototyping of figures for data analysis. For instance, building an interactive figure with three linked Bokeh plots requires only one line of code in HoloViews.
Adding overlaid plots, slider widgets, selector widgets, selection tools, and tabs is similarly straightforward.
Datashader is a separate Python library that renders even the largest datasets as fixed-size raster images that faithfully represent the underlying data. Datashader gives you the tools to build interactive Bokeh plot images that dynamically re-render in Python when you zoom and pan. This approach lets you display interactive visualizations of arbitrarily large datasets in standard web browsers.
Furthermore, Datashader works well with hvPlot and HoloViews. This lets you switch between base and rendered versions of a plot, interleave Datashader and Bokeh plots, and more. Here is an example of interaction HoloViews.