Bokeh integrates well with a wide variety of other libraries, allowing
you to use the most appropriate tool for each task.
To display visualizations in a browser, Bokeh converts Python data and
BokehJS. You can also use BokehJS as a standalone library or in
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.
The Panel library provides a high-level reactive interface that makes
it easy to build data-intensive dashboards and web applications on top of
Bokeh. Panel enabless full interoperability between Jupyter Notebooks
and Bokeh server. This lets you develop or prototype applications in
a notebook and deploy them on a server. Panel also interfaces with other
plotting libraries and lets you incorporate multiple data-science
artifacts into a single Bokeh application. Furthermore, the library
appearance your Bokeh apps.
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
Adding overlaid plots, slider widgets, selector widgets, selection
tools, and tabs is similarly straightforward.
process running in your browser to provide responsive, locally
interactive plots. However, browsers can only handle limited
amounts of data, making it infeasible to plot millions or billions
techniques produce misleading plots for such large datasets,
because of overplotting and related issues.
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
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.