This user guide is intended to walk you through many common tasks that
you might want to accomplish using Bokeh. The guide is arranged by
Get set up and running quickly.
Learn important foundational concepts about how Bokeh is organized.
Make different kinds of plots using the simple but flexible glyph
methods from the bokeh.plotting interface.
Provide data or subsets of data for plots and tables and share data
Combine multiple plots and widgets into specified layouts.
Handle categorical data with a variety of different techniques such
as bar charts, categorical heatmaps, visual dodging, and jitter.
Create network graph visualizations with configurable node and edge interactions.
Working with geographical data—Google Maps, GeoJSON, Tile Rendering.
Make interactive tools (like pan, zoom, select, and others) available
on your plots.
Customize every visual aspect of Bokeh plots—axes, grids, labels,
glyphs, and more.
Add informational annotations, such as labels, arrows, and legends to
Create more sophisticated interactions including widgets or linked
panning and selection.
Deploy the Bokeh Server to build and publish sophisticated data
Integrate with the Jupyter ecosystem.
Learn how to export Bokeh layouts as PNGs and SVGs.
Embed static or server-based Bokeh plots and widgets into HTML documents
in a variety of ways.
Use Bokeh’s capabilities from the command line with the bokeh
Add new capability to Bokeh with custom user extensions.
Improve performance for large datasets by using WebGL.
Use Bokeh together with libraries such as Datashader and HoloViews.
The examples in the user guide are written to be as minimal as possible, while
illustrating how to accomplish a single task within Bokeh. With a handful of
exceptions, no outside libraries, such as NumPy or Pandas, are required to run the
examples as written. However, Bokeh works well with NumPy, Pandas, or almost any
array or table-like data structure.