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 topic:
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 between plots.
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 plots.
Create more sophisticated interactions including widgets or linked panning and selection.
Deploy the Bokeh Server to build and publish sophisticated data applications.
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 command.
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.
Create plots in JavaScript by using BokehJS directly.
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.