User GuideΒΆ

This user guide is intended to guide you through many common tasks that you might want to accomplish using Bokeh. The guide is arranged by topic:

Getting Set Up
Install Bokeh and verify your installation is working correctly.
Defining Key Concepts
Define and explain important preliminary concepts.
Plotting with Basic Glyphs
Use the simple but flexible glyph methods from the bokeh.plotting interface to construct basic and custom plots.
Using High-level Charts
Use the high-level bokeh.charts interface to create common statistical charts quickly and easily.
Leveraging Other Libraries
Display a wide range of plots created using Matplotlib, Seaborn, pandas, or as Bokeh plots.
Styling Visual Attributes
Customize every visual aspect of Bokeh plots—axes, grids, labels, glyphs, and more.
Configuring Plot Tools
Make interactive tools (like pan, zoom, select, and others) available on your plots.
Laying Out Multiple Plots
Combine multiple plots and widgets into specified layouts.
Working in the Notebook
Creating and display interactive plots inside Jupyter/IPython notebooks.
Adding Interactions
Create more sophisticated interactions including widgets or linked panning and selection.
Deploying the Bokeh Server
Deploy the Bokeh Server to build and publish sophisticated data applications.
Embedding Bokeh Plots
Embed static or server-based Bokeh plots and widgets into HTML documents in a variety of ways.
Speeding up visualizations with WebGL
Improve performance for large datasets by using WebGL.
Learning More
See where to go next for more information and examples.

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, Pandas, or Blaze are required to run the examples as written. However, Bokeh works perfectly well with almost any array or table-like data structure.