User Guide
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 ggplot.py 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.
