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:
- Quickstart guide to Bokeh
- 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.
- Making 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.
- Using bokeh Commands
- Quickly create and iterate on Bokeh applications with the
bokehcommand line tool.
- Running a Bokeh Server
- Deploy the Bokeh Server to build and publish sophisticated data applications.
- Embedding Plots and Apps
- Embed static or server-based Bokeh plots and widgets into HTML documents in a variety of ways.
- Speeding up with WebGL
- Improve performance for large datasets by using WebGL.
- Mapping Geo Data
- Working with geographical data - Google Maps, GeoJSON, Tile Rendering.
- Learning More
- See where to go next for more information and examples.
- Bokeh tutorials
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 well with NumPy, Pandas, Blaze, or almost any array or table-like data structure.