Welcome to Bokeh in the Jupyter Notebook!

Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.

Quickstart

Get started with a 5-min introduction to Bokeh.

Tutorial

Start with the Introduction and Setup notebook and jump to any of the specific topic sections from there.

More information

For the full documentation, see http://bokeh.pydata.org/en/latest

To see the Bokeh source code, visit the GitHub repository: https://github.com/bokeh/bokeh

Be sure to follow us on Twitter @BokehPlots as well as on Youtube!

Contact

For questions, please join the Bokeh mailing list or visit the Gitter chat room

You can also ask questions on StackOverflow and use the #bokeh tag.

For information about commercial development, custom visualization development or embedding Bokeh in your applications, please contact [email protected]

To donate funds to support the development of Bokeh, please contact [email protected]

Thanks

Bokeh is developed with financial support from Anaconda, Inc. as well as individual community contributions. Many thanks to all of the Bokeh Github contributors.