Welcome to Bokeh in the Jupyter Notebook!

Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients.

Quickstart

Get started with a 5-min introduction to Bokeh here.

Notebook Gallery

Some examples of Bokeh's interactive plots in IPython Notebooks:

Texas unemployment | Linked brushing | Linked panning | Lorenz | Candlestick | Annular wedge | Rectangular | Glucose | Correlation | Bollinger | Color Scatter

Tutorial

Start with the Tutorial Introduction 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 and Vine!

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 sales@continuum.io

To donate funds to support the development of Bokeh, please contact info@pydata.org

Thanks

Bokeh is developed in part with funding from the DARPA XDATA program. Additionally, many thanks to all of the Bokeh Github contributors.