Welcome to Bokeh in IPython 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.


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

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

Texas unemployment | Linked brushing | Lorenz | Candlestick | Annular wedge | Vector | Rectangular | Glyphs | Glucose | Correlation | Bollinger

texas lorenz image annular vector

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!


For questions, please join the bokeh mailing list: https://groups.google.com/a/continuum.io/forum/#!forum/bokeh

You can also ask questions on StackOverflow and use the #bokeh tag: http://stackoverflow.com/questions/tagged/bokeh

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]


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