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.

Content

Quickstart

Get started with a 5-min introduction to Bokeh here

Tutorial

The Tutorial is divided in three sections: Basic, Topical and Advanced exercises. Each section contains several IPython notebooks with different exercises.

The [Exercises] notebooks contain the proposed exercises with hidden solutions, which can be displayed by clicking on the Show solution button, while the [Published] notebooks render the exercises solutions with their interactive plots.

Get started with Bokeh by going through the tutorial, attempting the exercises and checking the provided solution.

lines scatter image style hover
stocks histogram boxplot unemployment olympics
Basic Exercises: Topical Exercises: Advanced Exercises:
Charts: Widgets:

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

texas lorenz image annular vector

More Information

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

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

Be sure to follow us on Twitter @bokehplots!

Contact

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.

Follow us on Twitter @bokehplots! When tweeting about how awesome Bokeh is, be sure to use the #bokeh tag!

For information about commercial development, custom visualization development or embedding Bokeh in your applications, please contact pwang@continuum.io.

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

Thanks

Bokeh is developed with funding from DARPA‘s XDATA program.

Additionally, many thanks to all of the Bokeh Github contributors.