Notebook

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.

Quickstart¶

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

Notebook Gallery¶

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

Tutorial¶

[Introduction](../master/tutorial/00 - intro.ipynb)
[Basic Plotting](../master/tutorial/01 - plotting.ipynb)
[High Level Charts](../master/tutorial/02 - charts.ipynb)
[Low Level Models](../master/tutorial/03 - models.ipynb)
[Styling and Visual Appearance](../master/tutorial/04 - styling.ipynb)
[Sharing and Embedding Bokeh Plots](../master/tutorial/05 - sharing.ipynb)
[Adding Interactions](../master/tutorial/06 - interactions.ipynb)

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: 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 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.