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.
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
Start with the Tutorial Introduction and jump to any of the specific topic sections from there.
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 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
Bokeh is developed in part with funding from the DARPA XDATA program. Additionally, many thanks to all of the Bokeh Github contributors.