I am happy to announce the release of Bokeh version 0.4.4!
Bokeh is a Python library for visualizing large and realtime datasets on the web. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity to thin clients. Bokeh includes its own Javascript library (BokehJS) that implements a reactive scenegraph representation of the plot, and renders efficiently to HTML5 Canvas. Bokeh works well with IPython Notebook, but can generate standalone graphics that embed into regular HTML. If you are a Matplotlib user, you can just use %bokeh magic to start interacting with your plots in the notebook immediately!
Check out the full documentation, interactive gallery, and tutorial at
If you are using Anaconda, you can install with conda:
conda install bokeh
Alternatively, you can install with pip:
pip install bokeh
We are still working on some bigger features but want to get new fixes and functionality out to users as soon as we can. Some notable features of this release are:
* Additional Matplotlib, ggplot, and Seaborn compatibility (styling, more examples)
* TravisCI testing integration at Travis CI - Test and Deploy Your Code with Confidence
* Tool enhancements, constrained pan/zoom, more hover glyphs
* Server remote data and downsampling examples
* Initial work for Bokeh "app" concept
Also, we've also made lots of little bug fixes and enhancements - see the CHANGELOG for full details.
BokehJS is also available by CDN for use in standalone javascript applications:
http://cdn.pydata.org/bokeh-0.4.4.js
http://cdn.pydata.org/bokeh-0.4.4.css
http://cdn.pydata.org/bokeh-0.4.4.min.js
http://cdn.pydata.org/bokeh-0.4.4.min.css
Some examples of BokehJS use can be found on the Bokeh JSFiddle page:
Settings - JSFiddle - Code Playground
The release of Bokeh 0.5 is planned for early May. Some notable features we plan to include are:
* Abstract Rendering for semantically meaningful downsampling of large datasets
* Better grid-based layout system, using Cassowary.js
* More MPL/Seaborn/ggplot.py compatibility and examples, using MPLExporter
* Additional tools, improved interactions, and better plot frame
* Touch support
Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: GitHub - bokeh/bokeh: Interactive Data Visualization in the browser, from Python
Questions can be directed to the Bokeh mailing list: [email protected]
If you have interest in helping to develop Bokeh, please get involved! Special thanks to recent contributors: Amy Troschinetz and Gerald Dalley
Bryan Van de Ven
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