I am pleased to announce the release of Bokeh version 0.4!
Check out the full documentation and interactive gallery at
If you are using Anaconda, you can install with conda:
conda install bokeh
Alternatively, you can install with pip:
pip install bokeh
Some of the new features in this release include:
* Preliminary work on Matplotlib support: convert MPL figures to Bokeh plots
* Free public beta of Bokeh plot hosting at http://bokehplots.com
* Tool improvements:
- "always on" pan tool and wheel zoom tool (with shift key)
- box zoom tool
- viewport reset tool
* Enhanced datetime axis, with better performance and nicer ticking
* Expanded testing, including TravisCI integrations and static image output using PhantomJS
* RGBA and color mapped image plots now available from Python
* Python 3 supported
* Vastly improved documentation for glyphs, with inline examples and JSFiddle integration
Also, we've fixed lots of little bugs - see the CHANGELOG for full details.
Bokeh will be having a free "Office Hours" later this week! Join us this Thursday at 2pm CST on EngineHere at https://www.enginehere.com/stream/437/bokeh-04-release/ for a live informational session about the latest release. We'll be covering all the newest features and updates through a combination of live lecture, Q&A, and pair programming. It's all free, just sign up to the EngineHere learning platform.
Some examples of BokehJS use can be found on the Bokeh JSFiddle page:
The release of Bokeh 0.5 is planned for late March. 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
* Selection tools, tooltips, etc.
Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/continuumio/bokeh
Questions can be directed to the Bokeh mailing list: [email protected]
Special thanks to recent contributors: Janek Klawe, Samantha Hughes, Rebecca Paz, and Benedikt Sauer.
Bryan Van de Ven