I am very happy to announce the release of Bokeh version 0.5! (http://continuum.io/blog/bokeh-0.5)
Bokeh is a Python library for visualizing large and realtime datasets on the web.
This release includes many new features: weekly dev releases, a new plot frame, a click tool, "always on" hover tool, multiple axes, log axes, minor ticks, gears and gauges glyphs, and an NPM BokehJS package. Several usability enhancements have been made to the plotting.py interface to make it even easier to use. The Bokeh tutorial also now includes exercises in IPython notebook form. Of course, we've made many little bug fixes - see the CHANGELOG for full details.
The biggest news is all the long-term and architectural goals landing in Bokeh 0.5:
* Widgets! Build apps and dashboards with Bokeh
* Very high level bokeh.charts interface
* Initial Abstract Rendering support for big data visualizations
* Tighter Pandas integration
* Simpler, easier plot embedding options
Expect dynamic, data-driven layouts, including ggplot style auto-faceting in upcoming releases, as well as R language bindings, more statistical plot types in bokeh.charts, and cloud hosting for Bokeh apps.
Check out the full documentation, interactive gallery, and tutorial at
as well as the new Bokeh IPython notebook nbviewer index (including all the tutorials) at:
If you are using Anaconda, you can install with conda:
conda install bokeh
Alternatively, you can install with pip:
pip install bokeh
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]
If you have interest in helping to develop Bokeh, please get involved! Special thanks to recent contributors: Tabish Chasmawala, Samuel Colvin, Christina Doig, Tarun Gaba, Maggie Mari, Amy Troschinetz, Ben Zaitlen.
Bryan Van de Ven