On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.5.2!
Bokeh is a Python library for visualizing large and realtime datasets on the web.
Its goal is to provide to developers (and domain experts) with capabilities to easily create novel and powerful visualizations that extract insight from local or remote (possibly large) data sets, and to easily publish those visualization to the web for others to explore and interact with.
This release includes many bug fixes and improvements over our last recent 0.5.1 release:
New Layout system using kiwi.js constraint solver
Improved automated testing infrastructure
Abstract Rendering testing, server-side downsample fixes and ISO Contours
New “dashboard-like” example: https://github.com/ContinuumIO/bokeh/blob/master/examples/app/applet/stock_example.py
See the CHANGELOG for full details.
In upcoming releases, you should expect to see new layout capabilities (multiple axes, colorbar axes, better grid plots and improved annotations), new tools, more widgets and more charts, as well as an Object Query API, R language bindings, Blaze integration and cloud hosting for Bokeh apps.
Don’t forget to 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!