On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.6.1! 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 point release includes several bug fixes and improvements over our most recent 0.6.0 release: * Toolbar enhancements * bokeh-server fixes * Improved documentation * Button widgets * Google map support in the Python side * Code cleanup in the JS side and examples * New examples See the CHANGELOG for full details. In upcoming releases, you should expect to see more new layout capabilities (colorbar axes, better grid plots and improved annotations), additional tools, even more widgets and more charts, 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 http://bokeh.pydata.org as well as the Bokeh IPython notebook nbviewer index (including all the tutorials) at: http://nbviewer.ipython.org/github/ContinuumIO/bokeh-notebooks/blob/master/i... If you are using Anaconda or miniconda, you can install with conda: conda install bokeh Alternatively, you can install with pip: pip install bokeh BokehJS is also available by CDN for use in standalone javascript applications: http://cdn.pydata.org/bokeh-0.6.1.min.js http://cdn.pydata.org/bokeh-0.6.1.min.css 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: bokeh@continuum.io If you have interest in helping to develop Bokeh, please get involved! Cheers, Damián Avila damian.avila@continuum.io