On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.5.1! (http://continuum.io/blog/bokeh-0. <http://continuum.io/blog/bokeh-0.5>5.1) Bokeh is a Python library for visualizing large and realtime datasets on the web. This release includes many bug fixes and improvements over our last recent 0.5 release: * Hover activated by default * Boxplot in bokeh.charts * Better messages when you forget to start the bokeh-server * Fixed some packaging bugs * Fixed NBviewer rendering * Fixed some Unicodeencodeerror See the CHANGELOG for full details. In upcoming releases, you should expect to see dynamic, data-driven layouts (including ggplot-style auto-faceting), as well as R language bindings, more statistical plot types in bokeh.charts, 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 new Bokeh IPython notebook nbviewer index (including all the tutorials) at: http://nbviewer.ipython.org/github/ContinuumIO/bokeh -notebooks/blob/master/index.ipynb If you are using Anaconda, 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-0.5.1.min.js <http://cdn.pydata.org/bokeh-0.5.min.js> http://cdn.pydata.org/bokeh-0.5.1.min.css <http://cdn.pydata.org/bokeh-0.5.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! Damián.