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-0.5.1)
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
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Boxplot in bokeh.charts
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Better messages when you forget to start the bokeh-server
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Fixed some packaging bugs
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Fixed NBviewer rendering
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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](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](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: [email protected]
If you have interest in helping to develop Bokeh, please get involved!
Damián.