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: bo...@continuum.io
If you have interest in helping to develop Bokeh, please get involved!
Damián.
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