ANN: Bokeh 0.7.1 released

Community Support
1

On behalf of the Bokeh team, I am very happy to announce the release of Bokeh version 0.7.1!

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 release was focused on stability, bug fixes, improved documentation and better examples.

  • Several bokeh.charts bug fixes and enhancements, such as configurable tools

  • Docs improvements, in particular, documenting json for bokeh.models

  • Mpl compatility improved, now returning the plot object

  • A lot of encoding fixes, including fixes in some of our sample data

  • Faster runs in TravisCI using the new docker-based containerized infrastructure

  • New and improved examples, such as the Interactive Image Processing with Numba and Bokeh notebook

You can also see the CHANGELOG for full details.

We would like to mention that we are working hard to provide new language binding for Bokeh. Anyone interested in developing new language bindings is encouraged to contact us to request any help to make this happen quickly and to host your project under the Bokeh organization umbrella.

Also, the release of Bokeh 0.8 should happen in early 2015. Some notable features we intend to work on are:

  • R language binding

  • Simplifying production and multi-user Bokeh server deployments

  • CLI interface

  • Colorbar axis and axis location inspectors

  • Better support for maps and projections

As usual, 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/bokeh/bokeh-notebooks/blob/master/index.ipynb

To install the latest release, if you are using Anaconda, you can install it with conda:

conda install bokeh

Alternatively, you can install it with pip:

pip install bokeh

BokehJS is also available by CDN for use in standalone Javascript applications:

http://cdn.pydata.org/bokeh-0.7.1.min.js

http://cdn.pydata.org/bokeh-0.7.1.min.css

Finally, BokehJS is also installable with the Node Package Manager.

Issues, enhancement requests, and pull requests can be made on the Bokeh Github page: https://github.com/bokeh/bokeh

Questions can be directed to the Bokeh mailing list: [email protected]

Thank you for your attention!

Damián