We are pleased to announce the release of version 0.4.2 of Bokeh, an interactive web plotting library for Python!
This release includes improved Matplotlib and Seaborn support, an extensive tutorial with exercises and solutions, a new %bokeh magic command for IPython notebook, and Windows support for the Bokeh server with two new storage backend options.
Get It Now!
If you are using Anaconda, you can install with conda:
conda install bokeh
Alternatively, you can install with pip:
pip install bokeh
Bokeh is an interactive web plotting library for large and realtime datasets, combining the novel graphics of d3 with the ease of use of Python. For more information, please visit the Bokeh homepage, and consult the Technical Vision and FAQ.
This point release has arrived soon after the 0.4.1 release last month; there were still more great enhancements we wanted to roll out while work proceeds towards 0.5 in late March:
- Improved Matplotlib compatibility layer, beyond what 0.4.1 offered: PolyCollections and initial Seaborn examples
- Windows support for the Bokeh server with new “shelve” and in-memory storage backends
- a new %bokeh magic for the notebook that allows for configuring modes like autoshow, autohold for every cell
- a brand new tutorial with worked exercises and live plots in the answers
http://cdn.pydata.org/bokeh-0.4.2.js http://cdn.pydata.org/bokeh-0.4.2.css http://cdn.pydata.org/bokeh-0.4.2.min.js http://cdn.pydata.org/bokeh-0.4.2.min.css
Examples of BokehJS use can be found on the Bokeh JSFiddle page: http://jsfiddle.net/user/bokeh/fiddles/.
Marching towards 0.5
The release of Bokeh 0.5 is planned for late March. Some notable features we plan to include are:
- More refinements to interactions and tools
- Vastly improved layout system for plots, tools, and widgets
- “Prettification” of plots and toolbar, and the bokeh server index page
- Even more Matplotlib and Seaborn support
- Exposing ServerDataSource and RemoteDataSource objects
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@example.com
We would love to have contributions from folks. There are many easy places to plug in:
- touch events and mobile experience overall
- styling/design around plots, frames, etc.
- graphical configuration tools
- building out better Matplotlib support
- more language bindings: Scala, Ruby, R, Matlab, C, C++, etc.
- more nice-looking examples for our gallery page
If you would like some help incorporating Bokeh into your Notebooks, apps, or dashboards, please send an email to firstname.lastname@example.org to inquire about Continuum’s training and consulting services - not just for Bokeh, but for anything in the full NumPy/SciPy/PyData stack.Tags: Python Bokeh Visualization comments powered by Disqus
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