Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
For more information about the goals and direction of the project, please see the Technical Vision.
To get started quickly, follow the Quickstart.
To see examples of how you might use Bokeh with your own data, check out the Gallery.
Visit the source repository: https://github.com/bokeh/bokeh
Be sure to follow us on Twitter @bokehplots!
Photographers use the Japanese word “bokeh” to describe the blurring of the out-of-focus parts of an image. Its aesthetic quality can greatly enhance a photograph, and photographers artfully use focus to draw attention to subjects of interest. “Good bokeh” contributes visual interest to a photograph and places its subjects in context.
In this vein of focusing on high-impact subjects while always maintaining a relationship to the data background, the Bokeh project attempts to address fundamental challenges of large dataset visualization:
Some of the core ideas for the backend processing in bokeh-server are currently implemented as a standalone library, and are being developed under the term “Abstract Rendering”, which we will be presenting at VDA 2014. For more information, you can visit the Abstract Rendering github.
Bokeh is one of several open-source components of the broader technical vision of Continuum Analytics. By providing powerful data description and vector computing on remote and distributed data via Blaze and Numba, and providing interactive visualizations of them via Bokeh, we enable teams to collaboratively perform rich analysis, share them with others (potentially non-technical members of their team or business), and rapidly create analytical dashboards and monitoring interfaces.
One guiding principle for the development of Bokeh is to provide useful software for people, while incorporating novel ideas from the academic world of visualization research. Additionally, as a modular and open-source project, we hope that Bokeh will enable many other projects to build a rich suite of domain-specific applications that change existing, legacy paradigms of data processing workflow.
For questions, please join the bokeh mailing list: https://groups.google.com/a/continuum.io/forum/#!forum/bokeh
You can also ask questions on StackOverflow and use the #bokeh tag: http://stackoverflow.com/questions/tagged/bokeh.
Follow us on Twitter @bokehplots! When tweeting about how awesome Bokeh is, be sure to use the #bokeh tag!
For information about commercial development, custom visualization development or embedding Bokeh in your applications, please contact firstname.lastname@example.org.
To donate funds to support the development of Bokeh, please contact email@example.com.
Additionally, many thanks to all of the Bokeh Github contributors.
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