bokeh  2.4.0
About: Bokeh is an interactive visualization library for modern web browsers (using Python).
  Fossies Dox: bokeh-2.4.0.tar.gz  ("unofficial" and yet experimental doxygen-generated source code documentation)  

bokeh Documentation

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README.md
Bokeh logotype

Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.

Latest Release
pypi version
npm version
Downloads
License Bokeh license (BSD 3-clause) People GitHub contributors
Sponsorship Powered by NumFOCUS Live Tutorial Live Bokeh tutorial notebooks on MyBinder
Build Status Static Analysis
Support Community Support on discourse.bokeh.org Twitter Follow Bokeh on Twitter

If you like Bokeh and would like to support our mission, please consider making a donation.

colormapped image plot thumbnail anscombe plot thumbnail stocks plot thumbnail lorenz attractor plot thumbnail candlestick plot thumbnail scatter plot thumbnail SPLOM plot thumbnail
iris dataset plot thumbnail histogram plot thumbnail periodic table plot thumbnail choropleth plot thumbnail burtin antibiotic data plot thumbnail streamline plot thumbnail RGBA image plot thumbnail
stacked bars plot thumbnail quiver plot thumbnail elements data plot thumbnail boxplot thumbnail categorical plot thumbnail unemployment data plot thumbnail Les Mis co-occurrence plot thumbnail

Installation

The easiest way to install Bokeh is using the Anaconda Python distribution and its included Conda package management system. To install Bokeh and its required dependencies, enter the following command at a Bash or Windows command prompt:

conda install bokeh

To install using pip, enter the following command at a Bash or Windows command prompt:

pip install bokeh

For more information, refer to the installation documentation.

Resources

Once Bokeh is installed, check out the first steps guides.

Visit the full documentation site to view the User's Guide or launch the Bokeh tutorial to learn about Bokeh in live Jupyter Notebooks.

Community support is available on the Project Discourse.

If you would like to contribute to Bokeh, please review the Contributor Guide and request an invitation to the Bokeh Dev Slack workspace.

Note: Everyone interacting in the Bokeh project's codebases, issue trackers and discussion forums is expected to follow the Code of Conduct.

Follow us

Follow us on Twitter @bokeh

Support

Fiscal Support

The Bokeh project is grateful for individual contributions sponsorship as well as support by the organizations and companies below:

NumFocus Logo CZI Logo Quansight Logo
Blackstone Logo TideLift Logo
Anaconda Logo NVidia Logo Rapids Logo

If your company uses Bokeh and is able to sponsor the project, please contact info@bokeh.org

Bokeh is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides Bokeh with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit numfocus.org for more information.

Donations to Bokeh are managed by NumFOCUS. For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax adviser about your particular tax situation.

In-kind Support

The Bokeh project is also grateful for the donation of services from the following companies:

Security

To report a security vulnerability, please use the Tidelift security contact. Tidelift will coordinate the fix and disclosure.