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Interactive plots and applications in the browser from Python

Project description

Bokeh logotype

Bokeh is a fiscally sponsored project of NumFOCUS, a nonprofit dedicated to supporting the open-source scientific computing community. If you like Bokeh and would like to support our mission, please consider making a donation.

Latest Release Latest release version npm version Conda Conda downloads per month
License Bokeh license (BSD 3-clause) PyPI PyPI downloads per month
Sponsorship Powered by NumFOCUS Live Tutorial Live Bokeh tutorial notebooks on MyBinder
Build Status Current TravisCI build status Current Appveyor build status Support Community Support on discourse.bokeh.org
Static Analysis BetterCodeHub static analysis Twitter Follow BokehPlots on Twitter

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.

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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 Getting Started section of the Quickstart guide.

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 Developer Guide and say hello on the bokeh-dev chat channel.

Follow us

Follow us on Twitter @bokehplots

Sponsors

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

NumFocus Logo Anaconda Logo NVidia Logo Rapids Logo
Quansight Logo Rex Logo

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

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