Interactive Web Plotting for Python
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README.md

Bokeh

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 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 Gitter Chat on the Bokeh Gitter channel
Static Analyis BetterCodeHub static analysis Twitter Follow BokehPlots on Twitter

Bokeh is an interactive visualization library for Python that enables beautiful and meaningful visual presentation of data in modern web browsers. With Bokeh, you can quickly and easily create interactive plots, dashboards, and data applications.

Bokeh provides an elegant and concise way to construct versatile graphics while delivering high-performance interactivity for large or streamed datasets.

Interactive gallery

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.

Once Bokeh is installed, check out the Getting Started section of the Quickstart guide.

Documentation

Visit the Bokeh site for information and full documentation, or launch the Bokeh tutorial to learn about Bokeh in live Jupyter Notebooks.

Contribute to Bokeh

If you would like to contribute to Bokeh, please review the Developer Guide.

Follow us

Follow us on Twitter @bokehplots

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