Skip to content
Interactive Web Plotting for Python
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github linter Jun 18, 2019
bokeh Bryanv/6096 file input (#9083) Jul 15, 2019
bokehjs 'Updating for version 1.3.0rc1' Jul 15, 2019
conda.recipe temp pin pytest < 5.0 (#9040) Jul 2, 2019
docker-tools Bryanv/20 deprecations (#8574) Feb 2, 2019
examples allow set from pandas types (#9053) Jul 11, 2019
scripts exclude landing-2.0 issues from changelog for now (#9082) Jul 14, 2019
sphinx Bryanv/6096 file input (#9083) Jul 15, 2019
tests Bryanv/6096 file input (#9083) Jul 15, 2019
.appveyor.yml refine appveyor.yml settings (#8254) Sep 18, 2018
.bettercodehub.yml Finalize rename bokehjs/src/{coffee->lib} (#7792) Apr 12, 2018
.dockerignore Developer docker tools (#6375) Jul 16, 2017
.gitattributes add versioneer for version better automatic version number support Oct 10, 2013
.gitignore properly handle callbacks added during other event callbacks (#8837) Apr 13, 2019
.travis.yml Start testing landing-2.0 branch May 29, 2019
CHANGELOG 'Updating for version 1.2.0' May 28, 2019 Update Mar 1, 2019
FUNDING.yml Create FUNDING.yml May 27, 2019
LICENSE.txt move BokehJS license details to bokehjs/LICENSE (#8516) Dec 20, 2018
MAINTAINERS Update MAINTAINERS list (#8751) Mar 16, 2019 Improve the layout subsystem (#8085) Feb 1, 2019 Update Jul 11, 2019 Modularize CSS (#8989) Jun 12, 2019
classifiers.txt Use `git ls-files` to collect files for code quality tests (#5751) Jan 19, 2017 Restore selenium tests (#8110) Aug 2, 2018
examples.yaml Improve performance of widget heavy layouts (#8689) Feb 27, 2019
requirements.txt Reference link updated (#8897) May 9, 2019
secrets.tar.enc Enable and pypi uploads. Jun 19, 2015
setup.cfg resolve various deprectations (#8631) Feb 7, 2019 Corrected spelling mistakes (#8846) Apr 18, 2019 Use `git ls-files` to collect files for code quality tests (#5751) Jan 19, 2017


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
Static Analysis 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


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.


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

NumFocus Logo

You can’t perform that action at this time.