Skip to main content

Interactive plots and applications in the browser from Python

Project description

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

NumFocus Logo

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bokeh-1.0.2.tar.gz (16.2 MB view details)

Uploaded Source

File details

Details for the file bokeh-1.0.2.tar.gz.

File metadata

  • Download URL: bokeh-1.0.2.tar.gz
  • Upload date:
  • Size: 16.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.20.1 setuptools/40.6.2 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.7.0

File hashes

Hashes for bokeh-1.0.2.tar.gz
Algorithm Hash digest
SHA256 3c030598d55115c714af4f843403370b9138a15834835a48bd1fcfd905fd2c1f
MD5 a2071bd0e759ce37c409637a20cd06ef
BLAKE2b-256 544fe6554176080d5cb809a20f36b8723ded05872c60f72e791efd6f2a9346bd

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page