/bokeh

Interactive Web Plotting for Python

Primary LanguagePythonBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

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 want to support our mission, please consider making a donation to support our efforts.

Latest Release Latest release version
License Bokeh license (BSD 3-clause)
Sponsorship Powered by NumFOCUS
Build Status Current TravisCI build status
Static Analyis BetterCodeHub static analysis
Conda Conda downloads per month
PyPI PyPI downloads per month
Live Tutorial Live Bokeh tutorial notebooks on MyBinder
Gitter Chat on the Bokeh Gitter channel
Twitter Follow BokehPlots on Twitter

Bokeh, a Python interactive visualization library, 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 helps provide elegant, concise construction of versatile graphics in the, while also delivering high-performance interactivity over very large or streaming datasets.

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

We recommend using the Anaconda Python distribution and conda to install Bokeh. Enter this command at a Bash or Windows command prompt:

conda install bokeh

This installs Bokeh and all needed dependencies.

To begin using Bokeh or to install using pip, follow the Quickstart documentation.

Documentation

Visit the Bokeh web page for information and full documentation, or launch the Bokeh tutorial in live Jupyter Notebooks

Contribute to Bokeh

To contribute to Bokeh, please review the Developer Guide.

Follow us

Follow us on Twitter @bokehplots and on YouTube.

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