bokeh/bokeh


Interactive Data Visualization in the browser, from Python

https://bokeh.org

License: BSD-3-Clause

Language: Python

Keywords: bokeh, data-visualisation, interactive-plots, javascript, plots, plotting, python, visualization


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 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 Front Page 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 and say hello on the bokeh-dev chat channel.

Follow us

Follow us on Twitter @bokehplots

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Project Statistics

Sourcerank 15
Repository Size 143 MB
Stars 11,531
Forks 2,894
Watchers 446
Open issues 434
Dependencies 372
Contributors 387
Tags 361
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Bryan Van de Ven Mateusz Paprocki Damian Avila Sarah Bird Fabio Pliger Luke Canavan Paddy Mullen Havoc Pennington Peter Wang Brendan Collins Maggie-M canavandl-test Joseph Cottam Almar Klein Nick Roth Kevin Colten Justace Clutter Travis Swicegood hhuuggoo Karan Dodia

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1.3.4 August 07, 2019
1.3.2 August 04, 2019
1.3.2dev3 August 04, 2019
1.3.2dev2 August 04, 2019
1.3.2dev1 August 04, 2019
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