bokeh/bokeh

 bokeh / bokeh

bokeh / bokeh

 
 

Interactive Web Plotting for Python http://bokeh.pydata.org/en/latest/

 README

Bokeh

Latest Release | latest
release
---|---
License | Bokeh
license
Build Status | build
status
Static Analyis |
Conda | conda
downloads
PyPI |
Live Tutorial |
Gitter |
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 novel graphics in the style of D3.js, while also delivering high-performance interactivity over very large or streaming datasets.

Interactive gallery

image | anscombe | stocks | lorenz | candlestick | scatter | splom
---|---|---|---|---|---|---
iris | histogram | periodic | choropleth | burtin | streamline | image_rgba
stacked | quiver | elements | boxplot | categorical | unemployment | les_mis

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.