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 |
|
Conda |
|
| License |
|
PyPI |
|
| Sponsorship |
|
Live Tutorial |
|
| Build Status |
|
Support |
|
| Static Analysis |
|
|
Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Bokeh can help anyone who would like to quickly and easily make interactive plots, dashboards, and data applications.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 full documentation site to view the User's Guide or launch the Bokeh tutorial to learn about Bokeh in live Jupyter Notebooks.
Community support is available on the Project Discourse.
If you would like to contribute to Bokeh, please review the Developer Guide and say hello on the bokeh-dev chat channel.
Follow us on Twitter @bokehplots
The Bokeh project is grateful for individual contributions as well as sponsorship by the organizations and companies below:
|
|
|
|
|
|
If your company uses Bokeh and is able to sponsor the project, please contact [email protected]