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bokeh /bokeh

Interactive Data Visualization in the browser, from Python

13.7K Stars 3.5K Forks Last release: Not found BSD 3-Clause "New" or "Revised" License 18.6K Commits 64 Releases

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Bokeh logotype

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.

| Latest Release | Latest release versionnpm 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 github actions build status | Support | Community Support on | | Static Analysis | Language grade: PythonLanguage grade: JavaScript | Twitter | Follow Bokeh on Twitter |

If you like Bokeh and would like to support our mission, please consider making a donation.

| 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 |


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 request an invitation to the Bokeh Dev Slack workspace.

Note: Everyone interacting in the Bokeh project's codebases, issue trackers and discussion forums is expected to follow the Code of Conduct.

Follow us

Follow us on Twitter @bokeh


Fiscal Sponsors

The Bokeh project is grateful for individual contributions as well as sponsorship by the organizations and companies below:

| NumFocus Logo | Anaconda Logo | NVidia Logo | Rapids Logo |

| Quansight Logo | Rex Logo |

If your company uses Bokeh and is able to sponsor the project, please contact [email protected]

Bokeh is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides Bokeh with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit for more information.

Donations to Bokeh are managed by NumFOCUS. For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax adviser about your particular tax situation.

In-kind Sponsors

The Bokeh project is also grateful for the donation of services from the following companies:


To report a security vulnerability, please use the Tidelift security contact. Tidelift will coordinate the fix and disclosure.

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