Popularity
9.5
Declining
Activity
9.8
Growing
16,951
451
4,004

Description

Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.

Please visit the Bokeh web page for more information and full documentation.

To get started quickly, follow the Quickstart in the online documentation.

Be sure to follow us on Twitter @bokehplots, as well as on Vine, and Youtube!

Code Quality Rank: L4
Programming language: Python
License: BSD 3-clause "New" or "Revised" License
Latest version: v2.4.2

bokeh alternatives and similar packages

Based on the "Data Visualization" category.
Alternatively, view bokeh alternatives based on common mentions on social networks and blogs.

Do you think we are missing an alternative of bokeh or a related project?

Add another 'Data Visualization' Package

README


Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics and affords high-performance interactivity across large or streaming datasets. Bokeh can help anyone who wants to create interactive plots, dashboards, and data applications quickly and easily.

Package

Project

Downloads

Build

Community

Consider making a donation if you enjoy using Bokeh and want to support its development.

4x9 image grid of Bokeh plots

Installation

To install Bokeh and its required dependencies using pip, enter the following command at a Bash or Windows command prompt:

pip install bokeh

To install conda, enter the following command at a Bash or Windows command prompt:

conda install bokeh

Refer to the installation documentation for more details.

Resources

Once Bokeh is installed, check out the first steps guides.

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 Contributor Guide and request an invitation to the Bokeh Dev Slack workspace.

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

Follow us

Follow us on Twitter @bokeh

Support

Fiscal Support

The Bokeh project is grateful for individual contributions, as well as for monetary support from the organizations and companies listed below:

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 numfocus.org 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 Support

Non-monetary support can help with development, collaboration, infrastructure, security, and vulnerability management. The Bokeh project is grateful to the following companies for their donation of services:


*Note that all licence references and agreements mentioned in the bokeh README section above are relevant to that project's source code only.