Bokeh

Interactive Data Visualization in the browser, from Python
Alternatives To Bokeh
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
D3104,718
2 months ago3iscJavaScript
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Echarts54,5376,5544,083a day ago112September 13, 20222,288apache-2.0TypeScript
Apache ECharts is a powerful, interactive charting and data visualization library for browser
Superset51,09622 hours ago3April 29, 20221,311apache-2.0TypeScript
Apache Superset is a Data Visualization and Data Exploration Platform
Metabase31,818
2 hours ago1June 08, 20222,779otherClojure
The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
Bokeh17,3862,9366172 hours ago136July 05, 2022701bsd-3-clausePython
Interactive Data Visualization in the browser, from Python
Visx16,8752026518 hours ago47September 09, 2020129mitTypeScript
🐯 visx | visualization components
Plotly.js15,5047181976 hours ago213August 10, 20221,413mitJavaScript
Open-source JavaScript charting library behind Plotly and Dash
Apexcharts.js12,3723772607 hours ago188August 22, 2022260mitJavaScript
📊 Interactive JavaScript Charts built on SVG
Deck.gl10,6942801313 hours ago553September 16, 2022203mitJavaScript
WebGL2 powered visualization framework
Seaborn10,4946,3933,0173 hours ago30June 27, 2022115bsd-3-clausePython
Statistical data visualization in Python
Alternatives To Bokeh
Select To Compare


Alternative Project Comparisons
Readme
Bokeh logo -- text is white in dark theme and black in light theme

Bokeh is aninteractive visualizationlibrary for modern web browsers. It provides elegant, conciseconstructionof 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 Latest package version Supported Python versions Bokeh license (BSD 3-clause)
Project Github contributors Link to NumFOCUS Link to documentation
Downloads PyPI downloads per month Conda downloads per month
Build Current Bokeh-CI github actions build status Current BokehJS-CI github actions build status Codecov coverage percentage
Community Community support on discourse.bokeh.org Bokeh-tagged questions on Stack Overflow Follow Bokeh on Twitter

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:

NumFocus Logo CZI Logo Blackstone Logo
TideLift Logo Anaconda Logo NVidia Logo Rapids 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 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:

Popular Visualization Projects
Popular Data Visualization Projects
Popular User Interface Categories
Related Searches

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
Javascript
Python
Jupyter Notebook
Visualization
Plot
Data Visualization
Plotting
Visualisation