bokeh README

The project's README from github.com/bokeh/bokeh. As one markdown file: README.md.

Bokeh logo -- text is white in dark theme and black in light theme

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 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 NPM 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

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 using 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 checkout the Bokeh tutorial repository 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.

Support

Fiscal Support

The Bokeh project is grateful for individual contributions, as well as for present and past 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:

bokeh (bokeh/bokeh)

Searching Database…

20.5K ·GitHub
Bokeh
Что это такое?Bokeh — это библиотека для Python, которая позволяет строить интерактивные визуализации, работающие в современных браузерах: графики, дашборды и приложения для работы с данными с высокой производительностью даже на больших или потоковых наборах данных.
Чтобы пользоваться Bokeh, нужно писать на Python или на JavaScript?Вы пишете только на Python — Bokeh сам генерирует интерактивную графику, которая отображается в браузере. Браузерная часть (BokehJS, TypeScript-часть этого репозитория) уже сделана за вас.
Мне нужно быть веб-разработчиком, чтобы с этим работать?Нет — библиотека рассчитана на всех, кто хочет быстро и просто создавать интерактивные графики, дашборды и приложения для работы с данными, не выходя из Python.
Насколько Bokeh зрелый проект?Очень: разработка ведётся с 2012 года, у репозитория больше 20 000 звёзд на GitHub, а работу поддерживают CI-конвейеры, гайд для контрибьюторов и Slack для разработчиков.
What are similar projects?uPlot, perspective, matplotlib, altair, plotly.js, plotly.py, marimo, pyecharts
Which topics is it in?notebooks, plotting, dashboards, charting-library, jupyter, charts, visualization, data-visualization, data-science, real-time, streaming, browser
Creator
bokeh
Size
385.2 MB
Stars
20.5K
Updated
CommitOct 17
Age
14y
Commits · 90 Days