With a wide array of widgets, plot tools, and UI events that can trigger real Python callbacks, the Bokeh server is the bridge that lets you connect these tools to rich, interactive visualizations in …

Bokeh is a Python library for creating interactive visualizations for modern web browsers. It helps you build beautiful graphics, ranging from simple plots to complex dashboards with streaming …

The first steps guides are for anybody who is new to Bokeh. The only prerequisites for using these guides are a basic understanding of Python and a working installation of Bokeh.

Bokeh is officially supported (and continuously tested) on CPython versions 3.10 and later. It’s possible that Bokeh does work on other versions of Python, but no guarantees or support are …

All of the examples below are located in the examples subdirectory of the Bokeh repository. Click on an image below to see its code and interact with a live plot.

You can use several methods to change Bokeh’s configuration: Directly in the Python code, in a YAML configuration file, or with environment variables, for example.

When you pass sequences like Python lists or NumPy arrays to a Bokeh renderer, Bokeh automatically creates a ColumnDataSource with this data for you. However, creating a …

Bokeh is a Python library for creating interactive visualizations for modern web browsers. It helps you build beautiful graphics, ranging from simple plots to complex dashboards with streaming …

This site hosts examples of applications built using Bokeh, a library for building data visualizations and applications in the browser from Python (and other languages), without writing JavaScript.

With just a few lines of Python code, Bokeh enables you to create interactive, JavaScript-powered visualizations displayable in a web browser. The basic idea of Bokeh is a two-step process: …