Visualization libraries · NumFOCUS (Bokeh)

Bokeh

Python library for interactive, browser-rendered visualizations and lightweight data applications, without writing JavaScript.

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Bokeh is a Python visualization library that renders interactive charts in the browser — with panning, zooming, hover tooltips, and linked selections — without requiring the author to write any JavaScript. Charts are generated as standalone HTML/JavaScript output or served live through the Bokeh server, which also supports building small interactive data applications with widgets like sliders and dropdowns that trigger Python callbacks. This makes it a middle ground between static Matplotlib-style plotting and full app frameworks like Streamlit or Dash: closer to a charting library, but with enough interactivity for lightweight dashboards. Bokeh underpins the visualization layer of several other Python tools, including HoloViews and parts of the PyData visualization ecosystem.

At a glance

Vendor NumFOCUS (Bokeh)
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (BSD-3-Clause)
Best for Python developers who want interactive charts or small data apps without writing JavaScript.

Pricing

Free, open-source project fiscally sponsored by NumFOCUS with no pricing page.

Pricing has not been verified yet — see the vendor's site.

Features

  • Interactive browser-rendered charts
  • Pan, zoom, hover, and linked-selection tools
  • Bokeh server for live Python-backed apps
  • Widgets (sliders, dropdowns) with Python callbacks
  • Standalone HTML/JS export
  • Large-dataset rendering via WebGL

Integrations

Profile last reviewed September 21, 2026

Alternatives

Plotly Open-source charting libraries for Python, R, and JavaScript producing interactive, zoomable charts with built-in hover and export tools. Vega-Altair Declarative Python statistical visualization library that generates Vega-Lite specifications from a concise chart API. Matplotlib Python's foundational plotting library for static, publication-quality charts, from quick line plots to fully custom figures.

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Terms to know

Data visualization Representing data graphically so that patterns, trends and outliers are easier to see than in a table of numbers. Exploratory data analysis An open-ended first pass through a dataset to understand its structure and spot patterns before formal analysis.

Related guides

How to choose a visualization library Pick a visualization library by language, how much custom control you need, and whether commercial licensing is worth it — not by demo gallery polish.