---
id: bokeh
version: "3.9.2"
license: BSD-3-Clause
license_treatment: permissive
maintenance: active
---

# bokeh — Interactive plots and applications in the browser from Python

License: permissive · Maintenance: active · Downloads: 12.1M/mo

## What it is and what it does

Bokeh is a Python library for creating interactive, browser-based visualizations. It generates standalone HTML files or integrates with web frameworks to deliver plots and dashboards that respond to user interaction—panning, zooming, selection, and hover tooltips—without requiring JavaScript knowledge. The library handles both static plots and streaming data, making it suitable for exploratory analysis, real-time monitoring, and embedded applications.

The package depends on Jinja2 for templating, NumPy and contourpy for numerical operations, Pillow for image handling, PyYAML for configuration, Tornado for web serving, and xyzservices for map tile integration. It targets modern Python versions and is maintained as a production-stable project with active development and institutional support.

Use it for:

- Build interactive dashboards for real-time data monitoring and business intelligence applications.
- Create exploratory data analysis tools where users can interact with plots to zoom, pan, and select subsets.
- Embed interactive visualizations in web applications using Tornado or other Python web frameworks.
- Generate standalone HTML reports with interactive plots that can be shared without requiring a server.
- Visualize large datasets with linked plots and cross-filtering for multi-dimensional analysis.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Bokeh is an interactive visualization library that creates browser-based plots, dashboards, and data applications from Python code, with support for large and streaming datasets.

Yes. Bokeh is a mature, actively maintained library with no known vulnerabilities, low install friction, and broad applicability across data science, finance, healthcare, and research. Its permissive license and institutional backing make it a reliable choice for interactive visualization projects. Install it if you need browser-based interactivity.

## Install

pip install bokeh
uv add bokeh
poetry add bokeh

## Installing bokeh

Before you install: Installation is straightforward with low friction; the package is actively maintained with a recent release and strong community backing through NumFOCUS sponsorship.

License in practice: BSD-3-Clause is a permissive license that allows commercial and private use with minimal restrictions, making it suitable for most projects.

Quickstart:

pip install bokeh

from bokeh.plotting import figure, show
plot = figure(title="Example")
plot.line([1, 2], [3, 5])
show(plot)

Requires Python 3.10 or later.

Verify before relying:

- Whether narwhals is used as a dataframe abstraction layer or for a specific feature.
- Performance characteristics on very large datasets and typical latency expectations.
- Whether the package supports real-time streaming updates or requires data to be pre-loaded.

## Package facts

- License: BSD-3-Clause (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 12.1M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

interactive plots python, web-based data visualization, dashboard creation library, browser visualization python, streaming data plots, interactive charts web, python plotting library, interactive-visualization, web-dashboard, data-exploration

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