---
id: jupyter-bokeh
version: "4.1.0"
license: Copyright (c) 2012 - 2020, Anaconda, Inc., and Bokeh Contributors All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# jupyter_bokeh — A Jupyter extension for rendering Bokeh content.

License: permissive · Maintenance: active · Downloads: 185.3K/mo

## What it is and what it does

jupyter_bokeh is a JupyterLab extension that integrates Bokeh, a Python visualization library, into Jupyter notebooks. It allows you to create and display interactive Bokeh plots directly in notebook cells, enabling exploration of data through hover tooltips, pan/zoom, and other interactive tools without leaving the notebook environment.

The extension depends on bokeh and ipywidgets, and works with JupyterLab 3.0 and newer (with pip install) or older versions via separate labextension setup. It is maintained as part of the Bokeh project and tracks JupyterLab releases to ensure compatibility. The package is stable, actively maintained, and supports Python 3.8 through 3.11.

Use it for:

- Display interactive Bokeh plots in Jupyter notebooks for exploratory data analysis with hover tooltips and zoom controls.
- Build notebook-based dashboards combining Bokeh visualizations with Jupyter widgets for data exploration.
- Share reproducible analysis notebooks that render Bokeh graphics without requiring separate server deployment.
- Integrate Bokeh plots into scientific or research notebooks alongside markdown documentation and code cells.

## Worth the install?

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

Renders Bokeh visualizations directly within Jupyter notebooks and JupyterLab, enabling interactive plots and dashboards in notebook cells.

Yes. Low install friction, active maintenance, permissive license, and no known vulnerabilities. Install if you use Bokeh for visualization and want interactive plots directly in JupyterLab. Ensure your JupyterLab version matches the compatibility table (e.g., JupyterLab 4.0.x pairs with jupyter_bokeh 4.0.0).

## Install

pip install jupyter-bokeh
uv add jupyter-bokeh
poetry add jupyter-bokeh

## Installing jupyter_bokeh

Before you install: Low friction install via pip or conda. Active maintenance with recent releases; last commit 2026-08-10. Supports current Python versions (3.8–3.11) and tracks JupyterLab minor releases for compatibility.

License in practice: BSD license (permissive). No restrictions on commercial or private use; you may modify and distribute freely provided you retain the copyright notice and disclaimer.

Quickstart:

pip install jupyter_bokeh

import bokeh.plotting as bp
from bokeh.models import HoverTool

p = bp.figure(title="Example")
p.circle([1, 2, 3], [4, 5, 6])
bp.show(p)

Requires JupyterLab 3.0 or newer for pip install; older versions need separate labextension installation. Bokeh >= 2.0.0 required.

Verify before relying:

- Whether the extension works with all Bokeh server features or only static plots in cells.
- Performance characteristics with large or complex Bokeh visualizations in notebooks.

## Package facts

- License: Copyright (c) 2012 - 2020, Anaconda, Inc., and Bokeh Contributors All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 185.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

jupyter bokeh visualization, bokeh plots in jupyter, interactive bokeh notebooks, jupyter lab bokeh extension, bokeh rendering jupyter, notebook interactive graphics, jupyter-extension, visualization, interactive-plots

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