jupyter_bokeh
A Jupyter extension for rendering Bokeh content.
Decision gist · record as of 2026-08-14
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).AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires JupyterLab 3.0 or newer for pip install; older versions need separate labextension installation.
- Bokeh >= 2.0.0 required.
- Low friction install via pip or conda.
License · maintenance · safety
permissive license (permissive) — BSD license (permissive). No restrictions on commercial or private use; you may modify and distribute freely provided you retain the copyright notice and disclaimer.
last release 2026-06-09 (66 days) · last repo commit 2026-08-10 · 261 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 185,301 downloads/mo, #10,013 on PyPI
Alternatives
Verify before relying
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)- 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.
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 on this page. Verify before relying on it.
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
jupyter-bokeh on PyPI
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.
Requires JupyterLab 3.0 or newer for pip install; older versions need separate labextension installation. Bokeh >= 2.0.0 required.
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)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 | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesbokehipywidgets |
| Maintenance | Actively maintained 66 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 185,301 / month, #10,013 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableFramework :: JupyterFramework :: Jupyter :: JupyterLabFramework :: Jupyter :: JupyterLab :: 4Framework :: Jupyter :: JupyterLab :: ExtensionsFramework :: Jupyter :: JupyterLab :: Extensions :: PrebuiltIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: jupyter_bokeh-4.1.0-py3-none-any.whl
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See also bokeh · ipympl · jupyter-dash · jupyterlab-widgets · pythreejs · holoviews · jupyter-black · bqplot · ipydagred3 · matplotlib-inline