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jupyter-bokeh

library4.1.0pypypi✓ verified 83d ago

Jupyter Bokeh is a Jupyter extension that enables Bokeh plots to render interactively within Jupyter notebooks and JupyterLab environments. It acts as a bridge, ensuring that Bokeh's rich interactive visualizations work seamlessly. The current version is 4.0.5, and it follows Bokeh's release cadence, typically updating to support new Bokeh features and Jupyter environments.

data

pip install jupyter-bokeh

github ↗package ↗

INSTALL

IMPORT

SIG · JUPYTER-BOKEH

jupyter-bokeh

datapythonv4.1.0

Install

10.8s avg

Import

—

Disk

237MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v4.1.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

installs and imports cleanly · install 0.0s · import 0.000s · 228MB

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 10.8s · import 0.000s · 222MB

237MB installed

● package 237MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

BokehModel

✓ from jupyter_bokeh import BokehModel

✗ from jupyter_bokeh.widget import JupyterBokeh

After installing `jupyter-bokeh`, Bokeh plots automatically render interactively in Jupyter environments. Simply create a Bokeh plot using `bokeh.plotting` and call `show()`.

from bokeh.plotting import figure, show # Create a new plot with a title and axis labels p = figure(title="My First Bokeh Plot", x_axis_label='x', y_axis_label='y') # Add a circle renderer with size, color, and alpha p.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=10, color="navy", alpha=0.5) # Show the results in the notebook show(p)

Debug

Known issues

gotchaJupyter extension not properly enabled after installation may prevent plots from rendering or interacting correctly.

fix

Ensure the Jupyter extension is enabled: `jupyter nbextension enable --py --sys-prefix jupyter_bokeh` for Notebook or `jupyter labextension install @jupyter-widgets/jupyterlab-manager` for JupyterLab. Restart your Jupyter server afterwards.

affects: All versions

breakingIncompatible `bokeh` or `ipywidgets` versions can lead to rendering issues or broken interactivity.

fix

Always check the `jupyter-bokeh` documentation/PyPI page for compatible `bokeh` and `ipywidgets` versions. Upgrade both `jupyter-bokeh` and its dependencies (`pip install --upgrade jupyter-bokeh bokeh ipywidgets`) to ensure compatibility.

affects: All versions

gotchaPlots might appear as static images or not at all if the JavaScript frontend assets fail to load or are blocked.

fix

Check your browser's developer console for JavaScript errors. Ensure no ad-blockers or browser extensions are interfering with content delivery network (CDN) requests. Try clearing your browser cache or using a different browser. Verify the Jupyter extension is enabled.

affects: All versions

gotchaPlots not updating or interacting correctly when using complex Bokeh layouts or callbacks in Jupyter.

fix

Ensure that your `Bokeh` callbacks are correctly set up, especially if they involve `CustomJS` or `ColumnDataSource`. For complex interactive applications within Jupyter, consider using `Panel` with Bokeh which offers more robust server-side callback mechanisms.

affects: All versions

Upgrade

Version history

4.1.0latest on PyPI · released Jun 9, 2026

Audit

Dependencies

bokehrequiredRequired for creating Bokeh plots that jupyter-bokeh renders.

ipywidgetsrequiredCrucial for interactive widgets and communication in Jupyter.

notebookrequiredRequired for classic Jupyter Notebook integration.

paneloptionalUsed for some advanced dashboarding features, though not strictly required for basic rendering.

Agent activity

16 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/bokeh/jupyter_bokeh ↗

packagehttps://pypi.org/project/jupyter-bokeh/ ↗