jupyter_bokeh
A Jupyter extension for rendering Bokeh content within Jupyter. See also the separate ipywidgets_bokeh library for support for using Jupyter widgets/ipywidgets objects within Bokeh applications.
Install
For versions 3.0 and newer of JupyterLab, you have the option to install
jupyter_bokeh with either pip or conda:
pip install jupyter_bokeh
or
conda install -c conda-forge jupyter_bokeh
For versions of Jupyter Lab older than 3.0, you must install the labextension separately:
conda install -c conda-forge jupyter_bokeh
jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install @bokeh/jupyter_bokeh
To install a specific version:
jupyter labextension install @bokeh/jupyter_bokeh@x.y.x
Compatibility
The core Bokeh library is generally version independent of
JupyterLab and this jupyter_bokeh extension
for versions of bokeh>=2.0.0.
Our goal is that jupyter_bokeh minor releases (using the SemVer pattern) are
made to follow JupyterLab minor release bumps, while micro releases are for new jupyter_bokeh features
or bug fix releases. We've been previously inconsistent with having the extension release minor version bumps
track that of JupyterLab, so users seeking to find extension releases that are compatible with their JupyterLab
installation may refer to the below table.
Compatible JupyterLab and jupyter_bokeh versions
| JupyterLab | jupyter_bokeh |
|---|---|
| 0.34.x | 0.6.2 |
| 0.35.x | 0.6.3 |
| 1.0.x | 1.0.0 |
| 2.0.x | 2.0.0 |
| 3.0.x | 3.0.0 |
| 4.0.x | 4.0.0 |
Contributing
Development install
Note: You will need NodeJS to build the extension package.
The jlpm command is JupyterLab's pinned version of
yarn that is installed with JupyterLab. You may use
yarn or npm in lieu of jlpm below.
# Clone the repo to your local environment
# Change directory to the jupyter_bokeh directory
# Install package in development mode
pip install -e .
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Rebuild extension Typescript source after making changes
jlpm run build
You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.
# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm run watch
# Run JupyterLab in another terminal
jupyter lab
With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).
By default, the jlpm run build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:
jupyter lab build --minimize=False
Uninstall
pip uninstall jupyter_bokeh
Metadata
Release files for jupyter_bokeh 4.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| jupyter_bokeh-4.1.0.tar.gz | 759.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jupyter_bokeh-4.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.1 MB
Release files / jupyter_bokeh-4.1.0.tar.gz
| Download URL | jupyter_bokeh-4.1.0.tar.gz |
|---|---|
| Size | 759.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
Hatch/1.16.5 cpython/3.14.5 HTTPX/0.28.1
|
Release files / jupyter_bokeh-4.1.0-py3-none-any.whl
| Download URL | jupyter_bokeh-4.1.0-py3-none-any.whl |
|---|---|
| Size | 1.3 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
Hatch/1.16.5 cpython/3.14.5 HTTPX/0.28.1
|