Jupyter Bokeh

4.0.5 · active · verified Thu Apr 16

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.

Common errors

```
JavaScript output is disabled in JupyterLab
```
cause The JupyterLab output area is configured to not execute JavaScript, often for security reasons or by default for specific output types.

fix Right-click on the output cell in JupyterLab and select 'Enable Javascript/HTML Output' or check your JupyterLab settings for default output behavior.
```
Error loading script: http://localhost:8888/nbextensions/jupyter_bokeh/index.js
```
cause The `jupyter-bokeh` Jupyter Notebook extension was not properly enabled or is not accessible.

fix Run `jupyter nbextension enable --py --sys-prefix jupyter_bokeh` and restart your Jupyter Notebook server.
```
ModuleNotFoundError: No module named 'bokeh'
```
cause `bokeh` is not installed in the current Python environment.

fix Install Bokeh: `pip install bokeh`.
```
Bokeh: ERROR: Unable to display Bokeh plots in the Jupyter notebook. When showing a plot, make sure the corresponding notebook extension is loaded. Refer to the 'set up' section on the Bokeh user guide for more information.
```
cause `jupyter-bokeh` is installed but the corresponding Jupyter extension is not enabled, or the `bokeh` notebook extension is not properly configured.

fix Ensure `jupyter-bokeh` is installed and enabled: `pip install jupyter-bokeh` then `jupyter nbextension enable --py --sys-prefix jupyter_bokeh` (for Notebook) or `jupyter labextension install @jupyter-widgets/jupyterlab-manager` (for Lab). Restart Jupyter after enabling.

Warnings

gotcha Jupyter 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.
breaking Incompatible `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.
gotcha Plots 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.
gotcha Plots 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.

Install

pip install jupyter-bokeh Basic Installation
jupyter nbextension enable --py --sys-prefix jupyter_bokeh jupyter labextension install @jupyter-widgets/jupyterlab-manager Enable for Notebook/Lab (Optional but Recommended)

Imports

JupyterBokeh
```
from jupyter_bokeh.widget import JupyterBokeh
```
While Jupyter Bokeh largely works automatically, this import refers to the underlying widget. It's rarely directly used by end-users for plot rendering, but can be useful for debugging or advanced programmatic control.

Quickstart

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)

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