A little wrapper around uv to launch ephemeral Jupyter notebooks.

uvx juv
# Usage: juv [OPTIONS] COMMAND [ARGS]...
#
#   A wrapper around uv to launch ephemeral Jupyter notebooks.
#
# Options:
#   --help  Show this message and exit.
#
# Commands:
#   add      Add dependencies to the notebook.
#   info     Display juv and uv versions.
#   init     Initialize a new notebook.
#   run      Launch a notebook or script.
#   version  Display juv's version.

juv should feel familar for uv users. The goal is to extend its dependencies management to Jupyter notebooks.

# create a notebook
juv init notebook.ipynb
juv init --python=3.9 notebook.ipynb # specify a minimum Python version

# add dependencies to the notebook
juv add notebook.ipynb pandas numpy
juv add notebook.ipynb --requirements=requirements.txt

# launch the notebook
juv run notebook.ipynb
juv run --with=polars notebook.ipynb # additional dependencies for this session (not saved)
juv run --jupyter=notebook@6.4.0 notebook.ipynb # pick a specific Jupyter frontend

# JUV_JUPYTER env var to set preferred Jupyter frontend (default: lab)
export JUV_JUPYTER=nbclassic
juv run notebook.ipynb

If a script is provided to run, it will be converted to a notebook before launching the Jupyter session.

uvx juv run script.py
# Converted script to notebook `script.ipynb`
# Launching Jupyter session...

PEP 723 (inline script metadata) allows specifying dependencies as comments within Python scripts, enabling self-contained, reproducible execution. This feature could significantly improve reproducibility in the data science ecosystem, since many analyses are shared as standalone code (not packages). However, a lot of data science code lives in notebooks (.ipynb files), not Python scripts (.py files).

juv bridges this gap by:

• Extending PEP 723-style metadata support from uv to Jupyter notebooks
• Launching Jupyter sessions with the specified dependencies

It's a simple Python script that parses the notebook and starts a Jupyter session with the specified dependencies (piggybacking on uv's existing functionality).

juv is opinionated and might not suit your preferences. That's ok! uv is super extensible, and I recommend reading the wonderful documentation to learn about its primitives.

For example, you can achieve a similar workflow using the --with-requirements flag:

uvx --with-requirements=requirements.txt --from=jupyter-core --with=jupyterlab jupyter lab notebook.ipynb

While slightly more verbose and breaking self-containment, this approach totally works and saves you from installing another dependency.