A little wrapper around `uv` to launch ephemeral Jupyter notebooks.
Project description
juv
A little wrapper around uv to launch ephemeral Jupyter notebooks.
uvx juv
# A wrapper around uv to launch ephemeral Jupyter notebooks.
#
# Usage: juv [uvx flags] <COMMAND>[@version] [PATH]
#
# Commands:
# init: Initialize a new notebook
# add: Add dependencies to the notebook
# lab: Launch notebook/script in Jupyter Lab
# notebook: Launch notebook/script in Jupyter Notebook
# nbclassic: Launch notebook/script in Jupyter Notebook Classic
#
# Examples:
# juv init foo.ipynb
# juv add foo.ipynb numpy pandas
# juv lab foo.ipynb
# juv nbclassic script.py
# juv --python=3.8 notebook@6.4.0 foo.ipynb
Scripts will be converted to notebooks before launching the Jupyter session.
uvx juv lab script.py # creates script.ipynb
Any flags that are passed prior to the command (e.g., uvx juv --with=polars lab) will be forwarded to uvx as-is. This allows you to specify additional
dependencies, a different interpreter, etc.
what
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
uvto 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).
alternatives
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.
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