Package Management with `uv

Docs: https://docs.astral.sh/uv/

cd your-project
uv venv                    # Creates .venv in project directory
source .venv/bin/activate  # or: . .venv/bin/activate
uv pip install pandas numpy

Advantages over managing with conda: conda can introduce dependency duplication. uv uses aggressive caching to avoid re-downloading (and re-building) dependencies that have already been accessed in prior runs. Let’s say two projects are using the same ‘pandas’ version. Even if you have a separate environments for the projects, uv does not install the dependency twice (like it would do with conda environments).

uv commands:

Install uv (first time):

curl -LsSf https://astral.sh/uv/install.sh | sh

Setup project:

uv init && uv venv --python 3.12

Activate environment:

source .venv/bin/activate

Install project in editable mode:

uv pip install -e .

Add dependencies:

uv add pandas

Add dependencies (pip compatibility):

uv pip install pandas

Running scripts

uv run example.py

Using Jupyter from VS Code

Create a project.
uv init project

Move into the project directory.
cd project

Add ipykernel as a dev dependency.
uv add --dev ipykernel

Open the project in VS Code.
code .

Links

• Using uv with Jupyter