Setting up Python without the pain

uv is the best thing to happen to Python's ecosystem since sliced bread.

Disclaimer: Not a heavy Python user, definitely not at work, where 99% of my code is a delicious blend of Java, YAML and Dockerfiles.

For a new upcoming project at my day job, we settled, as a team, on using Python to build the thing.

It was a conscious decision based off the context in which this project was born: a mix of research and plain ol’ engineering, where we’ll need to collaborate cross-functionally with another team who has a lot of institutional knowledge piled up about this particular area. Since that team is fond of Python, we thought it would be the right decision to facilitate collaboration and break down silos in the future.

Obviously, this means that now we must set up and configure Python for this new project! And, as a not so far-fetched fact, doing that is a pain:

xkcd 1987 - the pain is real, the pain is all around you.

This actually compounds and is made much worse when you are working on a company-issued laptop that typically requires admin permissions to install anything in its “proper place”. On an ecosystem such as Python, where the binaries for issuing the “classic commands” are usually expected to be in standard locations to work out of the box, that is an issue that can prove impossible to solve.

The standard use case for virtual environments (via venv) also doesn’t really help since when creating a virtualenv, you need to have any potential Python version installed if you want to configure a venv to use it.

So, what’s the alternative? Consider this: what if you could abstract away all the complexity with a tool that “injects itself” in your regular development workflow but allows you much more flexibility in configuring local installations?

Enter Astral's uv!

uv solves the dependency and environment management headaches that have plagued Python developers for years. As a fast, user-friendly package installer and resolver written in Rust, uv brings modern tooling to the Python ecosystem without requiring admin privileges or system-wide installations.

What makes uv different?

Unlike traditional Python package managers, uv is designed with speed and developer experience at its core. Here's what makes it stand out:

1. Lightning fast performance: Being Rust-based, uv can install packages up to 10-100x faster than pip, dramatically reducing the time spent waiting for dependencies to resolve.

2. No admin privileges required: uv can install and manage Python packages in user space, making it perfect for corporate environments with strict IT policies.

3. Standalone operation: You don't need Python pre-installed to use uv. It can download and manage Python interpreters for you, allowing true isolation between projects.

4. Deterministic builds: uv generates lock files by default, ensuring consistent installations across all environments.

Getting started with uv

Installation is straightforward and doesn't require admin rights:

bash

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

Once installed, you can create isolated Python environments:

bash

# Create a new environment with Python 3.11
uv venv .venv --python=3.11

# Activate the environment
source .venv/bin/activate  # On Unix/macOS
.venv\Scripts\activate     # On Windows

Installing packages is blazing fast:

bash

# Install packages
uv pip install numpy pandas matplotlib

# Install from requirements file
uv pip install -r requirements.txt

How uv shines in cross-team projects

For cross-team projects or starting from scratch, uv is a game-changer. When starting from scratch, we can face several challenges:

1. Everyone had different Python setups (or none at all)

2. Corporate laptops restricted system installations

3. Different team members needed different package versions

With uv, we created a standardized workflow:

1. Each developer installed uv without needing IT approval

2. Our project config specified the exact Python version

3. A single lock file ensured everyone had identical dependencies

The result? Zero "it works on my machine" issues, dramatically reduced setup time for new team members, and seamless collaboration between engineering and research teams.

Beyond package management

What really sets uv apart is how it integrates with the broader Python ecosystem. It's not just a package installer—it's a comprehensive solution that includes:

• A faster alternative to pip (uv pip)

• Virtual environment management (uv venv)

• Dependency resolution and lock file generation

• Integration with popular tools like Poetry and Pipenv

For teams straddling multiple languages and environments, uv provides a consistent, modern workflow that feels familiar to developers coming from ecosystems with more mature tooling like Rust's Cargo or JavaScript's npm/yarn.

Conclusion

The Python packaging ecosystem has been notoriously challenging, especially in corporate environments with strict IT policies. With uv, these pain points are largely eliminated, allowing teams to focus on building software rather than fighting with dependency issues.

For our cross-functional project, uv wasn't just a convenience—it was the enabling factor that made Python a viable option for our team. If you've been struggling with Python environment management, especially on locked-down corporate machines, give uv a try. It might just restore your faith in Python as a practical choice for enterprise development.