UV - The Tool that Simplifies Environment and Dependency Management in Python

Anyone who works with Python knows that it can be a bit annoying to deal with managing virtual environments and dependencies. This is essential for maintaining control over projects, especially those that require specific libraries or different versions of Python. In these scenarios, the UV tool appears as a very interesting solution, combining environment and version management functionalities in an efficient and simplified flow. It was made by the same developers as Ruff, a formatter and linter that, like UV, was also written in Rust. In other words, performance is not a problem. In this post, we're going to talk a little about how UV can help developers maintain organization, productivity and flexibility in the development of their projects.

What is UV?

UV is a tool that integrates virtual environment management, dependency control and Python version management functionalities. Inspired by popular tools like pyenv and pipenv, UV brings together the best of both worlds, allowing you to easily create and manage virtual environments at the same time in which it maintains robust control over Python dependencies and versions.

With UV, you can:

• Switch between Python versions in a simple way (similar to Node's nvm)
• Create and manage specific virtual environments for each project
• Manage dependencies with version control and ease of installation
• Maintain a history of project dependencies to ensure portability

1. Virtual Environment Management

Virtual environments allow you to isolate a project's dependencies to avoid conflicts between packages and versions. However, manually managing these environments can be laborious and sometimes confusing. With UV, this task becomes much more fluid.

Creating a virtual environment is simple and quick with the uv venv command. After that, you can activate the environment with source .venv/Scripts/activate or even configure UV to activate the environment automatically when entering the project folder, facilitating the process and avoiding compatibility problems between different projects.

In addition, UV offers practical functionality to view existing environments and delete them when they are no longer needed, freeing up space and keeping your system organized.

2. Python Version Management

Many developers face the challenge of managing different versions of Python on their systems. Certain projects may require version 3.7, while others depend on functionality from version 3.9 or 3.11. With UV, you can easily install and switch between multiple versions of Python, all intuitively.

To install a new version of Python, just run uv python install , and UV will take care of the rest. After that, you can choose which version to use in each environment with uv python pin , ensuring that each project has the ideal version for its operation. This is especially useful for developers working on multiple projects or who need to maintain compatibility with older library versions.

3. Dependency Management and requirements.txt File

Just like pipenv, UV helps you manage dependencies in an organized way. With simple commands like uv add and uv remove , you can add or remove packages from the virtual environment. To simply update the requirements.txt file, simply use the uv pip compile pyproject.toml -o requirements.txt command. This command will cause all dependencies contained in the pyproject.toml file to be automatically added to requirements.txt, ensuring that all project dependencies are listed and updated.

UV also allows you to install specific package versions, which helps you avoid compatibility issues when working in a team or sharing the project with other developers. And if you need a clean installation of dependencies, the uv sync command reconfigures the environment based on the requirements.txt file, ensuring everything is in order.

4. Productivity and Ease of Use

The simplicity of UV is a big differentiator, as it allows developers to focus on what really matters: the code. With easy-to-remember commands and an intuitive structure, UV saves time and reduces the complexity of working with virtual environments and dependencies.

For example, when using UV, you no longer need to remember extensive commands to create or activate environments, or worry about Python version conflicts between projects. With a single tool, you have everything you need for complete management of environments and dependencies, resulting in significantly greater productivity.

It is still possible to integrate UV with other development tools, such as Ruff, already mentioned, and even Docker. This way, you can adapt the UV settings to the specific needs of your project. Anyway, there are many possibilities. The tool's documentation is very complete and is worth checking out.

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