mutmut VS pyenv

I wrote one (https://github.com/boxed/scientist) as I found the existing ones very complicated and that just gives me a bad feeling. Since I'm the author of mutmut (https://github.com/boxed/mutmut), I also made sure my implementation was 100% mutation tested before I used it in production.

I used my implementation to replace number parsing in my work project: https://kodare.net/2021/04/04/safe_number_parsing.html

Mutmut introduces a clever approach to scrutinizing your tests. It evaluates the effectiveness of your test suite by slightly altering the code after the tests have been written. If a test fails after a minor change, that's a good sign; it means the test is robust enough to catch those changes. But if the test passes even after the code change, it indicates that the test isn't effectively detecting that alteration – this is what Mutmut terms a "surviving mutant."

mutmut for mutation testing.

If you have a requirement for multiple, specific Python versions, why not just use pyenv?

https://github.com/pyenv/pyenv

For more information visit: pyenv repository

Note that virtual environments assume you are using the same global version of Python. Often, this is not the case and additional tools like pyenv can be used alongside virtual environments when you need to switch between versions of Python itself on your local machine.

Python version manager pyenv

First of all, install the Python virtual environment from these links: 1 and 2. I developed my GPT-based API in Python version 3.8.18. Pick any Python versions >= 3.7.

Note: Most modern systems will probably have a system environment that meets this requirement, but if yours does not or if you prefer not to install anything in your system environment (even if it's just PDM) check out asdf or pyenv to help install and manage additional Python environments.

When dealing with software development, reproducibility is key. This is why we encourage you to use Python virtual environments to set up an isolated environment for your project. Virtual environments allow the isolation of dependencies, which plays a crucial role to avoid breaking compatibility between different projects. We cannot cover all the details about virtual environments in this post, but we encourage you to learn more about venv, pyenv or conda for a better understanding on how to create and manage virtual environments.

For Python, with your use case I would avoid system packages, no matter the distro. It sounds like it would be worth setting up pyenv and working exclusively with virtual environments.

For OSX there is homebrew or pyenv (pyenv is another solution on Linux). As pyenv compiles from source it will require setting up XCode (the Apple IDE) tools to support this which can be pretty bulky. Windows users have chocolatey but the issue there is it works off the binaries. That means it won't have the latest security release available since those are source only. Conda is also another solution which can be picked up by Visual Studio Code as available versions of Python making development easier. In the end it might be best to consider using WSL on Windows for installing a Linux version and using that instead.