I used a requirements.in file to list out all the top-level direct dependencies & then used pip-compile from piptools to convert that into a frozen list of versioned dependencies. pip-compile is also nice because it doesn't upgrade unless explicitly asked to which makes collaboration really nice. I then used the requirements.txt & various supporting tooling to auto-create & keep updated a virtualenv (so that my peers didn't need to care about python details & just running the tool was reliable on any machine). It was super nice but there's no existing tooling out there to do anything like that & it took about a year or two to get the tooling into a nice place. It's surprisingly hard to create Python scripts that work reliably out-of-the-box on everyone's environments without the user having to do something (which always means in my experience that something doesn't work right). C modules were more problematic (needing Xcode installation on OSX, potentially precompiled external libraries not available via pip but also not installed by default), but I created additional scripts to help bring a new developer's machine to a "good state" to take manual config out of the equation. That works in a managed environment where "clean" machines all share the same known starting state + configs - I don't know how you'd tackle this problem in the wild.

I do think there's a lot of low-hanging fruit where Python could bake something in to auto-setup a virtualenv for a script entrypoint & have the developer just list the top-level dependencies & have the frozen dependency list also version controlled (+ if the virtualenv & frozen version-controlled dependency list disgaree rebuild virtualenv).