I’m setting up a new laptop and considering which of the (many) environment managers to use this time around. My standard has been miniconda, since a big plus for me is the ability to set and download specific python version for different projects all in one tool. I also quite like having global access to different environments (i.e. environments aren’t tied to specific projects). I typically have a standard GenDataSci environment always available for initially testing things out, then if I know I’ll be continuing as a single project I’ll make a stand alone environment for it.
But I’ve also used poetry for tighter control and reproducibility when I’m actually packaging to publish on PyPI. Hatch looks interesting as well but I can’t tell if it includes the ability to have separate python version installs for each environment.
What workflows and managers are people using now?
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It depends on what I’m developing: if I’m using Python for prototyping stuff for another language, using Jupyter with Anaconda, without virtual environments, tends to be my go-to, so I can have everything I need easily available and easy to debug. However, when I’m working on a package or script that will have to be used by others, I’m using vanilla virtual environments, so that I can check if everything works correctly in a vanilla environment.
I’ve never looked into poetry, though, what are its upsides?
For one thing, it creates a lock file which is super useful for packaging. Rather than just listing often open-ended package requirements, it defines exactly which versions are installed and locks to that. I think it also has a pretty strict dependency resolver which, again, is nice for package publishing if a bit frustrating for development. Also it makes publishing to PyPI very easy, with nice commands inside poetry rather than needing to use something else like flit.
I’m a PDM guy.
Interesting! To use different python versions, do you need to install them separately with something like pyenv, then point pdm to that? Or can it download and use your chosen python version automatically?
I’m a big fan of Poetry, mainly for it’s heavy use of
pyproject.tomlwhich lets you store your dependencies alongside your black, isort, and coverage config in one file. It keeps everything near and tidy.It also makes publishing packages to PyPI a breeze.
Yeah, I also really like the pyproject.toml. but it seems like most modern systems use it, I know hatch does too.
