Python has been my go-to for prototyping since 2003. Maybe I’ve just adjusted to its quirks, but I keep returning to it despite picking up another dozen languages since. I’ve encountered other people who hate it, but never understood why; what issues are you experiencing?
If you don't mind me chiming in, mostly looking for advice if you've got any. I tend to run into a lot of issues when trying to play with projects that use TensorFlow. I have an Apple Silicon laptop and I seem to always get stuck resolving circular, conflicting dependencies.
The worst offender is https://github.com/magenta/magenta. It's such a cool project and I got it running once a few years ago but recently lost a few solid weekends trying to get it up and running again.
I will say that the most issues I’ve encountered with Python happened on macOS machines. The default installation was always old, and success using a modern version depended highly on the techniques used to install it.
For anything that involves dependencies, I rely on venv or pyenv to create a clean environment. When on macOS, I tended to use docker/containers as well, but primarily because 99% of my Python work has been in a Linux environment and I wanted like-for-like as much as possible. But a good version manager directly on macOS should help quite a bit.
> I will say that the most issues I’ve encountered with Python happened on macOS machines.
Same here. It's probably somewhat to do with my only partial MacOS/Homebrew knowledge, but every time I'd ressurect a Python project on MacOS, Homebrew would end up screwing up / confusing / munging the system Python and it's own ones.
Never ran into problems like that on Linux (except for that one time I tried Linux homebrew). Just something about the way homebrew does stuff seems incompatible with me understanding it. Seems to be a "just me" thing though.
I just want to add that using the tool "asdf" for managing python versions on your Mac is very handy. And "direnv" is also great for auto-loading your environment when going into a project directory.
I honestly don’t think I’ve ever been able to git clone any python project (that isn’t hello world equivalent), follow the readme and have it just run first shot.
I’m always having to dive in and figure out which packages are missing, wrong version, etc.
As I do this I find myself wondering if the repo maintainers make a habit of actually trying to set up their project from scratch just by following their readmes.
I’m assuming they just get out of date and then leave it to the community to troubleshoot their own installs.
For whatever reason I don’t seem to have nearly the same difficulties trying to clone and run rust or node projects for example.
More generally, the fact that there are about 20 different ways to manage your venvs, and that you seemingly tend to accrete every version of python released in the last 20 years with no clear way of managing all those installations, makes it quite confusing for newbies.
Then work on that repo? Everything after "venv" is too new for me, and I've ignored it and somehow not had any issues. If there are some packages that rely on C code and don't have wheels or whatever, you need to deal with the C ecosystem which is the real hell in my opinion.
All the autotools/cmake/scons, library paths, header paths, PKG_CONFIG, etc. I've had so many issues building C projects that I can't even begin to remember all the issues.
No. Usually that will pick up newer versions of the project's transitive dependencies, which will have breaking changes (because these days breaking changes in minor versions is what all the cool kids are doing). Since it's Python you won't find out until you hit the wrong codepath while using the program and get a runtime error.
Curious what trouble you've had? The Python package ecosystem has always "just worked" for me. That's true regardless of the interpreter implementation I use. I generally test my projects against both CPython and Pypy.