Programming

17528 readers

238 users here now

Welcome to the main community in programming.dev! Feel free to post anything relating to programming here!

Cross posting is strongly encouraged in the instance. If you feel your post or another person's post makes sense in another community cross post into it.

Hope you enjoy the instance!

Rules

Follow the programming.dev instance rules
Keep content related to programming in some way
If you're posting long videos try to add in some form of tldr for those who don't want to watch videos

Wormhole

Follow the wormhole through a path of communities [email protected]

founded 2 years ago

MODERATORS

[email protected]

202

Is Python's tooling incredibly difficult, or am I just stupid? (self.programming)

submitted 3 weeks ago by zaphodb2002 to c/[email protected]

115 comments fedilink hide all child comments

So I'm no expert, but I have been a hobbyist C and Rust dev for a while now, and I've installed tons of programs from GitHub and whatnot that required manual compilation or other hoops to jump through, but I am constantly befuddled installing python apps. They seem to always need a very specific (often outdated) version of python, require a bunch of venv nonsense, googling gives tons of outdated info that no longer works, and generally seem incredibly not portable. As someone who doesn't work in python, it seems more obtuse than any other language's ecosystem. Why is it like this?

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 2 points 3 weeks ago (2 children)

Coming at this from the JS world... Why the heck would 2 projects share the same library? Seems like a pretty stupid idea that opens you up to a ton of issues, so what, you can save 200kb on you hard drive?

[–] [email protected] 4 points 3 weeks ago (1 children)

Yeah, not sure I would listen to this guy. Setting up a venv for each project is about a bare minimum for all the teams I've worked on.

That being said python env can be GBs in size (especially when doing data science).

[–] [email protected] 2 points 2 weeks ago

especially when doing data science

500MB for Ray, another 500MB for Polars (though that was a bug IIRC), a few more megs for whatever binaries to read out those weird weather files (NetCDF and Grib2).

[–] [email protected] 3 points 3 weeks ago* (last edited 3 weeks ago)

Why the heck would 2 projects share the same library?

Coming from the olden days, with good package management, infrequent updates and the idea that you wanted to indeed save that x number of bytes on the disk and in memory, only installing one was the way to go.

Python also wasn't exactly a high brow academic effort to brain storm the next big thing, it was built to be a simple tool and that included just fetching some library from your system was good enough. It only ended up being popular because it is very easy to get your feet wet and do something quick.