everyone focuses on the tooling, not many are focusing on the reason: python is extremely dynamic. like, magic dynamic you can modify a module halfway through an import, you can replace class attributes and automatically propagate to instances, you can decompile the bytecode while it’s running.

combine this with the fact that it’s installed by default and used basically everywhere and you get an environment that needs to be carefully managed for the sake of the system.

js has this packaging system down pat, but it has the advantage that it got mainstream in a sandboxed isolated environment before it started leaking out into the system. python was in there from the beginning, and every change breaks someone’s workflow.

the closest language to look at for packaging is probably lua, which has similar issues. however since lua is usually not a standalone application platform it’s not a big deal there.

Difficult? How so? I find compiling C and C++ stuff much more difficult than anything python. It never works on the first try whereas with python the chances are much much higher.

What’s is so difficult to understand about virtual envs? You have global python packages, you can also have per user python packages, and you can create virtual environments to install packages into. Why do people struggle to understand this?

The global packages are found thanks to default locations, which can be overridden with environment variables. Virtual environments set those environment variables to be able to point to different locations.

python -m venv .venv/ means python will execute the module venv and tell it to create a virtual environment in the .venv folder in the current directory. As mentioned above, the environment variables have to be set to actually use it. That’s when source .venv/bin/activate comes into play (there are other scripts for zsh and fish). Now you can run pip install $package and then run the package’s command if it has one.

It’s that simple. If you want to, you can make it difficult by doing sudo pip install $package and fucking up your global packages by possibly updating a dependency of another package - just like the equivalent of updating glibc from 1.2 to 1.3 and breaking every application depending on 1.2 because glibc doesn’t fucking follow goddamn semver.

As for old versions of python, bro give me a break. There’s pyenv for that if whatever old ass package you’re installing depends on an ancient 10 year old python version. You really think building a C++ package from 10 years ago will work more smoothly than python? Have fun tracking down all the unlocked dependency versions that “Worked On My Machine 🏧” at the start of the century.

The only python packages I have installing are those with C/C++ dependencies which have to be compiled at install time.

Y’all have got to be meme’ing.

Anti Commercial-AI license

It’s something of a “14 competing standards” situation, but uv seems to be the nerd favourite these days.

Python’s packaging is not great. Pip and venvs help but, it’s lightyears behind anything you’re used to. My go-to is using a venv for everything.

With all the hype surrounding Python it’s easy to forget that it’s a really old language. And, in my opinion, the leadership is a bit of a mess so there hasn’t been any concerted effort on standardizing tooling.

Some unsolicited advice from somebody who is used more refined build environments but is doing a lot of Python these days:

The whole venv thing isn’t too bad once you get the hang of it. But be prepared for people to tell you that you’re using the wrong venv for reasons you’ll never quit understand or likely need to care about. Just use the bundled “python -m venv venv” and you’ll be fine despite other “better” alternatives. It’s bundled so it’s always available to you. And feel free to just drop/recreate your venv whenever you like or need. They’re ephemeral and pretty large once you’ve installed a lot of things.

Use “pipx” to install python applications you want to use as programs rather than libraries. It creates and manages venvs for them so you don’t get library conflicts. Something like “pip-tools” for example (pipx install pip-tools).

Use “pyenv” to manage installed python versions - it’s a bit like “sdkman” for the JVM ecosystem and makes it easy to deal with the “specific versions of python” stuff.

For dependencies for an app - I just create a requirements.txt and “pip install -r requirements.txt” for the most part… Though I should use one of the 80 better ways to do it because they can help with updating versions automatically. Those tools mostly also just spit out a requirements.txt in the end so it’s pretty easy to migrate to them. pip-tools is what my team is moving towards and it seems a reasonable option. YMMV.

You re not stupid, python’s packaging & versionning is PITA. as long as you write it for yourself, you re good. As soon as you want to share it, you have a problem

Python never had much of a central design team. People mostly just scratched their own itch, so you get lots of different tools that do only a small part each, and aren’t necessarily compatible.

I agree. Python is my language of choice 80% or so of the time.

But my god, it does packaging badly! Especially if it’s dependent on linking to compiled code!

Why it is like that, I couldn’t tell. The language is older than git, so that might be part of it.

However, you’re installing python libraries from github? I very very rarely have to do that. In what context do you have to do that regularly?

venv nonsense

I mean, the fact that it isn’t more end-user invisible to me is annoying, and I wish that it could also include a version of Python, but I think that venv is pretty reasonable. It handles non-systemwide library versioning in what I’d call a reasonably straightforward way. Once you know how to do it, works the same way for each Python program.

Honestly, if there were just a frontend on venv that set up any missing environment and activated the venv, I’d be fine with it.

And I don’t do much Python development, so this isn’t from a “Python awesome” standpoint.

This is exactly how I feel about python as well… IMHO, it’s good for some advanced stuff, where bash starts to hit its limits, but I’d never touch it otherwise

It… depends. There is some great tooling for Python – this was less true only a few years ago, mind you – but the landscape is very much in flux, and usage of the modern stuff is not yet widespread. And a lot of the legacy stuff has a whole host of pitfalls.

Things are broadly progressing in the right direction, and I’d say I’m cautiously optimistic, although if you have to deal with anything related to conda then for the time being: good luck, and sorry.

The difficulty with python tooling is that you have to learn which tools you can and should completely ignore.

Unless you are a 100x engineer managing 500 projects with conflicting versions, build systems, docker, websites, and AAAH…

• you don’t really need venvs
• you should not use more than on package manager (I recommend pip) and you should cling to it with all your might and never switch. Mixing e.g. conda, on linux system installers like apt, is the problem. Just using one is fine.
• You don’t “need” need any other tools. They are bonuses that you should use and learn how to use, exactly when you need them and not before. (type hinting checker, linting, testing, etc…)

Why is it like this?

Isolation for reliability, because it costs the businesses real $$$ when stuff goes down.

venvs exists to prevent the case that “project 1” and “project 2” use the same library “foobar”. Except, “project 1” is old, the maintainer is held up and can’t update as fast and “project 2” is a cutting edge start up that always uses the newest tech.

When python imports a library it would use “the libary” that is installed. If project 2 uses foobar version 15.9 which changed functionality, and project 1 uses foobar uses version 1.0, you get a bug, always, in either project 1 or project 2. Venvs solve this by providing project specific sets of libraries and interpreters.

In practice for many if not most users, this is meaningless, because if you’re making e.g. a plot with matplotlib, that won’t change. But people have “best practices” so they just do stuff even if they don’t need it.

It is a tradeoff between being fine with breakage and fixing it when it occurs and not being fine with breakage. The two approaches won’t mix.

very specific (often outdated) version of python,

They are giving you the version that they know worked. Often you can just remove the specific version pinning and it will work fine, because again, it doesn’t actually change that much. But still, the project that’s online was the working state.

Tried to install Automatic1111 for Stable Diffusion in an Arch distrobox, and despite editing the .sh file to point to the older tarballed Python version as advised on Github, it still tells me it uses the most up to date one that’s installed system wide and thus can’t install pytorch. And that’s pretty much where my personal knowledge ends, and apparently that of those (i.e. that one person) on Github. ¯\_(ツ)_/¯

Always funny when people urge you to ask for help but no one ends up actually helping.

Yeah the tooling sucks. The only tooling I’ve liked is Poetry, I never have trouble installing or packaging the apps that use it.

This isn’t the answer you want, but Go(lang) is super easy to learn and has a ton of speed on python. Yes, it’s more difficult, but once you understand it, it’s got a lot going for it.