Just out of curiosity, I haven't seen anyone recommend miniconda... Why so, is there something wrong I'm not aware of?

I'm no expert, but I totally feel you, python packages, dependencies and version matching is a real nightmare. Even with venv I had a hard time to make everything work flawlessly, especially on MacOS.

However, with miniconda everything was way easier to configure and worked as expected.

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very specific (often outdated) version of python,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.

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

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.