It was fine, back in the early days (I started with 1.4-ish). I just downloaded the tarball, unpacked, configured, make, installed into /usr/local on my workstation, then downloaded and stuck any packages into site-packages. Numeric was sometime tricky to compile right, but ye olde "configure && make && make install" worked fine.
Of course, that worked because 1) I was really only doing one project, not juggling multiple ones, 2) there weren't all that many dependencies (Numeric, plotting, etc.), and 3) I was already up to my eyeballs in the build system with SWIG and linking to the actual compute code, so I knew my way around the system.
But every now and then I just shake my fist at the clouds then mutter darkly about just installing the dang thing and maybe not taking on so many dependencies. :-)
I’m newish to python, I’ve only used it for machine learning projects and some web scraping. Could somebody elaborate on venv? I just started using it but now everyone in this thread is saying how much they hate it. Is there an alternative?
venv is fine. Remember that this is a self-selected sample of people. You’re going to bump into the flaws and gotchas but it’s a perfectly usable tool.
Uhm so I was professionally setting up ML distros and ML containers for cloud deployments. Venv is not fine, especially if you've seen how other langs do it.
IMHO venvs are fine as an implementation detail, a building block for a slicker tool.
The annoyance with venvs is you have to create and activate them. In contrast for cargo (or stack or dotnet or yarn or pipenv or poetry), you just run the build tool in the project directory.
Another limitation of venv is it doesn't solve the problem of pinning a versions of Python, so you need another tool.
It also helps that even minor versions introduce breaking changes.
I doubt anyone really knows Python that well, unless they are on the core team.