Anaconda (and miniconda) is a very good solution if you need a container-like environment. I can easily go back to old projects with different python versions and do maintenance in them with no hassle.

pyproject.toml is a better solution

Does it handle binary dependencies and Python ABI changes well? Does it isolate them from almost the entire operating system? Conda does those.

Conda packages compiled such that the search paths of the binaries are not using the OS's (which why the Linux DE Qt theme doesn't work for Spyder).

Conda also comes with well optimized binaries for high performance compute which is absolutely a must for modern data science.

Use docker/podman if you are worried about ABI changes and isolating them from the entire operating system.

Most people's problems getting their Python toolchains to work optimally are caused by using operating systems that don't come with build utils. That's a cultural problem solved by using an operating system with a culture of distributing those tools.

Or use conda.

Or use debian

yes, because installing random python libraries to system python on Debian never broke any system.

Use poetry/Debian. Standard Python tooling is acceptable in 2024; using some busted 3-plus-year-old "supported" environment with a package manager with a really busted constraint solver, which doesn't even come bundled with compilers, is unnecessary.

I've installed plenty of compilers using conda - how long ago was it that you last tried it? even ROS is available in conda now, instead of requiring specific obsolete versions of Ubuntu.