[short_sells_poo]

I wonder to what degree these complex problems are self inflicted due to more than a decade of flipflopping between a myriad packaging and environment management solution. In such an environment, you are bound to have so many different approaches between important projects that trying to bring them all under one umbrella becomes next to impossible. What is a python package is relatively well defined, but how you build one is completely up to you.

Edit: I've just read another comment which I think pointed out the most pertinent fact - that python has often served as mere glue code for stuff written in lower level languages. This then results in an explosion of complexity as a python package not only has to package the python part, but has to be able to build and package the lower level stuff.

[wiredfool]

I'd have to look at numbers to be sure, but I think that the number of popular packages that include compiled libraries is dramatically higher than it was 10 years ago, and that's about when wheels were taking over. The Data/Science stack has really exploded in that time, and it's very heavily skewed towards packaging c/fortran/rust libraries. Previously there was PIL, some database drivers, and numpy was just getting started. I think more of the big packages were pure python. The earlier egg/easy_install/msi/exe installers have all faded away, and now it's really just wheels and sdists.

[andrewf]

What made it click for me is "Python packaging" means "how to install everything that I want to use from Python."

I wouldn't have considered "how to get BLAS onto a system" to be a "Python packaging" issue, but for people who want to rely on it via scipy/numpy/whatever, it is.