* The package formats (source or wheel) do not sufficiently capture assumptions that devs make at release time, in order to produce a usable installable artifac

* The package formats (source or wheel) do not sufficiently capture assumptions that devs make at release time, in order to produce a usable installable artifact at install time. * Worse, when different package builders release binaries, there is insufficient information in the binaries metadata in order to know which sets of binaries will actually install AND run properly on any given system. * Finally, the Python packaging ecosystem suffers from a "Tesla Autopilot" problem: some kinds of things are actually WORSE if they only work 90% of the time. So, many people get by just fine with pip and virtualenv... until they don't. Since package building and dependency-solving are not exactly the sexiest or most fun part of software development, most devs in the modern era don't necessarily take the time to understand the actual roots of the problem, but instead bat around lore and cut-and-paste stackoverflow until things seem to kind of work. A major part of Python's current success is due to its numerical and data analysis libraries. These, in turn, are successful because they take advantage of deep capabilities in C, C++, and other "native" code. This means that Python packaging inherits the original sins of C (the dynamic linker) and of C++ (no standard binary ABI). Most other languages do not have to solve such a hard problem: Perl, Ruby, Node, etc. all don't go nearly as deep as Python does in terms of leveraging a rich ecosystem of native code extension modules. Even Java avoids this and lives almost entirely within the JVM runtime - but even then, classpath conflicts show that dependency management in ANY language is a hard problem unless treated holistically and intentionally. Python got its package system bolted-on after the fact. Then several tools came and went as maintainers entered and exited the ecosystem. With Anaconda and conda, we are just now finally at a point where people can reliably install the basic scientific and numerical libraries, across hardware and OSes..... 20 years after I started using the language. :-)