So, my major comparator for ease of installation is a statistical programming language called R. Both Python and R have equal numbers of C/C++ dependencies, bu

So, my major comparator for ease of installation is a statistical programming language called R. Both Python and R have equal numbers of C/C++ dependencies, but the only breakage I experience is with Python. This is for a number of reasons: 1) R has CRAN (like CPAN, but for R). If your package does not build on the latest version of R, it becomes unavailable. 2) if your package has C++ dependencies, the installation will error out and say which headers its missing. This means that I can search for the header and install it. 3) Because of the removal of obsolete packages, dependencies just work and you can be assured that a package will build with a given version of R. Updates can be a problem, but they are opt-in at the package level. The core issue with Python is the acceptance that different projects can have entirely different dependencies, which means that this breakage is relatively common. Like, I build C/C++ on a semi-regular basis, and it can be super-painful, but Python is the only higher-level language that gives me this heartache. Not to mention that if you install pip from the Linux repositories, you're setting yourself up for a world of pain (this actually stopped me from learning Python for a number of years). To be fair, I mostly use conda for C/C++ dependencies as it works much better, but lots of packages are still only available through pip, which means I can't avoid the problem. To reiterate, I am super happy with this release of pip, as it will make my life much better. It doesn't go far enough, but it is much, much better than what existed before. Finally, Python packaging is a horrible wart on an otherwise good language, and I'm probably going to argue against using it in the future because of this.