It's often not feasible for legal or regulatory reasons to install bundled versions of C libraries. Like most enterprise software wants to use the system versio

It's often not feasible for legal or regulatory reasons to install bundled versions of C libraries. Like most enterprise software wants to use the system version of openssl because otherwise the vendor has to scramble to update for each openssl security issue. Even when it is possible to install bundled libraries, someone has to do the work to set up Python packages that bundle the native libraries. This work is done for some of the most important pacakges like TensorFlow, but probably not for some more esoteric library you want to use. I also suspect that people seriously using TensorFlow probably want careful control over which version they are using, rather than letting a random Python package manage that. Basically, interacting with native libraries is a hard problem to fully solve for any language. A lot of other languages like Java have struggled with this as well. But at least in Java you seldom use native dependencies. In Python you use them all the time (arguably, serving as glue code for crusty old FORTRAN and C libraries is where Python shines.) Python's stubborn determination not to standardize on anything for packaging (we have easy_install, conda, pip, pipenv, poetry, and who knows how many others) as well as its insistence on breaking its own compatibility certainly don't help either. And Python is more hostile than most languages to integrating with the system package manager (RPM, deb, etc.) because of how it sprays its files around the filesystem.