However, implementation aside, I think it's invalid to assert that the existence of a cottage industry is inherently a sign of weakness; it's also a sign of just how pervasively Python is used. Find me another language that's used as widely, as popularly (i.e. not just two random guys using it that way), and in as many different kind of settings as Python. (Java might be a contender, except that it's not really used for the really high-performance scientific workloads like Python.)
Integration is hell. This is a universal fact.
I'd rather pick a tool which doesn't add integration requirements!
I made my case explicitly, and it's about technical limitations of CPython that can (and, at some points WILL) be overcome. PyPy is a step in that direction, though not mature yet.
Python, in the form of the standard and most used implementation for scientific, technical etc programming, CPython, DOES add integration requirements.
It necessitates the use of C/Swig/Cython etc extensions for performance critical stuff. And it necessitates it, not because it puts a gun in your head or says so in some contract clause, but in the pragmatic way, that people find it's not fast enough for their needs in pure form.
That's why all the popular related Python projects are not written in pure Python, but in C/Fortran/et al (NumPy etc).
That's because you constrain your use of Python in I/O bound problems, like webpages and such. If you had any CPU bound problems, then you would have found Python is quite slow -- and you can get 2-20-100 times the performance in another language.
Python is very slow (compared to what it can be), which is what necessitates tons of workarounds like: C extensions, SWIG, NumPy and SciPy (C, Fortran, etc), Cython, PyPy, etc. A whole cottage industry around Python's slowness. Which is nice to exist, but not an ideal solution: suddenly you have 2 codebases to manage in 2 different languages, makefiles, portability issues, etc etc. All of those are barriers to entry.