The damaging thing is claiming things like PythonJS are implementations of Python, when they merely share syntax and some semantics. They might be fast, but that's not very relevant.
Runtimes like PyPy is where we should be looking, since they implement the full Python language and are fast.
Compared to what? Please be specific in your definition of "slow."
>Runtimes like PyPy is where we should be looking, since they implement the full Python language and are fast.
Why should 'we' be looking to PyPy? Have you run a non-trivial application with pypy (v. standard cpy) and found it to be significantly faster?
Even if your code would benefit from JIT, and your code is compatible for running on PyPy, does it benefit more from a compiler switch than CPython + Pandas?
Yes, I have run non-trivial applications on PyPy and they were significantly faster. Its only downside is libraries that depend on CPython extensions, but there are few of those left.
Pandas doesn't do much for removing function call overhead. I don't necessarily have large mathematical datasets, I just want my web service to be easier to write, by not having to care about what CPython is terribly slow at.
C most likely is, or at least more reliably. JS engines specialise to proper arrays most of the time, though. They're quite remarkably fast, much like PyPy.
CPython, the original Python interpreter written in C, is not particularly fast at anything.
I could've manually inlined lots of functions to avoid the overhead on CPython, but I'd have a horrible code base. On PyPy I can simply not care about this aspect, just like in most other languages.