Python is also heavily used in science, where performance really does matter. It's successful because of how highly ergonomic python apis can be built on top of

Python is also heavily used in science, where performance really does matter. It's successful because of how highly ergonomic python apis can be built on top of optimised C/C++/Fortran libraries. That said, there is clearly a desire to write 'fast' code in python itself without swapping to C. Cython helps, but to get really fast Cython code you actually have to write with C-semantics (so you are basically writing C with Python syntax). Projects like numba JIT are interesting in that they can optimise domain-specific code (i.e. numerical/array code) that's written in normal python style. It also means jumping through a few hoops (although with the latest version in many cases all you need is a single decorator on your hot function). You can even do GIL-less multithreading in some cases. Overall things are looking promising, with the addition of the frame evaluation API and possible improvements to the python C-api that could make JIT and similar extentions easier.