I see Julia and JavaScript eventually wining over those that can't be bothered to deal with C for the extra performance step.
The point is the lack of adoption of JIT runtimes across the Python community at large.
Including things like modifying every pixel of an image, and other things that need to be fast-ish.
That has always been my reason for not giving a Cython a real chance. I'm afraid I would go through the trouble porting everything, then have a use case where I need to just run pure python.
I've considered having a compat layer where you import dummy versions of all the classes and decorators if cython isn't present. Has anyone ever tried that?
[1] https://github.com/cython/cython/issues/1672#issuecomment-33...
(On a side note, using this feature led to a particularly memorable debugging session that set a personal record in terms of time spent vs amount of code changed to fix the issue: https://news.ycombinator.com/item?id=11115110 ).
https://cython.readthedocs.io/en/latest/src/tutorial/pure.ht...
In the standard library, they maintain both Python and C versions of some modules:
I feel that PyPy need to receive much more love and attention than it actually gets. I already donated, and put all my hopes on PyPy. The Windows is also very renegaded, where the multiprocessing module is unusable. I hope to get some time soon to help this incredible project and finish lib_pypy/_winapi.py.