Pypy has much slower C interop than CPython, which I believe is part of the tradeoff. Eg data analysis pipelines are probably still faster in numpy on CPython than pypy.

Not an expert here, but my understanding is that Python is dynamic to the point that optimizing is hard. Like allowing one namespace to modify another; last I used it, the Stackdriver logging adapter for Python would overwrite the stdlib logging library. You import stackdriver, and it changes logging to send logs to stackdriver.

All package level names (functions and variables) are effectively global, mutable variables.

I suspect a dramatically faster Python would involve disabling some of the more unhinged mutability. Eg package functions and variables cannot be mutated, only wrapped into a new variable.

See Smalltalk, Self and Common Lisp, and you will find languages that are even more dynamic than Python, and are in the genesis of high performance JIT research.