a. Better quality and maybe programmer productivity through static checking?
b. Better performance thanks to enforced type letting the compiler do a better job?
c. Something else?
Thanks!
Further down these are cast into more native types, and then walk out into public API's.
And there once more you need to do somewhat strict data-validation.
For these cases, typing rules and annotations will be a boon.
And do note, that there is more common a case of human error or refactoring error that causes problem. Someone sending in a default [] array in an object, and somewhere else it's an expected Boolean.
That matches well what I had in mind with my "a." suggestion.
Now, why cannot we expect "b. Better performance thanks to enforced type letting the compiler do a better job"? Are the semantics of what's being discuss insufficient to provide such benefits? I'm asking that question because I read often that dynamic typing is the #1 performance hit of Python (e.g. [1]). Now that this PEP lets developers provide and enforce type, what's still in the way of reaching the level of performance of statically-typed languages?
[1] Alex Gaynor - Fast Python, Slow Python: https://www.youtube.com/watch?v=7eeEf_rAJds
You can use PEP3107 annotations today and automatically validate them using a decorator to eliminate boilerplate code like that. They don't have to be something to look forward to tomorrow.
A (relatively) high performance runtime annotation type checker.
from ensure import ensure_annotations
@ensure_annotations
def f(x: int, y: float) -> float:
return x+y