Explanation: Python is a programming language. Numpy is a library for python that makes it possible to run large computations much faster than in native python. In order to make that possible, it needs to keep its own set of data types that are different from python’s native datatypes, which means you now have two different bool types and two different sets of True and False. Lovely.
Mypy is a type checker for python (python supports static typing, but doesn’t actually enforce it). Mypy treats numpy’s bool_ and python’s native bool as incompatible types, leading to the asinine error message above. Mypy is “technically” correct, since they are two completely different classes. But in practice, there is little functional difference between bool and bool_. So you have to do dumb workarounds like declaring every bool values as bool | np.bool_ or casting bool_ down to bool. Ugh. Both numpy and mypy declared this issue a WONTFIX. Lovely.
I am aware of what you are saying, however, I do not agree with your conclusions. Just for the sake of providing context for our discussion, I wrote plenty of code in statically typed languages, starting in a professional capacity some 33 years ago when switching from pure TASM to AT&T C++ 2, so there is no need to convince me of the benefits :)
That being said, I think we’re talking about different use cases here. When I’m talking configuration, I’m talking runtime settings provided by a customer, or service tech in the field - that hardly maps to a compiler error as you mentioned. It’s also better (more flexible / higher abstraction) than simply checking a JSON schema, and I’m personally encountering multiple new, custom JSON documents every week where it has proven to be a real timesaver.
I also do not believe that all data validation can be boiled down to simple type checking - libraries like pydantic handle complex validation cases with interdependencies between attributes, initialization order, and fields that need to be checked by a finite automaton, regex or even custom code. Sure, you can graft that on after the fact, but what the library does is provide a standardized way of handling these cases with (IMHO) minimal clutter. I know you basically made that point, but the example you gave is oversimplified - at least in what I do, I rarely encounter data that can be properly validated by simple type checking. If business logic and domain knowledge has to be part of the validation, I can save a ton of boilerplate code by writing my validations using pydantic.
Type annotations are a completely orthogonal case and I’ll be the first to admit that Python’s type situation is not ideal.