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.
Data typing is important. If two types do not have the same in-memory representation but you treat them like they do, you’re inviting a lot of potential bugs and security vulnerabilities to save a few characters.
ETA: The WONTFIX is absolutely the correct response here. This would allow devs to shoot themselves in the foot for no real gain, eliminating the benefit of things like mypy. Type safety is your friend and will keep you from making simple mistakes.
Even if they do have the same in-memory representation, you may want to assert types as different just by name.
AccountID: u64
TransactionID: u64
have the same in-memory representation, but are not interchangeable.
That is a very solid point. If user-defined types are NOT explicitly defined as compatible (supposing language support), they should not be.
In your example, if it were, say a banking system, allowing both types to be considered equivalent is just asking for customer data leaks.
Python does allow this with NewType. Type checkers see two different types, but it is the same class at runtime.