From numpy’s docs:

The bool_ data type is very similar to the Python bool but does not inherit from it because Python’s bool does not allow itself to be inherited from, and on the C-level the size of the actual bool data is not the same as a Python Boolean scalar.

and likewise:

The int_ type does not inherit from the int built-in under Python 3, because type int is no longer a fixed-width integer type.

Someone else points out that Python’s native bool is a subtype of int, so adding a bool to an int (or performing other mixed operations) is not an error, which might then go on to cause of a hard-to-catch semantic/mathematical error.

I am assuming that trying to add a NumPy bool_ to an int causes a compilation error at best and a run-time warning, or traceable program crash at worst.

here’s a good question answer on this topic

https://stackoverflow.com/questions/18922407/boolean-and-type-checking-in-python-vs-numpy

plus this is kinda the tools doing their jobs.

bool_ exists for whatever reason. its not a bool but functionally equivalent.

the static type checker mpy, correctly, states bool_ and bool aren’t compatible. in the same way other type different types aren’t compatible

Technically the Python bool is fine, but it’s part of what makes numpy special. Under the hood numpy uses c type data structures, (can look into cython if you want to learn more).

It’s part of where the speed comes from for numpy, these more optimized c structures, this means if you want to compare things (say an array of booleans to find if any are false) you either need to slow back down and mix back in Python’s frameworks, or as numpy did, keep everything cython, make your own data type, and keep on trucking knowing everything is compatible.

There’s probably more reasons, but that’s the main one I see. If they depend on any specific logic (say treating it as an actual boolean and not letting you adding two True values together and getting an int like you do in base Python) then having their own also ensures that logic.