BUG: np.str_ and np.bytes_ object have a confusing __repr__ and __str__

>>> s = np.str_("\0\0")
>>> list(s)
['\0', '\0']
>>> len(s)
2
>>> s
''  # wat

>>> b = np.bytes_(2)
>>> list(b)
[0, 0]
>>> len(b)
2
>>> b
b''  # wat

Caused by

https://github.com/numpy/numpy/blob/f15026ce782f3b8c291cde5363b86f4f1bc88ad8/numpy/core/src/multiarray/scalartypes.c.src#L389-L425

It looks to me like we should just remove our overloads of these functions, they don’t make any sense.

[seberg]

True, although that code will probably give the warning (although if you have many of that you would not notice). Its true from a subclassing perspective… It seems we either have to sacrifice consistent subclassing or consistency with arrays? Since this is NumPy scalar, which is almost an array, I think I still lean a bit towards 3. But basically there is no good solution and either 3. or 4. seem better than the current state to me.

[seberg]

I am for Option 3.

Any option should have the same result as np.array(string, dtype="S")[()] and np.array(str, "S") and np.string_(str) should be as similar as possible (at the moment at least, due to PyArray_Return).

Since it can be a bug for someone who needs the zero bytes, I think a warning makes sense. The only question is whether the warning for some reason is prohibitively noisy. I do not expect that. It could point out that void can sometimes be a way to spell bytes of fixed length.