Handling of dtype bool is not straight-forward

I would expect a numpy array containing boolean values to react meaningful to “is True”

Reproducing code example:

import numpy as np
np.array([True])[0] is True
Out[3]: False
np.array([True])[0] == True
Out[4]: True

dtype evaluates to dtype(‘bool’).

I know that the dtype is not build-in anymore, and technically it does not “point to the same object” but I see no reason why numpy should not gracefully handle that situation and do what the user obviously wants to do there.

[eric-wieser]

More information on integer caching in python, which is obviously by design.

The is operator in python simply means “these are the same instances of the object” - the language makes no guarantees about whether two integers are identical or different instances. I don’t think there’s anything to report here.

[pv]

>>> type(np.array([True])[0])
<class 'numpy.bool_'>

The behavior is intentional and will not be changed. If you want to cast to Python object, do

>>> np.array([True])[0].item() is True
True