numpy ndarray hashability

Question

I have some problems understanding how numpy objects hashability is managed.

>>> import numpy as np
>>> class Vector(np.ndarray):
...     p         


        

Answer 1

I get the same results in Python 2.6.6 and numpy 1.3.0. According to the Python glossary, an object should be hashable if __hash__ is defined (and is not None), and either __eq__ or __cmp__ is defined. ndarray.__eq__ and ndarray.__hash__ are both defined and return something meaningful, so I don't see why hash should fail. After a quick google, I found this post on the python.scientific.devel mailing list, which states that arrays have never been intended to be hashable - so why ndarray.__hash__ is defined, I have no idea. Note that isinstance(nparray, collections.Hashable) returns True.

EDIT: Note that nparray.__hash__() returns the same as id(nparray), so this is just the default implementation. Maybe it was difficult or impossible to remove the implementation of __hash__ in earlier versions of python (the __hash__ = None technique was apparently introduced in 2.6), so they used some kind of C API magic to achieve this in a way that wouldn't propagate to subclasses, and wouldn't stop you from calling ndarray.__hash__ explicitly?

Things are different in Python 3.2.2 and the current numpy 2.0.0 from the repo. The __cmp__ method no longer exists, so hashability now requires __hash__ and __eq__ (see Python 3 glossary). In this version of numpy, ndarray.__hash__ is defined, but it is just None, so cannot be called. hash(nparray) fails andisinstance(nparray, collections.Hashable) returns False as expected. hash(vector) also fails.