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    <p>I get the same results in Python 2.6.6 and numpy 1.3.0. According to <a href="http://docs.python.org/glossary.html#term-hashable">the Python glossary</a>, an object should be hashable if <code>__hash__</code> is defined (and is not <code>None</code>), and either <code>__eq__</code> or <code>__cmp__</code> is defined. <code>ndarray.__eq__</code> and <code>ndarray.__hash__</code> are both defined and return something meaningful, so I don't see why <code>hash</code> should fail. After a quick google, I found <a href="http://osdir.com/ml/python.scientific.devel/2005-09/msg00035.html">this post on the python.scientific.devel mailing list</a>, which states that arrays have never been intended to be hashable - so why <code>ndarray.__hash__</code> is defined, I have no idea. Note that <code>isinstance(nparray, collections.Hashable)</code> returns <code>True</code>.</p> <p>EDIT: Note that <code>nparray.__hash__()</code> returns the same as <code>id(nparray)</code>, so this is just the default implementation. Maybe it was difficult or impossible to remove the implementation of <code>__hash__</code> in earlier versions of python (the <code>__hash__ = None</code> 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 <code>ndarray.__hash__</code> explicitly?</p> <p>Things are different in Python 3.2.2 and the current numpy 2.0.0 from the repo. The <code>__cmp__</code> method no longer exists, so hashability now requires <code>__hash__</code> and <code>__eq__</code> (see <a href="http://docs.python.org/py3k/glossary.html#term-hashable">Python 3 glossary</a>). In this version of numpy, <code>ndarray.__hash__</code> is defined, but it is just <code>None</code>, so cannot be called. <code>hash(nparray)</code> fails and<code>isinstance(nparray, collections.Hashable)</code> returns <code>False</code> as expected. <code>hash(vector)</code> also fails.</p>
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    1. POnumpy ndarray hashability
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    1. COTHanks a lot for your answer. Regarding your edit, I do not reproduce what you say. I actually have >>> nparray.__hash__() 269709177 >>> id(nparray) 4315346832 so I'm still puzzled. I'm adding this in my post as code is unreadable in comments
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    2. COHmm... the hashes are the same the ids for me in Python 2.6.6 and numpy 1.3.0, but not in Python 2.7.2 and numpy 1.5.1. This is all very strange. It would probably help if I didn't happen to have a different version of numpy on every version of Python I have knocking around. Anyway, as far as I can see, the default definition of `__hash__` has returned the `id` for quite some time, so I suppose they must have explicitly overridden it to do something different in at least some versions of numpy.
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