Nick Coghlan wrote:
I hadn't really thought about it that way before this discussion - it
is the identity checking behaviour of the builtin containers that lets
us sensibly handle cases like sets of NumPy arrays.
Except that it doesn't:
>>> from numpy import array
>>> a1 = array([1,2])
>>> a2 = array([3,4])
>>> s = set([a1, a2])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'numpy.ndarray'
Lists aren't trouble-free either:
>>> lst = [a1, a2]
>>> a2 in lst
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous.
Use a.any() or a.all()
--
Greg
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