Here’s a hack that lets you keep using ==:
class IsCompare:
__array_priority__ = 999999 # needed to make it work on either side of `==`
def __init__(self, val): self._val = val
def __eq__(self, other): return other is self._val
def __neq__(self, other): return other is not self._val
a == IsCompare(None) # a is None
a == np.array(IsCompare(None)) # broadcasted a is None
Eric
On Mon, 17 Jul 2017 at 17:45 Robert Kern
On Mon, Jul 17, 2017 at 2:13 AM,
wrote: Dear all
I have object array of arrays, which I compare element-wise to None in
various places:
a =
numpy.array([numpy.arange(5),None,numpy.nan,numpy.arange(6),None],dtype=numpy.object)
a array([array([0, 1, 2, 3, 4]), None, nan, array([0, 1, 2, 3, 4, 5]), None], dtype=object) numpy.equal(a,None) FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
So far, I always ignored the warning, for lack of an idea how to resolve it.
Now, with Numpy 1.13, I have to resolve the issue, because it fails with:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
It seem that the numpy.equal is applied to each inner array, returning a Boolean array for each element, which cannot be coerced to a single Boolean.
The expression
numpy.vectorize(operator.is_)(a,None)
gives the desired result, but feels a bit clumsy.
Wrap the clumsiness up in a documented, tested utility function with a descriptive name and use that function everywhere instead.
-- Robert Kern _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion