What is the reason for this weirdness in numpy's all
?
>>> import numpy as np
>>> np.all(xrange(10))
False
>>> np.all(i for i in xrange(10))
True
Numpy.all does not understands generator expressions.
From the documentation
numpy.all(a, axis=None, out=None)
Test whether all array elements along a given axis evaluate to True.
Parameters :
a : array_like
Input array or object that can be converted to an array.
Ok, not very explicit, so lets look at the code
def all(a,axis=None, out=None):
try:
all = a.all
except AttributeError:
return _wrapit(a, 'all', axis, out)
return all(axis, out)
def _wrapit(obj, method, *args, **kwds):
try:
wrap = obj.__array_wrap__
except AttributeError:
wrap = None
result = getattr(asarray(obj),method)(*args, **kwds)
if wrap:
if not isinstance(result, mu.ndarray):
result = asarray(result)
result = wrap(result)
return result
As generator expression doesn't have all
method, it ends up calling _wrapit
In _wrapit
, it first checks for __array_wrap__
method which generates AttributeError
finally ending up calling asarray
on the generator expression
From the documentation of numpy.asarray
numpy.asarray(a, dtype=None, order=None)
Convert the input to an array.
Parameters :
a : array_like
Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays.
It is well documented about the various types of Input data thats accepted which is definitely not generator expression
Finally, trying
>>> np.asarray(0 for i in range(10))
array(<generator object <genexpr> at 0x42740828>, dtype=object)
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