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MAINT: Fix all special-casing of dtypes in count_nonzero #9849

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Oct 16, 2017
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MAINT: Fix all special-casing of dtypes in count_nonzero #9849

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32 changes: 6 additions & 26 deletions numpy/core/numeric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -411,33 +411,13 @@ def count_nonzero(a, axis=None):

a = asanyarray(a)

if a.dtype == bool:
return a.sum(axis=axis, dtype=np.intp)

if issubdtype(a.dtype, np.number):
return (a != 0).sum(axis=axis, dtype=np.intp)

if issubdtype(a.dtype, np.character):
nullstr = a.dtype.type('')
return (a != nullstr).sum(axis=axis, dtype=np.intp)

axis = asarray(normalize_axis_tuple(axis, a.ndim))
counts = np.apply_along_axis(multiarray.count_nonzero, axis[0], a)

if axis.size == 1:
return counts.astype(np.intp, copy=False)
# TODO: this works around .astype(bool) not working properly (gh-9847)
if np.issubdtype(a.dtype, np.character):
a_bool = a != a.dtype.type()
else:
# for subsequent axis numbers, that number decreases
# by one in this new 'counts' array if it was larger
# than the first axis upon which 'count_nonzero' was
# applied but remains unchanged if that number was
# smaller than that first axis
#
# this trick enables us to perform counts on object-like
# elements across multiple axes very quickly because integer
# addition is very well optimized
return counts.sum(axis=tuple(axis[1:] - (
axis[1:] > axis[0])), dtype=np.intp)
a_bool = a.astype(np.bool_, copy=False)

return a_bool.sum(axis=axis, dtype=np.intp)


def asarray(a, dtype=None, order=None):
Expand Down