- ak.count_nonzero(array, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=None, attrs=None)#
array – Array-like data (anything
axis (None or int) – If None, combine all values from the array into a single scalar result; if an int, group by that axis:
0is the outermost,
1is the first level of nested lists, etc., and negative
axiscounts from the innermost:
-1is the innermost,
-2is the next level up, etc.
keepdims (bool) – If False, this reducer decreases the number of dimensions by 1; if True, the reduced values are wrapped in a new length-1 dimension so that the result of this operation may be broadcasted with the original array.
mask_identity (bool) – If True, reducing over empty lists results in None (an option type); otherwise, reducing over empty lists results in the operation’s identity.
attrs (None or dict) – Custom attributes for the output array, if high-level.
Counts nonzero elements of
array (many types supported, including all
Awkward Arrays and Records). The identity of counting is
0 and it is
usually not masked. This operation is the same as NumPy’s
if all lists at a given dimension have the same length and no None values,
but it generalizes to cases where they do not.
ak.sum for a more complete description of nested list and missing
value (None) handling in reducers.