ak.count_nonzero#

Defined in awkward.operations.ak_count_nonzero on line 17.

ak.count_nonzero(array, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=None, attrs=None)#

Parameters:

array – Array-like data (anything ak.to_layout recognizes).
axis (None or int) – If None, combine all values from the array into a single scalar result; if an int, group by that axis: 0 is the outermost, 1 is the first level of nested lists, etc., and negative axis counts from the innermost: -1 is the innermost, -2 is 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.
highlevel (bool) – If True, return an ak.Array; otherwise, return a low-level ak.contents.Content subclass.
behavior (None or dict) – Custom ak.behavior for the output array, if high-level.
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 count_nonzero if all lists at a given dimension have the same length and no None values, but it generalizes to cases where they do not.

See ak.sum for a more complete description of nested list and missing value (None) handling in reducers.

Following the same rules as other reducers, ak.count_nonzero does not count None values. If it is desirable to count them, use ak.fill_none to turn them into something that would be counted.