Defined in awkward.operations.ak_count on line 12.
- ak.count(array, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=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.
highlevel (bool) – If True, return an
ak.Array; otherwise, return a low-level
behavior (None or dict) – Custom
ak.behaviorfor the output array, if high-level.
Counts 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 function has no analog in NumPy because counting values in a rectilinear array would only result in elements of the NumPy array’s shape.
However, for nested lists of variable dimension and missing values, the result of counting is non-trivial. For example, with this
>>> array = ak.Array([[ 0.1, 0.2 ], ... [None, 10.2, None], ... None, ... [20.1, 20.2, 20.3], ... [30.1, 30.2 ]])
the result of counting over the innermost dimension is
>>> ak.count(array, axis=-1) <Array [2, 1, None, 3, 2] type='5 * ?int64'>
the outermost dimension is
>>> ak.count(array, axis=0) <Array [3, 4, 1] type='3 * int64'>
and all dimensions is
>>> ak.count(array, axis=None) 8
The gaps and None values are not counted, and if a None value occurs at a higher axis than the one being counted, it is kept as a placeholder so that the outer list length does not change.
ak.sum for a more complete description of nested list and missing
value (None) handling in reducers.
Note also that this function is different from
ak.num, which counts
the number of values at a given depth, maintaining structure:
never counts across different lists the way that reducers do (
is not a reducer;
ak.count is). For the same
>>> ak.num(array, axis=0) 5 >>> ak.num(array, axis=1) <Array [2, 3, None, 3, 2] type='5 * ?int64'>
If it is desirable to include None values in
to turn the None values into something that would be counted.
If it is desirable to exclude NaN (“not a number”) values from
ak.nan_to_none to turn them into None, which are not counted.