ak.count#

Defined in awkward.operations.ak_count on line 23.

ak.count(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 or str) – 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; if a str, it is interpreted as the name of the axis which maps to an int if named axes are present. Named axes are attached to an array using ak.with_named_axis and removed with ak.without_named_axis; also see the Named axes user guide.

  • 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 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.

See 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: ak.num never counts across different lists the way that reducers do (ak.num is not a reducer; ak.count is). For the same array,

>>> 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 ak.count, use ak.fill_none to turn the None values into something that would be counted.

If it is desirable to exclude NaN (“not a number”) values from ak.count, use ak.nan_to_none to turn them into None, which are not counted.