ak.count — Awkward Array documentation

# ak.count

Defined in awkward.operations.reducers on line 10.

ak.count(array, axis=None, keepdims=False, mask_identity=False)
  Parameters:

    - array – Data in which to count elements.

    - 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.

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.