ak.ptp#

Defined in awkward.operations.ak_ptp on line 22.

ak.ptp(array, axis=None, *, keepdims=False, mask_identity=True, 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 of 0.

Returns the range of values in each group of elements from array (many types supported, including all Awkward Arrays and Records). The range of an empty list is None, unless mask_identity=False, in which case it is 0. This operation is the same as NumPy’s ptp if all lists at a given dimension have the same length and no None values, but it generalizes to cases where they do not.

For example, with

>>> array = ak.Array([[0, 1, 2, 3],
...                   [          ],
...                   [4, 5      ]])

The range of the innermost lists is

>>> ak.ptp(array, axis=-1)
<Array [3, None, 1] type='3 * ?int64'>

because there are three lists, the first has a range of 3, the second is None because the list is empty, and the third has a range of 1. Similarly,

>>> ak.ptp(array, axis=-1, mask_identity=False)
<Array [3, 0, 1] type='3 * float64'>

The second value is 0 because the list is empty.

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