- ak.ptp(array, axis=None, *, keepdims=False, mask_identity=True, flatten_records=unset)#
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 of 0.
Returns the range of values in each group of elements from
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
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
>>> 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.
ak.sum for a more complete description of nested list and missing
value (None) handling in reducers.