ak.max
Defined in awkward.operations.reducers on line 563.
- ak.max(array, axis=None, keepdims=False, initial=None, mask_identity=True)
- Parameters:
array – Data to maximize.
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 negativeaxis
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.
initial (None or number) – The minimum value of an output element, as an alternative to the numeric type’s natural identity (e.g. negative infinity for floating-point types, a minimum integer for integer types). If you use
initial
, you might also wantmask_identity=False
.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.
Returns the maximum value in each group of elements from array
(many
types supported, including all Awkward Arrays and Records). The identity
of maximization is -inf
if floating-point or the smallest integer value
if applied to integers. This identity is usually masked: the maximum of
an empty list is None, unless mask_identity=False
.
This operation is the same as NumPy’s
amax
if all lists at a given dimension have the same length and no None values,
but it generalizes to cases where they do not.
See ak.sum
for a more complete description of nested list and missing
value (None) handling in reducers.