- ak.min(array, axis=None, *, keepdims=False, initial=None, mask_identity=True, flatten_records=unset, highlevel=True, behavior=None)#
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.
initial (None or number) – The maximum value of an output element, as an alternative to the numeric type’s natural identity (e.g. infinity for floating-point types, a maximum integer for integer types). If you use
initial, you might also want
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 minimum value in each group of elements from
types supported, including all Awkward Arrays and Records). The identity
of minimization is
inf if floating-point or the largest integer value
if applied to integers. This identity is usually masked: the minimum of
an empty list is None, unless
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.
ak.sum for a more complete description of nested list and missing
value (None) handling in reducers.