- ak.min(array, axis=None, *, keepdims=False, initial=None, mask_identity=True, highlevel=True, behavior=None, attrs=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.
attrs (None or dict) – Custom attributes for the output array, if high-level.
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.