- ak.argmin(array, axis=None, *, keepdims=False, 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.
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 index position of the minimum value in each group of elements
array (many types supported, including all Awkward Arrays and
Records). The identity of minimization would be infinity, but argmin
must return the position of the minimum element, which has no value for
empty lists. Therefore, the identity should be masked: the argmin of
an empty list is None. If
mask_identity=False, the result would be
which is distinct from all valid index positions, but care should be taken
that it is not misinterpreted as “the last element of the list.”
This operation is the same as NumPy’s argmin 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.