ak.min

Defined in awkward.operations.reducers on line 501.

ak.min(array, axis=None, keepdims=False, initial=None, mask_identity=True)
Parameters

  • array – Data to minimize.

  • 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 negative axis counts from the innermost: -1 is the innermost, -2 is the next level up, etc.

  • keepdims (bool) – If False, this reducer descreases 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=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 minimum value in each group of elements from array (many 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 mask_identity=False. This operation is the same as NumPy’s amin 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.