ak.min — Awkward Array documentation

# ak.min

Defined in awkward.operations.reducers on line 502.

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 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=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.