ak._v2.ak_max.max

Defined in awkward._v2.operations.reducers.ak_max on line 9.

ak._v2.ak_max.max(array, axis=None, keepdims=False, initial=None, mask_identity=True, flatten_records=False)
Parameters

  • array – Array-like data (anything ak.to_layout recognizes).

  • 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 minimum value of an output element, as an alternative to the numeric type’s natural identity (e.g. negative infinity for floating-point types, a minimum 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.

  • flatten_records (bool) – If True, axis=None combines fields from different records; otherwise, records raise an error.

Returns the maximum value in each group of elements from array (many types supported, including all Awkward Arrays and Records). The identity of maximization is -inf if floating-point or the smallest integer value if applied to integers. This identity is usually masked: the maximum of an empty list is None, unless mask_identity=False. This operation is the same as NumPy’s amax 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.