Defined in awkward.operations.reducers on line 562.

ak.max(array, axis=None, keepdims=False, initial=None, mask_identity=True)
  • array – Data to maximize.

  • 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.

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.