ak.max
------
.. py:module: ak.max
Defined in `awkward.operations.ak_max `__ on `line 18 `__.
.. py:function:: ak.max(array, axis=None, *, keepdims=False, initial=None, mask_identity=True, highlevel=True, behavior=None, attrs=None)
:param array: Array-like data (anything :py:obj:`ak.to_layout` recognizes).
:param axis: 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.
:type axis: None or int
:param keepdims: 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.
:type keepdims: bool
:param initial: 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``.
:type initial: None or number
:param mask_identity: If True, reducing over empty lists results in
None (an option type); otherwise, reducing over empty lists
results in the operation's identity.
:type mask_identity: bool
:param highlevel: If True, return an :py:obj:`ak.Array`; otherwise, return
a low-level :py:obj:`ak.contents.Content` subclass.
:type highlevel: bool
:param behavior: Custom :py:obj:`ak.behavior` for the output array, if
high-level.
:type behavior: None or dict
:param attrs: Custom attributes for the output array, if
high-level.
:type attrs: None or dict
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 :py:obj:`ak.sum` for a more complete description of nested list and missing
value (None) handling in reducers.
See also :py:obj:`ak.nanmax`.