ak.min ------ .. py:module: ak.min Defined in `awkward.operations.ak_min `__ on `line 18 `__. .. py:function:: ak.min(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 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``. :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 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 :py:obj:`ak.sum` for a more complete description of nested list and missing value (None) handling in reducers. See also :py:obj:`ak.nanmin`.