ak.mean#

Defined in awkward.operations.ak_mean on line 29.

ak.mean(x, weight=None, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=None, attrs=None)#

Parameters:

x – The data on which to compute the mean (anything ak.to_layout recognizes).
weight – Data that can be broadcasted to x to give each value a weight. Weighting values equally is the same as no weights; weighting some values higher increases the significance of those values. Weights can be zero or negative.
axis (None or int or str) – 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; if a str, it is interpreted as the name of the axis which maps to an int if named axes are present. Named axes are attached to an array using ak.with_named_axis and removed with ak.without_named_axis; also see the Named axes user guide.
keepdims (bool) – If False, this function decreases the number of dimensions by 1; if True, the output values are wrapped in a new length-1 dimension so that the result of this operation may be broadcasted with the original array.
mask_identity (bool) – If True, the application of this function on empty lists results in None (an option type); otherwise, the calculation is followed through with the reducers’ identities, usually resulting in floating-point nan.
highlevel (bool) – If True, return an ak.Array; otherwise, return a low-level ak.contents.Content subclass.
behavior (None or dict) – Custom ak.behavior for the output array, if high-level.
attrs (None or dict) – Custom attributes for the output array, if high-level.

Computes the mean in each group of elements from x (many types supported, including all Awkward Arrays and Records). The grouping is performed the same way as for reducers, though this operation is not a reducer and has no identity. It is the same as NumPy’s mean if all lists at a given dimension have the same length and no None values, but it generalizes to cases where they do not.

Passing all arguments to the reducers, the mean is calculated as:

ak.sum(x*weight) / ak.sum(weight)

For example, with an array like

>>> array = ak.Array([[0, 1, 2, 3],
                      [          ],
                      [4, 5      ]])

The mean of the innermost lists is

>>> ak.mean(array, axis=-1)
<Array [1.5, nan, 4.5] type='3 * float64'>

because there are three lists, the first has mean 1.5, the second is empty, and the third has mean 4.5.

The mean of the outermost lists is

>>> ak.mean(array, axis=0)
<Array [2, 3, 2, 3] type='4 * float64'>

because the longest list has length 4, the mean of 0 and 4 is 2.0, the mean of 1 and 5 is 3.0, the mean of 2 (by itself) is 2.0, and the mean of 3 (by itself) is 3.0. This follows the same grouping behavior as reducers.

See ak.sum for a complete description of handling nested lists and missing values (None) in reducers.