Defined in awkward.operations.ak_mean on line 23.

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

See also ak.nanmean.