- ak._v2.ak_moment.moment(x, n, weight=None, axis=None, keepdims=False, mask_identity=True, flatten_records=False)¶
x – The data on which to compute the moment (anything
weight – Data that can be broadcasted to
xto 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:
0is the outermost,
1is the first level of nested lists, etc., and negative
axiscounts from the innermost:
-1is the innermost,
-2is 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
flatten_records (bool) – If True, axis=None combines fields from different records; otherwise, records raise an error.
n``th moment 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.
This function has no NumPy equivalent.
Passing all arguments to the reducers, the moment is calculated as
ak.sum((x*weight)**n) / ak.sum(weight)