ak._v2.ak_moment.moment¶

Defined in awkward._v2.operations.reducers.ak_moment on line 9.

ak._v2.ak_moment.moment(x, n, weight=None, axis=None, keepdims=False, mask_identity=True, flatten_records=False)
Parameters
• x – The data on which to compute the moment (anything ak.to_layout recognizes).

• n (int) – The choice of moment: 0 is a sum of weights, 1 is ak.mean, 2 is ak.var without subtracting the mean, etc.

• 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.

• flatten_records (bool) – If True, axis=None combines fields from different records; otherwise, records raise an error.

Computes the 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)

The n=2 moment differs from ak.var in that ak.var also subtracts the mean (the n=1 moment).

See ak.sum for a complete description of handling nested lists and missing values (None) in reducers, and ak.mean for an example with another non-reducer.