# ak.moment¶

Defined in awkward.operations.reducers on line 837.

ak.moment(x, n, weight=None, axis=None, keepdims=False, mask_identity=True)
Parameters
• x – the data on which to compute the moment.

• 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`.

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.