# ak.var#

Defined in awkward.operations.ak_var on line 13.

ak.var(x, weight=None, ddof=0, axis=None, *, keepdims=False, mask_identity=False)#
Parameters
• x – The data on which to compute the variance (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.

• ddof (int) – “delta degrees of freedom”: the divisor used in the calculation is `sum(weights) - ddof`. Use this for “reduced variance.”

• 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 variance 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 var 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 variance is calculated as

```ak.sum((x - ak.mean(x))**2 * weight) / ak.sum(weight)
```

If `ddof` is not zero, the above is further corrected by a factor of

```ak.sum(weight) / (ak.sum(weight) - ddof)
```

Even without `ddof`, `ak.var` differs from `ak.moment` with `n=2` because the mean is subtracted from all points before summing their squares.

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.

See also `ak.nanvar`.