ak.var

Defined in awkward.operations.reducers on line 910.

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

  • 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 descreases 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.