- ak._v2.ak_var.var(x, weight=None, ddof=0, axis=None, keepdims=False, mask_identity=True, flatten_records=False)¶
x – The data on which to compute the variance (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.
ddof (int) – “delta degrees of freedom”: the divisor used in the calculation is
sum(weights) - ddof. Use this for “reduced variance.”
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
Computes the variance in each group of elements from
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
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)
ddof is not zero, the above is further corrected by a factor of
ak.sum(weight) / (ak.sum(weight) - ddof)