ak.var#

Defined in awkward.operations.ak_var on line 29.

ak.var(x, weight=None, ddof=0, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=None, attrs=None)#

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 or str) – 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; if a str, it is interpreted as the name of the axis which maps to an int if named axes are present. Named axes are attached to an array using ak.with_named_axis and removed with ak.without_named_axis; also see the Named axes user guide.
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.
highlevel (bool) – If True, return an ak.Array; otherwise, return a low-level ak.contents.Content subclass.
behavior (None or dict) – Custom ak.behavior for the output array, if high-level.
attrs (None or dict) – Custom attributes for the output array, if high-level.

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.