ak.var ====== Defined in `awkward.operations.ak_var `__ on `line 29 `__. .. py:function:: ak.var(x, weight=None, ddof=0, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=None, attrs=None) :param x: The data on which to compute the variance (anything :py:obj:`ak.to_layout` recognizes). :param 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. :param ddof: "delta degrees of freedom": the divisor used in the calculation is ``sum(weights) - ddof``. Use this for "reduced variance." :type ddof: int :param axis: 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 :py:obj:`ak.with_named_axis` and removed with :py:obj:`ak.without_named_axis`; also see the `Named axes user guide <../../user-guide/how-to-array-properties-named-axis.html>`__. :type axis: None or int or str :param keepdims: 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. :type keepdims: bool :param mask_identity: 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``. :type mask_identity: bool :param highlevel: If True, return an :py:obj:`ak.Array`; otherwise, return a low-level :py:obj:`ak.contents.Content` subclass. :type highlevel: bool :param behavior: Custom :py:obj:`ak.behavior` for the output array, if high-level. :type behavior: None or dict :param attrs: Custom attributes for the output array, if high-level. :type attrs: None or dict 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``, :py:obj:`ak.var` differs from :py:obj:`ak.moment` with ``n=2`` because the mean is subtracted from all points before summing their squares. See :py:obj:`ak.sum` for a complete description of handling nested lists and missing values (None) in reducers, and :py:obj:`ak.mean` for an example with another non-reducer. See also :py:obj:`ak.nanvar`.