ak.moment --------- .. py:module: ak.moment Defined in `awkward.operations.ak_moment `__ on `line 21 `__. .. py:function:: ak.moment(x, n, weight=None, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=None, attrs=None) :param x: The data on which to compute the moment (anything :py:obj:`ak.to_layout` recognizes). :param n: The choice of moment: ``0`` is a sum of weights, ``1`` is :py:obj:`ak.mean`, ``2`` is :py:obj:`ak.var` without subtracting the mean, etc. :type n: int :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 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. :type axis: None or int :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 ``n``th moment 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. This function has no NumPy equivalent. Passing all arguments to the reducers, the moment is calculated as .. code-block:: python ak.sum((x*weight)**n) / ak.sum(weight) The ``n=2`` moment differs from :py:obj:`ak.var` in that :py:obj:`ak.var` also subtracts the mean (the ``n=1`` moment). 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.