ak.nanvar
---------
.. py:module: ak.nanvar
Defined in `awkward.operations.ak_var `__ on `line 97 `__.
.. py:function:: ak.nanvar(x, weight=None, ddof=0, axis=None, *, keepdims=False, mask_identity=True, flatten_records=unset)
: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.
: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
Like :py:obj:`ak.var`, but treating NaN ("not a number") values as missing.
Equivalent to
.. code-block:: python
ak.var(ak.nan_to_none(array))
with all other arguments unchanged.
See also :py:obj:`ak.var`.