ak.covar
--------
.. py:module: ak.covar
Defined in `awkward.operations.ak_covar `__ on `line 21 `__.
.. py:function:: ak.covar(x, y, weight=None, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=None, attrs=None)
:param x: One coordinate to use in the covariance calculation (anything :py:obj:`ak.to_layout` recognizes).
:param y: The other coordinate to use in the covariance calculation (anything :py:obj:`ak.to_layout` recognizes).
:param weight: Data that can be broadcasted to ``x`` and ``y`` to give each point
a weight. Weighting points equally is the same as no weights;
weighting some points higher increases the significance of those
points. 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 covariance of ``x`` and ``y`` (many types supported, including
all Awkward Arrays and Records, must be broadcastable to each other).
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 covariance is calculated as
.. code-block:: python
ak.sum((x - ak.mean(x))*(y - ak.mean(y))*weight) / ak.sum(weight)
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.