observations given (unbiased estimate). If `bias` is True,
    then normalization is by ``N``. This keyword can be overridden by
    the keyword ``ddof`` in numpy versions >= 1.5.
allow_masked : bool, optional
    If True, masked values are propagated pair-wise: if a value is masked
    in `x`, the corresponding value is masked in `y`.
    If False, raises a `ValueError` exception when some values are missing.
ddof : {None, int}, optional
    If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is
    the number of observations; this overrides the value implied by
    ``bias``. The default value is ``None``.

Raises
------
ValueError
    Raised if some values are missing and `allow_masked` is False.

See Also
--------
numpy.cov

Examples
--------
>>> import numpy as np
>>> x = np.ma.array([[0, 1], [1, 1]], mask=[0, 1, 0, 1])
>>> y = np.ma.array([[1, 0], [0, 1]], mask=[0, 0, 1, 1])
>>> np.ma.cov(x, y)
masked_array(
data=[[--, --, --, --],
      [--, --, --, --],
      [--, --, --, --],
      [--, --, --, --]],
mask=[[ True,  True,  True,  True],
      [ True,  True,  True,  True],
      [ True,  True,  True,  True],
      [ True,  True,  True,  True]],
fill_value=1e+20,
dtype=float64)

Nz