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# numpy.ma.cov

`numpy.ma.``cov`(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None)

  Estimate the covariance matrix.

  Except for the handling of missing data this function does the same as `numpy.cov`. For more details and examples, see `numpy.cov`.

  By default, masked values are recognized as such. If x and y have the same shape, a common mask is allocated: if `x[i,j]` is masked, then `y[i,j]` will also be masked. Setting allow_masked to False will raise an exception if values are missing in either of the input arrays.

  Parameters : | x : array_like A 1-D or 2-D array containing multiple variables and observations. Each row of x represents a variable, and each column a single observation of all those variables. Also see rowvar below. y : array_like, optional An additional set of variables and observations. y has the same form as x. rowvar : bool, optional If rowvar is True (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations. bias : bool, optional Default normalization (False) is by `(N-1)`, where `N` is the number of 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 New in version 1.5. 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`

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