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# numpy.ma.cov

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

  Estimates the covariance matrix.

  Normalization is by (N-1) where N is the number of observations (unbiased estimate). If bias is True then normalization is by N.

  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 Input data. If x is a 1D array, returns the variance. If x is a 2D array, returns the covariance matrix. y : array_like, optional Optional set of variables. rowvar : {False, True} optional If rowvar is true, then each row is a variable with observations in columns. If rowvar is False, each column is a variable and the observations are in the rows. bias : {False, True} optional Whether to use a biased (True) or unbiased (False) estimate of the covariance. If bias is True, then the normalization is by N, the number of observations. Otherwise, the normalization is by (N-1). allow_masked : {True, False} 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.
  Raises: | ValueError: : Raised if some values are missing and allow_masked is False.

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