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# numpy.ma.corrcoef

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

  The correlation coefficients formed from the array x, where the rows are the observations, and the columns are variables.

  corrcoef(x,y) where x and y are 1d arrays is the same as corrcoef(transpose([x,y]))

  Parameters: | x : ndarray Input data. If x is a 1D array, returns the variance. If x is a 2D array, returns the covariance matrix. y : {None, ndarray} optional Optional set of variables. rowvar : {False, True} optional If True, then each row is a variable with observations in columns. If 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 True, then the normalization is by N, the number of non-missing 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 an exception.

  See also

  `cov`

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