#### Previous topic

numpy.ma.conjugate

#### Next topic

numpy.ma.cov

# numpy.ma.corrcoef

`numpy.ma.``corrcoef`(x, y=None, rowvar=True, bias=<class numpy._globals._NoValue>, allow_masked=True, ddof=<class numpy._globals._NoValue>)

  Return Pearson product-moment correlation coefficients.

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

  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 shape 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 : _NoValue, optional Has no effect, do not use. Deprecated since version 1.10.0. 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 an exception. Because bias is deprecated, this argument needs to be treated as keyword only to avoid a warning. ddof : _NoValue, optional Has no effect, do not use. Deprecated since version 1.10.0.

  See also

  `numpy.corrcoef`
    Equivalent function in top-level NumPy module.
  `cov`
    Estimate the covariance matrix.

  Notes

  This function accepts but discards arguments bias and ddof. This is for backwards compatibility with previous versions of this function. These arguments had no effect on the return values of the function and can be safely ignored in this and previous versions of numpy.