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# numpy.corrcoef

`numpy.``corrcoef`(x, y=None, rowvar=1, bias=0, ddof=None)

  Return correlation coefficients.

  Please refer to the documentation for `cov` for more detail. The relationship between the correlation coefficient matrix, P, and the covariance matrix, C, is

  The values of P are between -1 and 1, inclusive.

  Parameters : | x : array_like A 1-D or 2-D array containing multiple variables and observations. Each row of m 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 m. rowvar : int, optional If rowvar is non-zero (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 : int, optional Default normalization is by `(N - 1)`, where `N` is the number of observations (unbiased estimate). If bias is 1, then normalization is by `N`. These values can be overridden by using the keyword `ddof` in numpy versions >= 1.5. 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`.
  Returns : | out : ndarray The correlation coefficient matrix of the variables.

  See also

  `cov`
    Covariance matrix