numpy.corrcoef¶

numpy.corrcoef(x, y=None, rowvar=1, bias=0, ddof=None)[source]¶

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

$P_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } }$

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

Parameters:

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.

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.