Return Pearson product-moment correlation coefficients.

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

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

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.

An additional set of variables and observations. y has the same shape as x.

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.

Has no effect, do not use.

Deprecated since version 1.10.0.

Has no effect, do not use.

Deprecated since version 1.10.0.

Data-type of the result. By default, the return data-type will have at least numpy.float64 precision.

New in version 1.20.

The correlation coefficient matrix of the variables.

See also

Covariance matrix

Notes

Due to floating point rounding the resulting array may not be Hermitian, the diagonal elements may not be 1, and the elements may not satisfy the inequality abs(a) >> import numpy as np >>> rng = np.random.default_rng(seed=42) >>> xarr = rng.random((3, 3)) >>> xarr array([[0.77395605, 0.43887844, 0.85859792], [0.69736803, 0.09417735, 0.97562235], [0.7611397 , 0.78606431, 0.12811363]]) >>> R1 = np.corrcoef(xarr) >>> R1 array([[ 1. , 0.99256089, -0.68080986], [ 0.99256089, 1. , -0.76492172], [-0.68080986, -0.76492172, 1. ]])

If we add another set of variables and observations yarr, we can compute the row-wise Pearson correlation coefficients between the variables in xarr and yarr.

Finally if we use the option rowvar=False, the columns are now being treated as the variables and we will find the column-wise Pearson correlation coefficients between variables in xarr and yarr.