Compute pearson product-moment correlation coefficients of two given NumPy arrays

In NumPy, We can compute pearson product-moment correlation coefficients of two given arrays with the help of numpy.corrcoef() function.

In this function, we will pass arrays as a parameter and it will return the pearson product-moment correlation coefficients of two given arrays.

Syntax: numpy.corrcoef(x, y=None, rowvar=True, bias=, ddof=)
Return: Pearson product-moment correlation coefficients

Let’s see an example:

Example 1:

Python

# import library
import numpy as np

# create numpy 1d-array
array1 = np.array([0, 1, 2])
array2 = np.array([3, 4, 5])

# pearson product-moment correlation
# coefficients of the arrays
rslt = np.corrcoef(array1, array2)

print(rslt)

Output

[[1. 1.]
 [1. 1.]]

Example 2:

Python

# import numpy library
import numpy as np

# create a numpy 1d-array
array1 = np.array([ 2, 4, 8])
array2 = np.array([ 3, 2,1])


# pearson product-moment correlation
# coefficients of the arrays
rslt2 = np.corrcoef(array1, array2)

print(rslt2)

Output

[[ 1.         -0.98198051]
 [-0.98198051  1.        ]]