Compute the covariance matrix of two given NumPy arrays
Last Updated :
15 Jul, 2025
Improve
In NumPy for computing the covariance matrix of two given arrays with help of numpy.cov(). In this, we will pass the two arrays and it will return the covariance matrix of two given arrays.
Syntax: numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None)
Example 1:
import numpy as np
array1 = np.array([0, 1, 1])
array2 = np.array([2, 2, 1])
# Original array1
print(array1)
# Original array2
print(array2)
# Covariance matrix
print("\nCovariance matrix of the said arrays:\n",
np.cov(array1, array2))
Output:
[0 1 1] [2 2 1] Covariance matrix of the said arrays: [[ 0.33333333 -0.16666667] [-0.16666667 0.33333333]]
Example 2:
import numpy as np
array1 = np.array([2, 1, 1, 4])
array2 = np.array([2, 2, 1, 1])
# Original array1
print(array1)
# Original array2
print(array2)
# Covariance matrix
print("\nCovariance matrix of the said arrays:\n",
np.cov(array1, array2))
Output:
[2 1 1 4] [2 2 1 1] Covariance matrix of the said arrays: [[ 2. -0.33333333] [-0.33333333 0.33333333]]
Example 3:
import numpy as np
array1 = np.array([1, 2])
array2 = np.array([1, 2])
# Original array1
print(array1)
# Original array2
print(array2)
# Covariance matrix
print("\nCovariance matrix of the said arrays:\n",
np.cov(array1, array2))
Output
[1 2] [1 2] Covariance matrix of the said arrays: [[0.5 0.5] [0.5 0.5]]
Example 4:
import numpy as np
x = [1.23, 2.12, 3.34, 4.5]
y = [2.56, 2.89, 3.76, 3.95]
# find out covariance with respect
# rows
cov_mat = np.stack((x, y), axis = 1)
print("shape of matrix x and y:",
np.shape(cov_mat))
print("shape of covariance matrix:",
np.shape(np.cov(cov_mat)))
print(np.cov(cov_mat))
Output
shape of matrix x and y: (4, 2) shape of covariance matrix: (4, 4) [[ 0.88445 0.51205 0.2793 -0.36575] [ 0.51205 0.29645 0.1617 -0.21175] [ 0.2793 0.1617 0.0882 -0.1155 ] [-0.36575 -0.21175 -0.1155 0.15125]]
Article Tags :
