Covariance in Python NumPy

This tutorial will introduce the method to calculate the covariance between two NumPy arrays in Python.

Covariance With the numpy.cov() Function

In statistics, covariance is the measure of change in one variable with the change in the other variable. Covariance tells us how much one variable changes if another variable is changed. We can calculate the covariance between two NumPy arrays with the numpy.cov(a1, a2) function in Python.

Here, a1 represents a collection of values of the first variable, and a2 represents a collection of values of the second variable. The numpy.cov() function returns a 2D array in which the value at index [0][0] is the covariance between a1 and a1, the value at index [0][1] is the covariance between a1 and a2, the value at index [1][0] is the covariance between a2 and a1, and the value at index [1][1] is the covariance between a2 and a2. See the following code example.

import numpy as np

array1 = np.array([1, 2, 3])
array2 = np.array([2, 4, 5])

covariance = np.cov(array1, array2)[0][1]
print(covariance)

Output:

1.5

We first created the two NumPy arrays array1 and array2 with the np.array() function. Then we calculated the covariance with the np.cov(array1, array2)[0][1] and saved the result in the covariance variable. In the end, we printed the value inside the covariance variable.