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Showing content from https://www.tutorialspoint.com/python/python_statistics_covariance_function.htm below:

Python statistics.covariance() Function

The Python statistics.covariance() function is a measure of the relationship between two random variables. The covariance indicates how two variables are related and also helps to know whether the two variables vary together or differ from each other values.

A positive covariance value determines that different variables movie in same direction. Similarly, if one random variable decreases, the other variable also decreases.

A negative covariance values determines the variables move in opposite direction. Similarly, if one random variable decreases, the other variable increases.

When the two random variables are independent of each other, the covariance between them is zero.

Following is the basic syntax for the statistics.covariance() function.

statistics.covariance(x, y, /)

This function contains sequence of elements like list, tuple, iterator or any array with the same length as an input.

This returns the sample covariance of two input points x and y.

In the below example we are finding covariance for the given values using statistics.covariance() Function.

import statistics
a = [1, 2, 3, 4, 5, 6, 7]
b = [9, 8, 7, 6, 5, 4, 3]
c = statistics.covariance(a, b)
print("Sample covariance between a and b:", c)

The result is produced as follows −

Sample covariance between a and b: -4.666666666666667

Now, we are importing statistics in the given code using statistics.covariance() function.

from statistics import covariance
a = [3, 4, 5, 6, 7, 8]
b = [2, 4, 6, 7, 8, 9]
c = covariance(a,b)
print(c)

This produces the following result −

4.8

Here, we are calculating the numpy covariance using statistics.covariance() function.

import numpy as np 
n = [0.34, 4.54, 3.22, 2.12, 0.12]
p = [1.23, 3.45, 2.2, 1.2, 0.3]
w = np.cov(n,p)
print(w)

We will get the output as follows −

[[3.55532 2.10384]
 [2.10384 1.43513]]

In the below example we are calculating the covariance using statistics.covariance() function.

import statistics
s = [2, 3, 4, 5, 6]
f = [1, 2, 3, 4, 5]
g = statistics.covariance(s, f)
print(g)

The output obtained is as follows −

2.5

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