Covariance

Covariance is a measure of the joint variability of two random variables.

The value of covariance	Meaning
Positive	Two variables move in the same direction
0	Two variables no linear relationship
Negative	Two variables move in opposite directions

The formulas are different for the sample and population, but we will not dive deeper into them. In this chapter, we will discuss covariances of the following dataset:

	Store_ID	Store_Area	Items_Available	Daily_Customer_Count	Store_Sales
0	0	1659	1961	530	66490
1	1	1461	1752	210	39820
2	2	1340	1609	720	54010
3	3	1451	1748	620	53730
4	4	1770	2111	450	46620

Store_ID - The unique id of the store;
Store_Area - The area of the store;
Items_Available - The number of items that are available in the store;
Daily_Customer_Count - The daily number of customers in the store;
Store_Sales - The number of sales in the store.

Calculating Covariance with Python:

To compute covariance in Python, you can use the np.cov() function from the NumPy library. It requires two parameters: the sequences of data for which you want to calculate the covariance.

The result is the value at index [0,1]. This course won't cover the other values in the output, refer to the example:


              123456789
            
import pandas as pd 
import numpy as np

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/update/Stores.csv')

# Calculating covariance 
cov = np.cov(df['Store_Area'], df['Items_Available'])[0,1]

print(round(cov, 2))

This indicates that the values move in the same direction. This makes sense because a larger store area corresponds to a greater number of items. One significant drawback of covariance is that the value can be infinite.

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Section 4. Chapter 1