Course Content
Learning Statistics with Python
Learning Statistics with Python
Covariance
Covariance is a measure of the joint variability of two random variables.
The formulas for sample and population covariance differ, but they won't be explored in detail here. This chapter focuses on the covariances of the following dataset:
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, use the np.cov() function from the NumPy library. It takes two parameters: the data sequences 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:
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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