Summary  
This chapter explains how to implement code that calculates covariance and correlation coefficients to quantify the linear relationship between numerical data sets.  

General domain of usage  
Data analysis

import unittest
import pandas as pd
import numpy as np

def _dynamic_test(test_case, condition, success_msg, failure_msg):
    if condition:
        test_case._testMethodName = success_msg
        test_case.assertTrue(True, success_msg)
    else:
        test_case._testMethodName = failure_msg
        test_case.fail(failure_msg)


class TestCovariance(unittest.TestCase):
    def test_covariance(self):
        import user_code

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

        expected_cov = np.cov(df['Daily_Customer_Count'], df['Items_Available'])[0, 1]

        condition = abs(user_code.cov - expected_cov) < 1e-6

        _dynamic_test(
            self,
            condition,
            "The covariance is calculated correctly.",
            "The covariance is incorrect. Check your calculation."
        )


class TestCorrelation(unittest.TestCase):
    def test_correlation(self):
        import user_code

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

        expected_corr = np.corrcoef(df['Daily_Customer_Count'], df['Items_Available'])[0, 1]

        condition = abs(user_code.corr - expected_corr) < 1e-6

        _dynamic_test(
            self,
            condition,
            "The correlation is calculated correctly.",
            "The correlation is incorrect. Check your calculation."
        )


if __name__ == "__main__":
    unittest.main()


test_code.py

Build a strong foundation in statistics using Python. Learn essential statistical concepts and apply them through NumPy and pandas. Move from basic measures like mean and variance to hypothesis testing, confidence intervals, and data-driven insights with hands-on practice.

Discover core statistical principles, including data types, measures of central tendency, and key differences between samples and populations.

Learn to calculate and interpret mean, median, and mode using Python. Practice these operations with pandas to analyze real datasets.

Understand how variance and standard deviation measure data spread. Learn to compute both manually and using Python tools.

Explore how covariance and correlation describe relationships between variables. Practice calculating and comparing both metrics in Python.

Master confidence intervals to estimate population parameters. Use NumPy, pandas, and visualization libraries to calculate and interpret intervals with real data.

Learn the fundamentals of hypothesis testing and the t-test. Understand how to design, perform, and interpret tests using Python to support data-driven decisions.

Calculate Covariance and Correlation

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