Summary  
This chapter explains how to implement code that computes the mean, variance, and standard deviation of a data set by accumulating sums of squared deviations and applying the appropriate formulas.

General domain of usage  
Statistical data analysis

import unittest
import pandas as pd

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 TestVarianceStd(unittest.TestCase):
    def test_variance_std(self):
        # ÐÐ¼Ð¿Ð¾ÑÑ ÐºÐ¾ÑÐ¸ÑÑÑÐ²Ð°ÑÑÐºÐ¾Ð³Ð¾ ÑÑÑÐµÐ½Ð½Ñ
        import user_code

        # ÐÐ°Ð²Ð°Ð½ÑÐ°Ð¶ÑÑÐ¼Ð¾ ÑÑ ÑÐ°Ð¼Ñ Ð´Ð°Ð½Ñ
        df = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/update/ds_salaries_statistics',
            index_col=0
        )

        # ÐÐ±ÑÐ¸ÑÐ»ÑÑÐ¼Ð¾ Ð¾ÑÑÐºÑÐ²Ð°Ð½Ñ Ð·Ð½Ð°ÑÐµÐ½Ð½Ñ
        expected_var = df['salary'].var()
        expected_std = df['salary'].std()

        # ÐÐµÑÐµÐ²ÑÑÑÑÐ¼Ð¾ Ð· ÑÐ¾ÑÐ½ÑÑÑÑ Ð´Ð¾ 1e-6
        condition = (
            abs(user_code.var - expected_var) < 1e-6 and
            abs(user_code.std - expected_std) < 1e-6
        )

        _dynamic_test(
            self,
            condition,
            "The variance and standard deviation are calculated correctly.",
            "The variance and/or standard deviation are incorrect. Check your calculations."
        )

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.

Calculating Variance and Standard Deviation

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