Summary  
This chapter demonstrates how to implement code that computes the mean of a dataset, sums the squared deviations to calculate variance, and then derives the standard deviation by taking the square root of that variance.

General domain of usage  
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

Learn core statistical concepts used in data analysis with Python. The course covers descriptive statistics, including mean, median, mode, variance, and standard deviation, as well as sampling, probability distributions, the Central Limit Theorem, and outlier detection.

Calculating Variance and Standard Deviation

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