Summary  
This chapter covers exploratory data analysis techniques in code, including loading and inspecting data structures, computing summary statistics, handling missing values, and printing sample records to identify patterns and issues.

General domain of usage  
Data science

import unittest
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

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 TestDataReading(unittest.TestCase):
    def test_data_loaded(self):
        import user_code

        expected_data = pd.read_csv(
            'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/section_1/confidence.csv',
            index_col=0
        )

        condition = (
            isinstance(user_code.data, pd.DataFrame)
            and user_code.data.shape == expected_data.shape
        )

        _dynamic_test(
            self,
            condition,
            "The CSV file is correctly read into the 'data' variable.",
            "The CSV file is not read correctly into the 'data' variable."
        )


class TestPlot(unittest.TestCase):
    def test_histplot(self):
        import user_code

        # ÐÐµÑÐµÐ²ÑÑÑÑÐ¼Ð¾, ÑÐ¸ ÑÑÐ²Ð¾ÑÐµÐ½Ð¾ Ð³ÑÐ°ÑÑÐº Ð· seaborn.histplot
        plot_obj = user_code.plot

        condition = (
            hasattr(plot_obj, "get_xlabel")
            and plot_obj.get_xlabel() == "The Mass"
            and plot_obj.get_ylabel() == "The Quantity"
        )

        _dynamic_test(
            self,
            condition,
            "The histplot is created with correct parameters.",
            "The histplot parameters are incorrect."
        )


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.

Explore the Data Set

Solution

Awesome!

Explore the Data Set

Solution