import unittest
import psycopg2
import pandas as pd

class TestPostgresQuery(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.connection = psycopg2.connect(
            host="localhost",
            user="agent",
            password="qwerty",
            dbname="testdb",
            sslmode="disable"
        )
        cls.cursor = cls.connection.cursor()

    def test_all_columns_and_data_status_present(self):
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        expected_columns = ['id', 'name', 'department', 'salary', 'years_at_company', 'data_status']
        condition = all(col in columns for col in expected_columns)
        _dynamic_test(
            self,
            condition,
            "All required columns including data_status are present.",
            f"Missing columns: {set(expected_columns) - set(columns)}"
        )

    def test_data_status_complete(self):
        # Insert a row with all columns NOT NULL
        self.cursor.execute("INSERT INTO employees (id, name, department, salary, years_at_company) VALUES (200, 'Alice', 'HR', 52000, 2)")
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        df = pd.DataFrame(rows, columns=columns)
        row = df[df['id'] == 200].iloc[0]
        condition = row['data_status'] == 'Complete'
        _dynamic_test(
            self,
            condition,
            "data_status is 'Complete' when all fields are NOT NULL.",
            "data_status is not 'Complete' when all fields are NOT NULL."
        )

    def test_data_status_incomplete(self):
        # Insert a row with a NULL value
        self.cursor.execute("INSERT INTO employees (id, name, department, salary, years_at_company) VALUES (100, NULL, 'Engineering', 90000.00, 3)")
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        df = pd.DataFrame(rows, columns=columns)
        row = df[df['id'] == 100].iloc[0]
        condition = row['data_status'] == 'Incomplete'
        _dynamic_test(
            self,
            condition,
            "data_status is 'Incomplete' when a field is NULL.",
            "data_status is not 'Incomplete' when a field is NULL."
        )

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

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

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


test_solution.py

SQLにおけるCASE WHEN文を基礎的な条件分岐からデータ分析・レポーティングでの高度な活用例まで習得できる包括的なモジュールです。理論と実践的な課題を通じて、複雑なビジネス要件に対応する表現力豊かなクエリの作成方法を学びます。

CASE WHEN文の基本、構文、およびSQLクエリでの単純な条件付きロジックへの活用方法を学習します。

チャレンジ：従業員データの欠損値を処理する

解答