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_customers_returned(self):
        """Check that all customer rows are returned."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        condition = len(rows) == 10
        _dynamic_test(
            self,
            condition,
            "All customers are returned.",
            f"Expected 10 rows, got {len(rows)}."
        )

    def test_domain_column_correct(self):
        """Check that the domain column contains only the part after '@' for each row."""
        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]
        result_df = pd.DataFrame(rows, columns=columns)
        domains_expected = ["example.com"] * 10
        condition = result_df['domain'].tolist() == domains_expected
        _dynamic_test(
            self,
            condition,
            "Domain column contains correct values.",
            f"Domain column values are incorrect: {result_df['domain'].tolist()}"
        )

    def test_id_name_email_columns_match(self):
        """Check that id, name, and email columns match input data."""
        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]
        result_df = pd.DataFrame(rows, columns=columns)
        expected_data = [
            (1, 'Alice Johnson', 'alice.johnson@example.com'),
            (2, 'Rajesh Kumar', 'rajesh.kumar@example.com'),
            (3, 'Maria Garcia', 'maria.garcia@example.com'),
            (4, 'Chen Wei', 'chen.wei@example.com'),
            (5, 'Fatima Al-Farsi', 'fatima.alfarsi@example.com'),
            (6, 'John Smith', 'john.smith@example.com'),
            (7, 'Sofia Rossi', 'sofia.rossi@example.com'),
            (8, 'Lucas MÃ¼ller', 'lucas.mueller@example.com'),
            (9, 'Emily Brown', 'emily.brown@example.com'),
            (10, 'Takashi Sato', 'takashi.sato@example.com'),
        ]
        actual_data = list(zip(result_df['id'], result_df['name'], result_df['email']))
        condition = actual_data == expected_data
        _dynamic_test(
            self,
            condition,
            "id, name, and email columns match input data.",
            f"Returned id, name, and email columns do not match input data."
        )

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

組み込みSQL関数を活用して、文字列、日付、数値、論理値、NULL、JSONデータを操作する方法を習得します。本モジュールでは、実践的なアプローチでSQL関数の理解と応用を深め、現実のシナリオにおけるデータクエリおよび変換スキルを向上させます。

データベース内のテキストデータを操作および分析するための、SQLの強力な文字列関数を学習します。

チャレンジ：メールドメインの抽出

解答