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_above_average_customers_rows(self):
        """Check that only above-average customers are returned with expected fields."""
        with open("query.sql", "r") as file:
            query = file.read()

        # Calculate expected average total quantity per customer
        self.cursor.execute("""
            SELECT customer_id, SUM(quantity) AS total_quantity
            FROM sales
            GROUP BY customer_id
        """)
        rows = self.cursor.fetchall()
        customer_totals = [row[1] for row in rows]
        avg_total = sum(customer_totals) / len(customer_totals)
        above_avg_customers = [row[0] for row in rows if row[1] > avg_total]

        # Get expected result
        if above_avg_customers:
            format_ids = ','.join(str(i) for i in above_avg_customers)
            expected_query = f"""
                SELECT c.customer_id, c.first_name, c.last_name, SUM(s.quantity) AS total_quantity
                FROM customers c
                JOIN sales s ON c.customer_id = s.customer_id
                WHERE c.customer_id IN ({format_ids})
                GROUP BY c.customer_id, c.first_name, c.last_name
            """
            self.cursor.execute(expected_query)
            expected_rows = self.cursor.fetchall()
            expected_columns = [desc[0] for desc in self.cursor.description]
            expected_df = pd.DataFrame(expected_rows, columns=expected_columns)
        else:
            expected_df = pd.DataFrame(columns=["customer_id", "first_name", "last_name", "total_quantity"])

        # Run user query
        self.cursor.execute(query)
        result_rows = self.cursor.fetchall()
        result_columns = [desc[0] for desc in self.cursor.description]
        result_df = pd.DataFrame(result_rows, columns=result_columns)

        # Check columns
        required_cols = ["customer_id", "first_name", "last_name", "total_quantity"]
        cond_cols = set(required_cols).issubset(set(result_df.columns))
        _dynamic_test(self, cond_cols,
            "All required columns are present in the result.",
            f"Missing one or more required columns: {set(required_cols) - set(result_df.columns)}")

        # Check expected rows are present
        missing_rows = []
        for _, exp_row in expected_df.iterrows():
            mask = (result_df[required_cols] == exp_row[required_cols]).all(axis=1)
            if not mask.any():
                missing_rows.append(exp_row[required_cols].to_dict())
        cond_rows = len(missing_rows) == 0
        fail_msg = f"The following expected rows are missing: {missing_rows}" if not cond_rows else ""
        _dynamic_test(self, cond_rows,
            "All above-average customers are present in result.",
            fail_msg)

        # Check that no below/equal average customers are present
        forbidden_ids = [row[0] for row in rows if row[1] <= avg_total]
        forbidden_present = result_df['customer_id'].isin(forbidden_ids).any()
        _dynamic_test(self, not forbidden_present,
            "No customers with total quantity less than or equal to the average are present.",
            "Result contains customers with total quantity less than or equal to the average.")

    def test_query_uses_subquery(self):
        """Check that the query uses a subquery to calculate the average."""
        with open("query.sql", "r") as file:
            query = file.read()
        # Simple check for subquery usage
        found_subquery = "select" in query.lower() and "avg" in query.lower()
        _dynamic_test(self, found_subquery,
            "Query uses a subquery to calculate the average.",
            "Query does not use a subquery to calculate the average.")

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

実践的なデータ分析スキルに焦点を当て、SQLを基礎から学べる包括的で初心者向けのコースです。分かりやすい解説、実際の事例、実践的な課題を通じて、SQLを用いたデータのクエリ、変換、分析を習得します。

リレーショナルデータベースとSQLの基礎概念を学習します。テーブルからデータを取得、フィルタリング、並べ替える方法を探求し、より高度なデータ分析の基礎を築きます。

SQLの強力な集計機能を深く学びます。データを要約し、グループ化し、より深い洞察のために分析する方法を習得します。

複数のテーブルからデータを組み合わせる力を引き出します。さまざまな種類の結合を実行し、複雑なビジネス上の質問に答える方法を学びます。

サブクエリ、ウィンドウ関数、データ変換などを含む高度なSQL技術を習得し、強力なデータ分析を実現します。

チャレンジ：平均以上の顧客

解答