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_only_unsold_products_returned(self):
        """Check that only products not in sales are returned."""
        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)
        # Find all product_ids in sales
        self.cursor.execute("SELECT DISTINCT product_id FROM sales")
        sold_ids = {row[0] for row in self.cursor.fetchall()}
        # All returned product_ids must NOT be in sold_ids
        if not result_df.empty:
            not_sold = result_df['product_id'].apply(lambda x: x not in sold_ids)
            condition = not_sold.all()
        else:
            condition = True
        _dynamic_test(
            self,
            condition,
            "Only unsold products are returned.",
            "Some returned products have sales entries."
        )

    def test_all_unsold_products_included(self):
        """Check that all unsold products are included in the result."""
        with open("query.sql", "r") as file:
            query = file.read()
        # Find all product_ids in sales
        self.cursor.execute("SELECT DISTINCT product_id FROM sales")
        sold_ids = {row[0] for row in self.cursor.fetchall()}
        # Find all unsold products
        self.cursor.execute("SELECT * FROM products WHERE product_id NOT IN (SELECT product_id FROM sales)")
        expected_rows = self.cursor.fetchall()
        expected_columns = [desc[0] for desc in self.cursor.description]
        expected_df = pd.DataFrame(expected_rows, columns=expected_columns)
        # User result
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        result_df = pd.DataFrame(rows, columns=columns)
        # Compare
        missing = []
        for _, expected_row in expected_df.iterrows():
            expected_dict = expected_row.to_dict()
            mask = (result_df[list(expected_dict.keys())] == pd.Series(expected_dict)).all(axis=1)
            if not mask.any():
                missing.append(expected_dict)
        condition = len(missing) == 0
        failure_msg = f"Missing unsold products: {missing}" if missing else ""
        _dynamic_test(
            self,
            condition,
            "All unsold products are returned.",
            failure_msg
        )

    def test_all_product_columns_included(self):
        """Check that all columns from products table are included in the output."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        self.cursor.execute("SELECT * FROM products LIMIT 1")
        product_columns = [desc[0] for desc in self.cursor.description]
        # All product columns must be present in output
        missing = [col for col in product_columns if col not in columns]
        condition = len(missing) == 0
        failure_msg = f"Missing columns from products: {missing}" if missing else ""
        _dynamic_test(
            self,
            condition,
            "All columns from products table are included.",
            failure_msg
        )

    def test_no_sold_products_returned(self):
        """Check that no sold products are in the result."""
        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)
        self.cursor.execute("SELECT DISTINCT product_id FROM sales")
        sold_ids = {row[0] for row in self.cursor.fetchall()}
        if not result_df.empty:
            has_sold = result_df['product_id'].apply(lambda x: x in sold_ids)
            condition = not has_sold.any()
        else:
            condition = True
        _dynamic_test(
            self,
            condition,
            "No sold products are included in the result.",
            "Some sold products are present in the output."
        )

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。

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解答