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_rows_returned(self):
        """Check if all rows from the movies table are returned."""
        self.cursor.execute("SELECT COUNT(*) FROM movies;")
        expected_count = self.cursor.fetchone()[0]
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        _dynamic_test(
            self,
            len(rows) == expected_count,
            f"Returned all {expected_count} rows from the movies table.",
            f"Returned {len(rows)} rows; expected {expected_count}."
        )

    def test_action_high_box_office_first(self):
        """Check that all 'Action' movies with box_office >= 50000000 appear before any other movie."""
        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)
        df["is_action_high"] = (df["genre"] == "Action") & (df["box_office"] >= 50000000)
        # Index of first non-action-high
        non_action_high_indices = df.index[~df["is_action_high"]].tolist()
        first_non_action_high_idx = non_action_high_indices[0] if non_action_high_indices else len(df)
        # Check if any action-high appears after
        action_high_after = df.loc[first_non_action_high_idx:]["is_action_high"].any() if first_non_action_high_idx < len(df) else False
        _dynamic_test(
            self,
            not action_high_after,
            "All 'Action' movies with box_office >= 50000000 appear first.",
            "Some 'Action' movies with box_office >= 50000000 appear after other movies."
        )

    def test_other_movies_rating_desc(self):
        """Check that all non-'Action' high box office movies are ordered by rating descending."""
        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)
        df["is_action_high"] = (df["genre"] == "Action") & (df["box_office"] >= 50000000)
        non_action_high = df[~df["is_action_high"]].copy()
        ratings = non_action_high["rating"].tolist()
        sorted_ratings = sorted(ratings, reverse=True)
        _dynamic_test(
            self,
            ratings == sorted_ratings,
            "All other movies are ordered by rating descending.",
            "Non-'Action' high box office movies are not ordered by rating descending."
        )

    def test_action_high_ordered_by_rating(self):
        """Check that within the 'Action' high box office group, movies are ordered by rating descending."""
        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)
        df["is_action_high"] = (df["genre"] == "Action") & (df["box_office"] >= 50000000)
        action_high = df[df["is_action_high"]].copy()
        ratings = action_high["rating"].tolist()
        sorted_ratings = sorted(ratings, reverse=True)
        _dynamic_test(
            self,
            ratings == sorted_ratings,
            "'Action' movies with high box office are ordered by rating descending.",
            "'Action' high box office movies are not ordered by rating descending."
        )

    def test_no_action_low_box_office_first(self):
        """Check that no 'Action' movie with box_office < 50000000 appears before any non-'Action' movie."""
        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)
        df["is_action_low"] = (df["genre"] == "Action") & (df["box_office"] < 50000000)
        df["is_non_action_high"] = ~((df["genre"] == "Action") & (df["box_office"] >= 50000000))
        action_low_idxs = df.index[df["is_action_low"]].tolist()
        non_action_high_idxs = df.index[df["is_non_action_high"]].tolist()
        if action_low_idxs and non_action_high_idxs:
            first_non_action_high_idx = non_action_high_idxs[0]
            action_low_before_non_action_high = any(idx < first_non_action_high_idx for idx in action_low_idxs)
        else:
            action_low_before_non_action_high = False
        _dynamic_test(
            self,
            not action_low_before_non_action_high,
            "No 'Action' movie with box_office < 50000000 appears before any non-'Action' movie.",
            "An 'Action' movie with box_office < 50000000 appears before a non-'Action' movie."
        )

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：カスタムムービーランキング

解答