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_returns_only_customers_with_total_quantity_greater_than_5(self):
        """Check that only customers with total_quantity > 5 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]
        df = pd.DataFrame(rows, columns=columns)
        # All total_quantity must be > 5
        condition = True
        if not df.empty:
            condition = (df['total_quantity'] > 5).all()
        _dynamic_test(
            self,
            condition,
            "All customers in result have total_quantity greater than 5.",
            "Some customers have total_quantity less than or equal to 5."
        )

    def test_output_columns(self):
        """Check that output contains customer_id and total_quantity columns."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        condition = set(columns) == set(["customer_id", "total_quantity"])
        _dynamic_test(
            self,
            condition,
            "Output contains both customer_id and total_quantity columns.",
            f"Output columns are incorrect: found {columns}."
        )

    def test_total_quantity_is_sum(self):
        """Check that total_quantity is correct sum for each customer_id."""
        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)
        # Calculate expected sums for all customers
        self.cursor.execute("SELECT customer_id, SUM(quantity) AS total_quantity FROM sales GROUP BY customer_id HAVING SUM(quantity) > 5;")
        expected_rows = self.cursor.fetchall()
        expected_df = pd.DataFrame(expected_rows, columns=["customer_id", "total_quantity"])
        # Merge on customer_id and compare total_quantity
        merged = pd.merge(df, expected_df, on="customer_id", suffixes=("_user", "_expected"), how="outer", indicator=True)
        correct = (merged['total_quantity_user'] == merged['total_quantity_expected']).all() and (merged['_merge'] == 'both').all()
        _dynamic_test(
            self,
            correct,
            "total_quantity values are correct for all qualifying customers.",
            "total_quantity values are incorrect or missing for some customers."
        )

    def test_customers_with_quantity_5_or_less_not_included(self):
        """Check that customers with total quantity 5 or less are not included."""
        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)
        # Find all customers with total quantity <= 5
        self.cursor.execute("SELECT customer_id, SUM(quantity) FROM sales GROUP BY customer_id HAVING SUM(quantity) <= 5;")
        low_qty_customers = [row[0] for row in self.cursor.fetchall()]
        if df.empty:
            condition = True
        else:
            condition = not df['customer_id'].isin(low_qty_customers).any()
        _dynamic_test(
            self,
            condition,
            "No customers with total quantity <= 5 are included.",
            "Some customers with total quantity <= 5 are present in the result."
        )

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：大量取引顧客

解答