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_query_returns_single_row_and_two_columns(self):
        """Check if query returns exactly one row and two columns."""
        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]
        condition = (len(rows) == 1) and (len(columns) == 2)
        _dynamic_test(
            self,
            condition,
            "Query returns a single row with two columns.",
            f"Query should return one row with two columns, got {len(rows)} row(s) and {len(columns)} column(s)."
        )

    def test_total_sales_is_correct(self):
        """Check if total_sales column correctly counts rows in sales table."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute("SELECT COUNT(*) FROM sales;")
        expected_total_sales = self.cursor.fetchone()[0]
        self.cursor.execute(query)
        row = self.cursor.fetchone()
        # Try both column names and index
        if isinstance(row, dict):
            user_total_sales = row.get('total_sales')
        else:
            user_total_sales = row[0]
        condition = (user_total_sales == expected_total_sales)
        _dynamic_test(
            self,
            condition,
            f"total_sales value is correct: {expected_total_sales}.",
            f"total_sales value is incorrect. Expected {expected_total_sales}, got {user_total_sales}."
        )

    def test_total_quantity_is_correct(self):
        """Check if total_quantity column correctly sums the quantity column."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute("SELECT SUM(quantity) FROM sales;")
        expected_total_quantity = self.cursor.fetchone()[0]
        self.cursor.execute(query)
        row = self.cursor.fetchone()
        # Try both column names and index
        if isinstance(row, dict):
            user_total_quantity = row.get('total_quantity')
        else:
            user_total_quantity = row[1]
        condition = (user_total_quantity == expected_total_quantity)
        _dynamic_test(
            self,
            condition,
            f"total_quantity value is correct: {expected_total_quantity}.",
            f"total_quantity value is incorrect. Expected {expected_total_quantity}, got {user_total_quantity}."
        )

    def test_column_names(self):
        """Check if columns are named total_sales and total_quantity (case-insensitive)."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0].lower() for desc in self.cursor.description]
        expected_columns = ["total_sales", "total_quantity"]
        condition = all(col in columns for col in expected_columns)
        missing = [col for col in expected_columns if col not in columns]
        _dynamic_test(
            self,
            condition,
            "Columns are correctly named.",
            f"Missing columns: {', '.join(missing)}."
        )


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：総売上計算

解答