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_selects_correct_columns(self):
        """Check if query selects the correct columns from products."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        expected_columns = ["product_id", "name", "price", "discount", "stock"]
        condition = set(columns) == set(expected_columns)
        _dynamic_test(
            self,
            condition,
            "Query selects the correct columns.",
            f"Query does not select the correct columns. Got: {columns}"
        )

    def test_returns_only_low_stock(self):
        """Check that only products with stock <= 50 are returned."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        if len(rows) == 0:
            condition = True  # Acceptable if no such products exist
        else:
            condition = all(row[4] <= 50 for row in rows)
        _dynamic_test(
            self,
            condition,
            "All returned products have stock <= 50.",
            "Query returns products with stock greater than 50."
        )

    def test_no_extra_rows(self):
        """Check that products with stock > 5 are not included in the result."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        returned_ids = {row[0] for row in rows}
        self.cursor.execute("SELECT product_id FROM products WHERE stock > 50;")
        nonlow_ids = {row[0] for row in self.cursor.fetchall()}
        condition = returned_ids.isdisjoint(nonlow_ids)
        _dynamic_test(
            self,
            condition,
            "No products with stock > 50 are included.",
            "Query includes products with stock > 50."
        )


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

組み込みSQL関数を活用して、文字列、日付、数値、論理値、NULL、JSONデータを操作する方法を習得します。本コースでは、実践的なアプローチでSQL関数の理解と応用を深め、データのクエリや変換スキルを向上させます。

データベース内のテキストデータを操作および分析するための、SQLの強力な文字列関数を学習します。

SQLの日時関数を習得し、時系列データを効果的に扱う。

SQLの数値関数を使用した計算およびデータ分析の習得。

SQLの論理関数を活用して、クエリのフローを制御し、データに基づいた意思決定を行うことができます。

SQLで特殊な関数を使用してNULL値の扱いを習得します。

SQLでJSONデータを扱うための、クエリおよび操作用の専用関数の学習。

チャレンジ：在庫が少ない商品を見つける

解答