import unittest
import psycopg2
import pandas as pd
import re

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_column_name(self):
        """Check if the result column is named total_inventory_value."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        _dynamic_test(
            self,
            'total_inventory_value' in [normalize_text(c) for c in columns],
            "The result column is named total_inventory_value.",
            "The result column must be named total_inventory_value."
        )

    def test_single_row(self):
        """Check if the query returns a single row."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        _dynamic_test(
            self,
            len(rows) == 1,
            "Query returns a single row.",
            f"Query should return a single row, but got {len(rows)} rows."
        )

    def test_total_inventory_value(self):
        """Check if the total inventory value is correctly calculated as sum(price * stock) for all products."""
        with open("query.sql", "r") as file:
            query = file.read()
        # Calculate expected value
        self.cursor.execute("SELECT SUM(price * stock) FROM products;")
        expected = self.cursor.fetchone()[0]
        self.cursor.execute(query)
        result = self.cursor.fetchone()[0]
        _dynamic_test(
            self,
            result == expected,
            "Total inventory value is correct.",
            f"Total inventory value is incorrect. Expected {expected}, got {result}."
        )

    def test_uses_sum_and_multiplication(self):
        """Check that the query uses SUM and multiplies price and stock."""
        with open("query.sql", "r") as file:
            query = file.read().lower().replace(' ', '')
        has_sum = 'sum(' in query
        has_multiplication = ('price*stock' in query) or ('stock*price' in query)
        _dynamic_test(
            self,
            has_sum and has_multiplication,
            "Query uses SUM and multiplies price and stock.",
            "Query must use SUM and multiply price and stock."
        )

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：在庫価値の計算

解答