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_single_row_three_columns(self):
        """Check that result has one row and three 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) == 3
        _dynamic_test(
            self,
            condition,
            "Query returns a single row with three columns.",
            f"Expected 1 row and 3 columns, got {len(rows)} rows and {len(columns)} columns."
        )

    def test_column_names(self):
        """Check that columns are named min_price, max_price, avg_price."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [normalize_text(desc[0]) for desc in self.cursor.description]
        expected = ["min_price", "max_price", "avg_price"]
        missing = [col for col in expected if col not in columns]
        condition = len(missing) == 0
        _dynamic_test(
            self,
            condition,
            "Returned columns are named min_price, max_price, and avg_price.",
            f"Missing columns: {', '.join(missing)}."
        )

    def test_min_price_value(self):
        """Check that min_price matches the lowest price in products."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        row = self.cursor.fetchone()
        col_idx = [desc[0] for desc in self.cursor.description].index("min_price")
        min_price = row[col_idx]
        self.cursor.execute("SELECT MIN(price) FROM products;")
        expected = self.cursor.fetchone()[0]
        condition = float(min_price) == float(expected)
        _dynamic_test(
            self,
            condition,
            "min_price value is correct.",
            f"min_price should be {expected}, got {min_price}."
        )

    def test_max_price_value(self):
        """Check that max_price matches the highest price in products."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        row = self.cursor.fetchone()
        col_idx = [desc[0] for desc in self.cursor.description].index("max_price")
        max_price = row[col_idx]
        self.cursor.execute("SELECT MAX(price) FROM products;")
        expected = self.cursor.fetchone()[0]
        condition = float(max_price) == float(expected)
        _dynamic_test(
            self,
            condition,
            "max_price value is correct.",
            f"max_price should be {expected}, got {max_price}."
        )

    def test_avg_price_value(self):
        """Check that avg_price matches the average price in products."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        row = self.cursor.fetchone()
        col_idx = [desc[0] for desc in self.cursor.description].index("avg_price")
        avg_price = row[col_idx]
        self.cursor.execute("SELECT AVG(price) FROM products;")
        expected = self.cursor.fetchone()[0]
        # Compare rounded to 2 decimals for floating point tolerance
        condition = round(float(avg_price), 2) == round(float(expected), 2)
        _dynamic_test(
            self,
            condition,
            "avg_price value is correct (rounded to 2 decimals).",
            f"avg_price should be {expected}, got {avg_price}."
        )

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：価格分布の分析

解答