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_all_columns(self):
        """Check that the query returns all columns from transactions with correct names."""
        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 = ["transaction_id", "amount", "status", "payment_method"]
        condition = columns == expected_columns
        _dynamic_test(
            self,
            condition,
            "Query returns all columns with correct names.",
            f"Query does not return the expected columns. Got: {columns}"
        )

    def test_null_payment_method_displays_unknown(self):
        """Check that rows with null payment_method display 'Unknown'."""
        # Insert a row with null payment_method
        self.cursor.execute("INSERT INTO transactions (transaction_id, amount, status, payment_method) VALUES (99, 123.45, 'completed', NULL);")
        self.connection.commit()
        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)
        filtered = df[df['transaction_id'] == 99]
        # Should be exactly one row
        condition = (
            len(filtered) == 1 and
            filtered.iloc[0]['payment_method'] == 'Unknown'
        )
        _dynamic_test(
            self,
            condition,
            "Rows with null payment_method display 'Unknown'.",
            "Row with null payment_method does not display 'Unknown'."
        )
        # Clean up
        self.cursor.execute("DELETE FROM transactions WHERE transaction_id = 99;")
        self.connection.commit()

    def test_non_null_payment_method_unchanged(self):
        """Check that rows with non-null payment_method display original value."""
        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)
        # Select a known row
        row = df[df['transaction_id'] == 1].iloc[0]
        condition = row['payment_method'] == 'credit_card'
        _dynamic_test(
            self,
            condition,
            "Rows with non-null payment_method display original value.",
            f"Row with non-null payment_method does not display original value: got {row['payment_method']}"
        )

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：支払い方法の欠落を処理する

解答