import unittest
import psycopg2
import pandas as pd
from datetime import timedelta

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_returns_only_late_orders(self):
        """Check that only orders shipped more than 3 days after order_date are returned."""
        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]
        result_df = pd.DataFrame(rows, columns=columns)
        if not result_df.empty:
            # Check that all returned rows have shipped_at > order_date + 3 days
            result_df['order_date'] = pd.to_datetime(result_df['order_date'])
            result_df['shipped_at'] = pd.to_datetime(result_df['shipped_at'])
            late_mask = result_df['shipped_at'] > (result_df['order_date'] + pd.Timedelta(days=3))
            condition = late_mask.all()
            failure_msg = "Some returned rows do not have shipped_at more than 3 days after order_date."
        else:
            # Acceptable if no rows, as no late orders in base data
            condition = True
            failure_msg = ""
        _dynamic_test(
            self,
            condition,
            "Only orders shipped more than 3 days after order_date are returned.",
            failure_msg or "Some rows were returned, but no orders should be late based on the data."
        )

    def test_no_early_orders_in_result(self):
        """Check that no orders shipped within 3 days are included in the result."""
        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]
        result_df = pd.DataFrame(rows, columns=columns)
        if not result_df.empty:
            result_df['order_date'] = pd.to_datetime(result_df['order_date'])
            result_df['shipped_at'] = pd.to_datetime(result_df['shipped_at'])
            # Any row where shipped_at <= order_date + 3 days is an error
            early_mask = result_df['shipped_at'] <= (result_df['order_date'] + pd.Timedelta(days=3))
            condition = (~early_mask).all()
            failure_msg = "Early orders (not late) were included in the result."
        else:
            condition = True
            failure_msg = ""
        _dynamic_test(
            self,
            condition,
            "No early orders are included in the result.",
            failure_msg
        )

    def test_query_works_with_late_data(self):
        """Check that query correctly returns late orders if present."""
        # Insert a late order
        self.cursor.execute("INSERT INTO orders (order_id, customer_id, order_date, delivery_time, shipped_at) VALUES (11, 111, '2024-05-01', '10:00:00', '2024-05-05 12:00:00')")
        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]
        result_df = pd.DataFrame(rows, columns=columns)
        # There should be one row, order_id 11
        condition = (result_df.shape[0] >= 1) and (11 in result_df['order_id'].values)
        _dynamic_test(
            self,
            condition,
            "Late order is returned correctly.",
            "Late order was not returned as expected."
        )

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：遅延納品の検索

解答