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_all_products_returned(self):
        """Check that all products are returned by the query."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        self.cursor.execute("SELECT COUNT(*) FROM products;")
        expected_count = self.cursor.fetchone()[0]
        _dynamic_test(
            self,
            len(rows) == expected_count,
            "All products are returned.",
            f"Expected {expected_count} products, got {len(rows)}."
        )

    def test_discounted_price_column_present(self):
        """Check that the discounted_price column exists in the result."""
        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,
            "discounted_price" in columns,
            "discounted_price column exists.",
            "discounted_price column is missing."
        )

    def test_discounted_price_calculation(self):
        """Check that discounted_price is calculated as price - discount for each product."""
        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)
        self.cursor.execute("SELECT product_id, price, discount FROM products;")
        prod_rows = self.cursor.fetchall()
        for product_id, price, discount in prod_rows:
            expected_value = round(float(price) - float(discount), 2)
            actual_value = df[df["product_id"] == product_id]["discounted_price"].iloc[0]
            condition = abs(float(actual_value) - expected_value) < 0.01
            _dynamic_test(
                self,
                condition,
                f"discounted_price correct for product_id {product_id}.",
                f"discounted_price incorrect for product_id {product_id}: expected {expected_value}, got {actual_value}."
            )

    def test_discounted_price_rounded(self):
        """Check that discounted_price is rounded to two decimal places for all products."""
        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)
        not_rounded = []
        for idx, v in df["discounted_price"].items():
            # Check if value has at most two decimal places
            str_v = f"{v:.2f}"
            if "." in str_v:
                decimals = str_v.split(".")[1]
                if len(decimals) != 2:
                    not_rounded.append((idx, v))
        condition = len(not_rounded) == 0
        _dynamic_test(
            self,
            condition,
            "All discounted_price values are rounded to two decimal places.",
            f"Some discounted_price values are not rounded to two decimals: {not_rounded}"
        )

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

Master the use of built-in SQL functions to manipulate strings, dates, numbers, logical values, nulls, and JSON data. This module provides a hands-on approach to understanding and applying SQL functions in real-world scenarios, enhancing your data querying and transformation skills.

Explore SQL's powerful string functions for manipulating and analyzing textual data in databases.

Challenge: Calculate Discounted Prices

Ratkaisu