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_rows_returned(self):
        """Check if query returns all rows from orders table."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute("SELECT COUNT(*) FROM orders;")
        expected_count = self.cursor.fetchone()[0]
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        _dynamic_test(
            self,
            len(rows) == expected_count,
            f"Returned all {expected_count} rows from orders table.",
            f"Returned {len(rows)} rows, expected {expected_count}."
        )

    def test_required_columns_present(self):
        """Check if output contains required columns."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        required_columns = {"order_id", "customer_id", "order_date", "predicted_delivery_date"}
        missing = required_columns - set(columns)
        _dynamic_test(
            self,
            not missing,
            "All required columns are present.",
            f"Missing columns: {', '.join(missing)}."
        )

    def test_predicted_delivery_date_correct(self):
        """Check if predicted_delivery_date is exactly 5 days after order_date."""
        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)
        incorrect = []
        for _, row in df.iterrows():
            order_date = pd.to_datetime(row["order_date"]).date()
            predicted = pd.to_datetime(row["predicted_delivery_date"]).date()
            if predicted != order_date + pd.Timedelta(days=5):
                incorrect.append({
                    "order_id": row["order_id"],
                    "order_date": str(order_date),
                    "predicted_delivery_date": str(predicted)
                })
        _dynamic_test(
            self,
            not incorrect,
            "All predicted_delivery_date values are exactly 5 days after order_date.",
            f"Incorrect prediction for: {incorrect}"
        )

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: Predict Delivery Dates

Ratkaisu