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 course 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.

Master SQL's date and time functions to work with temporal data effectively.

Learn to use SQL's numeric functions for calculations and data analysis.

Harness SQL's logical functions to control query flow and make decisions based on data.

Master handling of NULL values in SQL using specialized functions.

Learn to work with JSON data in SQL using specialized functions for querying and manipulation.

Challenge: Predict Delivery Dates

Lösning

Challenge: Predict Delivery Dates

Lösning