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_selects_order_id_and_order_date(self):
        """Check if order_id and order_date columns are present 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,
            'order_id' in columns and 'order_date' in columns,
            "Result contains both 'order_id' and 'order_date' columns.",
            "Result does not contain both 'order_id' and 'order_date' columns."
        )

    def test_days_since_order_column_present(self):
        """Check if days_since_order column is present 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,
            'days_since_order' in columns,
            "Result contains 'days_since_order' column.",
            "Result does not contain 'days_since_order' column."
        )

    def test_days_since_order_is_correct(self):
        """Check if days_since_order is the difference between CURRENT_DATE and order_date for each row."""
        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)

        # Get current date from database for consistency
        self.cursor.execute("SELECT CURRENT_DATE;")
        current_date = self.cursor.fetchone()[0]

        self.cursor.execute("SELECT order_id, order_date FROM orders;")
        orders = self.cursor.fetchall()
        expected = {}
        for order_id, order_date in orders:
            expected[order_id] = (current_date - order_date).days

        mismatch = []
        for _, row in result_df.iterrows():
            oid = row['order_id']
            days = row['days_since_order']
            if oid in expected and days != expected[oid]:
                mismatch.append((oid, days, expected[oid]))
        condition = len(mismatch) == 0
        if not condition:
            mismatch_str = "\\n".join(f"order_id={oid}: got {got}, expected {exp}" for oid, got, exp in mismatch)
            failure_msg = f"Incorrect days_since_order values:\\n{mismatch_str}"
        else:
            failure_msg = ""
        _dynamic_test(
            self,
            condition,
            "days_since_order values are correct.",
            failure_msg
        )

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 Order Age

Lösning