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_only_null_manager_id(self):
        """Check that only employees with null manager_id 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]
        df = pd.DataFrame(rows, columns=columns)
        condition = df['manager_id'].isnull().all() and len(df) > 0
        _dynamic_test(
            self,
            condition,
            "All returned employees have null manager_id.",
            "Some returned employees have a non-null manager_id, or result is empty."
        )

    def test_all_columns_included(self):
        """Check that all columns from employees table are included."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        expected_columns = ["employee_id", "name", "department", "manager_id", "bonus"]
        condition = set(expected_columns) == set(columns)
        _dynamic_test(
            self,
            condition,
            "All columns from employees table are included.",
            f"Missing or extra columns in result: {set(columns) ^ set(expected_columns)}"
        )

    def test_returns_correct_employees(self):
        """Check that correct employees are returned when manager_id is null."""
        with open("query.sql", "r") as file:
            query = file.read()
        # Get expected employees
        self.cursor.execute("SELECT employee_id FROM employees WHERE manager_id IS NULL")
        expected_ids = set(row[0] for row in self.cursor.fetchall())
        # Run user query
        self.cursor.execute(query)
        result_ids = set(row[0] for row in self.cursor.fetchall())
        condition = result_ids == expected_ids
        _dynamic_test(
            self,
            condition,
            "Returned employees match those with null manager_id.",
            f"Returned employee IDs do not match: {expected_ids ^ result_ids}"
        )

    def test_works_with_additional_nulls(self):
        """Check that query works if more employees with null manager_id are added."""
        # Add a test employee with null manager_id
        self.cursor.execute("""
            INSERT INTO employees (employee_id, name, department, manager_id, bonus)
            VALUES (999, 'Test User', 'Test', NULL, 999.99)
        """)
        self.connection.commit()
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        employee_ids = [row[0] for row in rows]
        condition = 999 in employee_ids
        _dynamic_test(
            self,
            condition,
            "Query includes newly added employee with null manager_id.",
            "Query does not include newly added employee with null manager_id."
        )
        # Clean up
        self.cursor.execute("DELETE FROM employees WHERE employee_id = 999")
        self.connection.commit()
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: Find Employees Without Managers

Solution