import unittest
import psycopg2
import pandas as pd
from decimal import Decimal

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_column_name(self):
        """Check that result column is named avg_bonus."""
        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,
            len(columns) == 1 and columns[0].lower() == "avg_bonus",
            "The result column is named avg_bonus.",
            f"The result column should be named avg_bonus, but got: {columns}"
        )

    def test_single_row(self):
        """Check that query returns a single row."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        _dynamic_test(
            self,
            len(rows) == 1,
            "Query returns a single row.",
            f"Query should return a single row, but got {len(rows)} rows."
        )

    def test_nulls_treated_as_zero(self):
        """Check that query treats NULL bonus values as zero when averaging."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        user_result = self.cursor.fetchone()[0]
        # Calculate expected average: replace null with 0
        bonuses = [Decimal('1500.00'), Decimal('0'), Decimal('2000.00'), Decimal('1200.00'), Decimal('0'), Decimal('1800.00'), Decimal('1300.00'), Decimal('0'), Decimal('1100.00'), Decimal('0')]
        expected_avg = sum(bonuses) / Decimal(len(bonuses))
        # Allow small floating point error
        condition = abs(user_result - expected_avg) < Decimal('0.01')
        _dynamic_test(
            self,
            condition,
            "Query treats NULL bonuses as zero and calculates correct average.",
            f"Expected average bonus: {expected_avg}, but got: {user_result}"
        )

    def test_uses_coalesce(self):
        """Check that COALESCE is used in the query to handle NULLs."""
        with open("query.sql", "r") as file:
            query = file.read()
        uses_coalesce = "coalesce" in query.lower()
        _dynamic_test(
            self,
            uses_coalesce,
            "Query uses COALESCE to replace NULLs.",
            "Query does not use COALESCE to handle NULL values in bonus column."
        )

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):
    import re
    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 Average Bonus

Ratkaisu