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 setUp(self):
        self.cursor.execute("ROLLBACK; BEGIN;")

    def test_columns_present(self):
        """Check that all required columns are present in the query result."""
        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 = [
            "performance_id",
            "employee_id",
            "review_date",
            "score",
            "name",
            "department",
            "transaction_amount",
            "avg_last_12_months",
            "is_spike"
        ]
        missing = [col for col in expected_columns if col not in columns]
        _dynamic_test(
            self,
            len(missing) == 0,
            "All required columns are present.",
            f"Missing columns: {', '.join(missing)}"
        )

    def test_avg_last_12_months_calculation(self):
        """Check avg_last_12_months is correct for at least one employee."""
        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)
        # Pick Alice Johnson (employee_id=1)
        alice = df[df['employee_id'] == 1].sort_values('review_date')
        # Her two reviews: 2022-03-20 (score=8), 2023-03-18 (score=9)
        # First review: no prior 12 months, avg_last_12_months should be null
        first = alice.iloc[0]
        _dynamic_test(
            self,
            pd.isnull(first['avg_last_12_months']),
            "First transaction has null avg_last_12_months.",
            "First transaction should have null avg_last_12_months."
        )
        # Second review: prior 12 months includes only first review (score=8)
        second = alice.iloc[1]
        avg_val = second['avg_last_12_months']
        if isinstance(avg_val, float):
            diff = abs(avg_val - 8.0)
        elif isinstance(avg_val, Decimal):
            diff = abs(avg_val - Decimal('8.0'))
        else:
            diff = abs(float(avg_val) - 8.0)
        _dynamic_test(
            self,
            diff < 0.001,
            "Second transaction avg_last_12_months is correct.",
            f"Second transaction avg_last_12_months should be 8.0, got {second['avg_last_12_months']}"
        )

    def test_is_spike_flag(self):
        """Check is_spike is set correctly for all transactions."""
        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)
        # For Alice Johnson (employee_id=1):
        # 2022-03-20: avg_last_12_months=null, score=8, is_spike=0
        # 2023-03-18: avg_last_12_months=8, score=9, 9 < 16, so is_spike=0
        alice = df[df['employee_id'] == 1].sort_values('review_date')
        _dynamic_test(
            self,
            alice.iloc[0]['is_spike'] == 0,
            "First Alice transaction is not flagged as spike.",
            "First Alice transaction should not be flagged as spike."
        )
        _dynamic_test(
            self,
            alice.iloc[1]['is_spike'] == 0,
            "Second Alice transaction is not flagged as spike.",
            "Second Alice transaction should not be flagged as spike."
        )
        # For Bob Smith (employee_id=2):
        # 2021-08-01: avg_last_12_months=null, is_spike=0
        # 2022-08-01: avg=7, score=8, 8<14, is_spike=0
        # 2023-08-01: avg=(7+8)/2=7.5, score=7, 7<15, is_spike=0
        bob = df[df['employee_id'] == 2].sort_values('review_date')
        _dynamic_test(
            self,
            all(bob['is_spike'] == 0),
            "Bob Smith has no spikes, as expected.",
            "Bob Smith transactions should not be flagged as spikes."
        )

    def test_single_transaction_employee(self):
        """Check that employees with one transaction have null avg_last_12_months and is_spike=0."""
        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)
        # Frank Green (employee_id=6) has only one review
        frank = df[df['employee_id'] == 6]
        _dynamic_test(
            self,
            len(frank) == 1 and pd.isnull(frank.iloc[0]['avg_last_12_months']) and frank.iloc[0]['is_spike'] == 0,
            "Single transaction employee handled correctly.",
            "Single transaction employee avg_last_12_months should be null and is_spike should be 0."
        )

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

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Challenge: Detect Spending Spikes

Lösung

Challenge: Detect Spending Spikes

Lösung