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_signup_month_grouping(self):
        """Check query groups by employee hire month as signup_month."""
        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)

        # All signup_month values must match employee hire months truncated to month
        self.cursor.execute("SELECT DISTINCT DATE_TRUNC('month', hire_date) FROM employees")
        expected_months = set(row[0] for row in self.cursor.fetchall())
        actual_months = set(result_df['signup_month'])
        missing = expected_months - actual_months
        _dynamic_test(
            self,
            len(missing) == 0,
            "All signup_month values are present and grouped by hire month.",
            f"Missing signup_months in result: {missing}"
        )

    def test_activity_month_identification(self):
        """Check query identifies activity_month for each performance review."""
        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)

        self.cursor.execute("SELECT DISTINCT DATE_TRUNC('month', review_date) FROM performance")
        expected_months = set(row[0] for row in self.cursor.fetchall())
        actual_months = set(result_df['activity_month'])
        missing = expected_months - actual_months
        _dynamic_test(
            self,
            len(missing) == 0,
            "All activity_month values are present and identified correctly.",
            f"Missing activity_months in result: {missing}"
        )

    def test_retention_rate_calculation(self):
        """Check retention rate is correct for a known cohort and month."""
        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)

        # Instead of a hardcoded date, find a cohort with only one employee and a month where they had a review
        self.cursor.execute("""
            SELECT e.employee_id, DATE_TRUNC('month', e.hire_date) AS signup_month, DATE_TRUNC('month', p.review_date) AS activity_month
            FROM employees e
            JOIN performance p ON e.employee_id = p.employee_id
        """)
        df = pd.DataFrame(self.cursor.fetchall(), columns=["employee_id","signup_month","activity_month"])
        # Find a cohort with only one employee
        self.cursor.execute("SELECT DATE_TRUNC('month', hire_date) as signup_month, COUNT(*) FROM employees GROUP BY DATE_TRUNC('month', hire_date)")
        cohort_sizes = dict(self.cursor.fetchall())
        single_cohorts = [k for k,v in cohort_sizes.items() if v==1]
        found = False
        for signup_month in single_cohorts:
            filtered = df[df['signup_month']==signup_month]
            if not filtered.empty:
                # Take first activity_month for this cohort
                activity_month = filtered.iloc[0]['activity_month']
                user_filtered = result_df[(result_df['signup_month'] == signup_month) & (result_df['activity_month'] == activity_month)]
                if not user_filtered.empty:
                    rate = float(user_filtered.iloc[0]['retention_rate'])
                    _dynamic_test(
                        self,
                        abs(rate - 100.0) < 0.01,
                        "Retention rate for single-employee cohort and active month is 100.0.",
                        f"Expected 100.0 retention rate, got {rate}"
                    )
                    found = True
                    break
        if not found:
            _dynamic_test(
                self,
                False,
                "",
                "No single-employee cohort with a review found for testing."
            )

    def test_decimal_precision(self):
        """Check retention_rate is returned as a decimal value (not integer)."""
        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)
        # There must be at least one value with a decimal point
        has_decimal = any((isinstance(val, float) and not float(val).is_integer()) for val in result_df['retention_rate'])
        _dynamic_test(
            self,
            has_decimal or any('.' in str(val) for val in result_df['retention_rate']),
            "At least one retention_rate value has decimal precision.",
            "No retention_rate values have decimal precision."
        )

    def test_all_signup_activity_combinations_present(self):
        """Check all signup_month and activity_month combinations where cohort members were active are present."""
        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)

        # For each employee, get their signup month and each month they had a review
        self.cursor.execute("""
            SELECT DISTINCT DATE_TRUNC('month', e.hire_date) AS signup_month, DATE_TRUNC('month', p.review_date) AS activity_month
            FROM employees e
            JOIN performance p ON e.employee_id = p.employee_id
        """)
        expected = set(self.cursor.fetchall())
        actual = set(zip(result_df['signup_month'], result_df['activity_month']))
        missing = expected - actual
        _dynamic_test(
            self,
            len(missing) == 0,
            "All signup_month and activity_month combinations with active cohort members are present.",
            f"Missing combinations: {missing}"
        )


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: Monthly Retention Rate Calculation

Lösung

Challenge: Monthly Retention Rate Calculation

Lösung