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_columns_present(self):
        """Check that the output includes the required columns."""
        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 = ["sale_id", "sale_date", "employee_id", "amount", "moving_avg_7d"]
        missing = [col for col in expected_columns if col not in columns]
        _dynamic_test(
            self,
            len(missing) == 0,
            "All required columns are present in the output.",
            f"Missing columns: {missing}"
        )

    def test_ordering(self):
        """Check that the results are ordered by employee_id and sale_date."""
        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)
        if not df.empty:
            sorted_df = df.sort_values(["employee_id", "sale_date"]).reset_index(drop=True)
            condition = df[["employee_id", "sale_date"]].reset_index(drop=True).equals(
                sorted_df[["employee_id", "sale_date"]]
            )
        else:
            condition = True
        _dynamic_test(
            self,
            condition,
            "Results are ordered by employee_id and sale_date.",
            "Results are not ordered by employee_id and sale_date."
        )

    def test_moving_average_correctness(self):
        """Check the correctness of the 7-day moving average calculation for a sample employee."""
        with open("query.sql", "r") as file:
            query = file.read()
        # Get user result
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        df = pd.DataFrame(rows, columns=columns)
        # Check only for employee_id = 101
        df_101 = df[df["employee_id"] == 101].sort_values("sale_date").reset_index(drop=True)
        # Get correct moving averages for employee 101
        amounts = [1200.50, 1100.00, 1250.00, 1150.00, 1280.00, 1190.00]
        dates = ["2024-06-01", "2024-06-02", "2024-06-04", "2024-06-06", "2024-06-08", "2024-06-10"]
        # Manually calculate moving averages for each row for employee 101
        sale_dates = ["2024-06-01", "2024-06-02", "2024-06-04", "2024-06-06", "2024-06-08", "2024-06-10"]
        amounts = [1200.50, 1100.00, 1250.00, 1150.00, 1280.00, 1190.00]
        expected_avgs = []
        for i in range(len(amounts)):
            window = amounts[max(0, i-6):i+1]
            avg = round(sum(window)/len(window), 5)
            expected_avgs.append(avg)
        # Compare with user result (allow small rounding difference)
        correct = True
        for idx, row in enumerate(df_101.itertuples()):
            # Find corresponding date in manual calculation
            if str(row.sale_date) in sale_dates:
                i = sale_dates.index(str(row.sale_date))
                user_avg = float(row.moving_avg_7d)
                if not abs(user_avg - expected_avgs[i]) < 0.01:
                    correct = False
        _dynamic_test(
            self,
            correct,
            "Moving average is correctly calculated for employee 101.",
            "Moving average is not correctly calculated for employee 101."
        )

    def test_partition_by_employee(self):
        """Check that moving average is calculated separately for each 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)
        # For each employee, check that moving_avg_7d is not affected by other employees
        affected = False
        for emp_id in df["employee_id"].unique():
            emp_df = df[df["employee_id"] == emp_id].sort_values("sale_date").reset_index(drop=True)
            # For each row, moving average must only use this employee's rows
            for idx, row in enumerate(emp_df.itertuples()):
                # The moving average should be between min and max of this employee's amount so far
                window = emp_df.iloc[max(0, idx-6):idx+1]["amount"].astype(float)
                avg = round(window.mean(), 5)
                if not abs(float(row.moving_avg_7d) - avg) < 0.01:
                    affected = True
        _dynamic_test(
            self,
            not affected,
            "Moving average is calculated separately for each employee.",
            "Moving average is not partitioned by employee_id."
        )

    def test_window_frame(self):
        """Check that the window frame includes the current row and previous 6 rows only."""
        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 employee 102 (has more than 7 sales)
        emp_id = 102
        emp_df = df[df["employee_id"] == emp_id].sort_values("sale_date").reset_index(drop=True)
        # For each row, check window size is at most 7
        correct = True
        for idx, row in enumerate(emp_df.itertuples()):
            window = emp_df.iloc[max(0, idx-6):idx+1]["amount"].astype(float)
            avg = round(window.mean(), 5)
            if not abs(float(row.moving_avg_7d) - avg) < 0.01:
                correct = False
        _dynamic_test(
            self,
            correct,
            "Window frame includes current row and previous 6 rows only.",
            "Window frame is not correct (not current row and previous 6 rows)."
        )


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

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Challenge: 7-Day Moving Average of Sales

Solução

Challenge: 7-Day Moving Average of Sales

Solução