import unittest
import psycopg2
import pandas as pd
from datetime import date, timedelta

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()
        # Create performance table and sample data
        cls.cursor.execute("DROP TABLE IF EXISTS performance;")
        cls.cursor.execute("DROP TABLE IF EXISTS employees;")
        cls.cursor.execute("""
            CREATE TABLE employees (
                employee_id SERIAL PRIMARY KEY,
                name VARCHAR(100) NOT NULL,
                department VARCHAR(50) NOT NULL,
                hire_date DATE NOT NULL,
                salary DECIMAL(10,2) NOT NULL
            );
        """)
        cls.cursor.execute("""
            CREATE TABLE performance (
                performance_id SERIAL PRIMARY KEY,
                employee_id INTEGER NOT NULL REFERENCES employees(employee_id),
                review_date DATE NOT NULL,
                score INTEGER NOT NULL CHECK (score BETWEEN 1 AND 10)
            );
        """)
        cls.cursor.execute("""
            INSERT INTO employees (name, department, hire_date, salary) VALUES
            ('Alice Johnson', 'Engineering', '2021-03-15', 85000.00),
            ('Bob Smith', 'Marketing', '2020-07-22', 65000.00),
            ('Carol Lee', 'Human Resources', '2019-11-01', 60000.00);
        """)
        # Insert test data for three employees
        cls.cursor.execute("""
            INSERT INTO performance (employee_id, review_date, score) VALUES
            (1, '2022-03-20', 8),
            (1, '2023-03-18', 9),
            (2, '2021-08-01', 7),
            (2, '2022-08-01', 8),
            (2, '2023-08-01', 7),
            (3, '2020-12-15', 6),
            (3, '2021-12-15', 7);
        """)
        cls.connection.commit()

    @classmethod
    def tearDownClass(cls):
        cls.cursor.execute("DROP TABLE IF EXISTS performance;")
        cls.cursor.execute("DROP TABLE IF EXISTS employees;")
        cls.connection.commit()
        cls.cursor.close()
        cls.connection.close()

    def test_returns_one_row_per_review(self):
        """Check the query returns one row per review."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute("SELECT COUNT(*) FROM performance;")
        expected_count = self.cursor.fetchone()[0]
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        _dynamic_test(
            self,
            len(rows) == expected_count,
            "The query returns one row for each review.",
            f"Expected {expected_count} rows, got {len(rows)}."
        )

    def test_output_columns(self):
        """Check 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]
        required = ['employee_id', 'review_date', 'score', 'rolling_3_month_avg_score']
        missing = [col for col in required if col not in columns]
        _dynamic_test(
            self,
            not missing,
            "All required columns are present in the result.",
            f"Missing columns: {', '.join(missing)}."
        )

    def test_ordering(self):
        """Check the results are ordered by employee_id, review_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)
        sorted_df = df.sort_values(["employee_id", "review_date"]).reset_index(drop=True)
        condition = df.reset_index(drop=True).equals(sorted_df)
        _dynamic_test(
            self,
            condition,
            "Result is ordered by employee_id and review_date.",
            "Result is not ordered by employee_id and review_date."
        )

    def test_rolling_3_month_avg_score(self):
        """Check the rolling 3-month average score values are correct for each row."""
        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)
        # Ensure review_date is pandas.Timestamp
        df['review_date'] = pd.to_datetime(df['review_date'])
        # Calculate expected rolling averages manually
        expected = []
        for employee_id in df['employee_id'].unique():
            edf = df[df['employee_id'] == employee_id].copy()
            for idx, row in edf.iterrows():
                end_date = row['review_date']
                start_date = (end_date - pd.DateOffset(months=2)).replace(day=1)
                mask = (edf['review_date'] >= start_date) & (edf['review_date'] <= end_date)
                window_avg = edf.loc[mask, 'score'].astype(float).mean()
                expected.append(window_avg)
        actual = df['rolling_3_month_avg_score'].astype(float).tolist()
        # Compare floats with tolerance
        comparison = all(abs(a - b) < 0.01 for a, b in zip(expected, actual))
        _dynamic_test(
            self,
            comparison,
            "The rolling_3_month_avg_score column contains correct averages.",
            "The rolling_3_month_avg_score column contains incorrect values."
        )

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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Explore the foundational concepts of window functions, their syntax, and how they differ from traditional aggregate functions. Learn to apply basic window functions for analytics.

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Challenge: Rolling 3-Month Spend per Customer

Soluzione

Challenge: Rolling 3-Month Spend per Customer

Soluzione