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 if required columns are present in the result."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        required_cols = {"cohort_start", "week_number", "retained_users"}
        condition = required_cols.issubset(set(columns))
        _dynamic_test(
            self,
            condition,
            "All required columns are present in the result.",
            f"Missing columns: {required_cols - set(columns)}"
        )

    def test_result_values(self):
        """Check if the result matches expected cohort/week/user counts."""
        with open("query.sql", "r") as file:
            user_query = file.read()
        # Reference solution
        solution_query = '''
        SELECT
          u.signup_date AS cohort_start,
          FLOOR(EXTRACT(EPOCH FROM (l.login_date::timestamp - u.signup_date::timestamp)) / (7*24*60*60)) AS week_number,
          COUNT(DISTINCT l.user_id) AS retained_users
        FROM users u
        JOIN logins l
          ON u.user_id = l.user_id
          AND l.login_date >= u.signup_date
        GROUP BY u.signup_date, week_number
        ORDER BY cohort_start, week_number;
        '''
        self.cursor.execute(solution_query)
        rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        solution_df = pd.DataFrame(rows, columns=columns)

        self.cursor.execute(user_query)
        rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        user_df = pd.DataFrame(rows, columns=columns)

        # Compare expected rows
        missing_rows = []
        for _, expected_row in solution_df.iterrows():
            expected_dict = expected_row.to_dict()
            mask = (user_df[list(expected_dict.keys())] == pd.Series(expected_dict)).all(axis=1)
            if not mask.any():
                missing_rows.append(expected_dict)
        condition = len(missing_rows) == 0
        if not condition:
            failure_msg = f"The following expected rows are missing:\\n" + "\\n".join(str(r) for r in missing_rows)
        else:
            failure_msg = ""
        _dynamic_test(
            self,
            condition,
            "All expected cohort/week rows are present with correct retained_users count.",
            failure_msg
        )

    def test_no_extra_rows(self):
        """Check that user result does not contain extra rows not in the reference solution."""
        with open("query.sql", "r") as file:
            user_query = file.read()
        # Reference solution
        solution_query = '''
        SELECT
          u.signup_date AS cohort_start,
          FLOOR(EXTRACT(EPOCH FROM (l.login_date::timestamp - u.signup_date::timestamp)) / (7*24*60*60)) AS week_number,
          COUNT(DISTINCT l.user_id) AS retained_users
        FROM users u
        JOIN logins l
          ON u.user_id = l.user_id
          AND l.login_date >= u.signup_date
        GROUP BY u.signup_date, week_number
        ORDER BY cohort_start, week_number;
        '''
        self.cursor.execute(solution_query)
        rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        solution_df = pd.DataFrame(rows, columns=columns)

        self.cursor.execute(user_query)
        rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        user_df = pd.DataFrame(rows, columns=columns)

        # Compare extra rows
        extra_rows = []
        for _, user_row in user_df.iterrows():
            user_dict = user_row.to_dict()
            mask = (solution_df[list(user_dict.keys())] == pd.Series(user_dict)).all(axis=1)
            if not mask.any():
                extra_rows.append(user_dict)
        condition = len(extra_rows) == 0
        if not condition:
            failure_msg = f"The following extra rows are present in your result:\\n" + "\\n".join(str(r) for r in extra_rows)
        else:
            failure_msg = ""
        _dynamic_test(
            self,
            condition,
            "No extra rows are present in the result.",
            failure_msg
        )

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 SQL from the ground up with a focus on real-world product analytics. Learn to analyze sessionized data, measure retention and churn, track feature adoption, and perform A/B test analysis using advanced SQL techniques. This course is designed for aspiring product analysts with no prior SQL experience, blending engaging theory with hands-on challenges.

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Learn to track and analyze how users discover and adopt new product features using SQL.

Master advanced SQL techniques for analyzing A/B tests and using window functions for product metrics.

Challenge: Retention Matrix

Ratkaisu

Challenge: Retention Matrix

Ratkaisu