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_first_use_each_feature(self):
        """Check if query returns correct first use date for each user-feature pair."""
        with open("query.sql", "r") as file:
            query = file.read()
        # Run user query
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        result_df = pd.DataFrame(rows, columns=columns)
        # Expected result
        expected = [
            {'user_id': 1, 'feature_name': 'Search', 'first_use_date': pd.Timestamp('2024-05-01')},
            {'user_id': 1, 'feature_name': 'Upload', 'first_use_date': pd.Timestamp('2024-05-02')},
            {'user_id': 2, 'feature_name': 'Search', 'first_use_date': pd.Timestamp('2024-05-03')},
            {'user_id': 2, 'feature_name': 'Download', 'first_use_date': pd.Timestamp('2024-05-04')},
            {'user_id': 3, 'feature_name': 'Upload', 'first_use_date': pd.Timestamp('2024-05-06')},
            {'user_id': 3, 'feature_name': 'Download', 'first_use_date': pd.Timestamp('2024-05-08')},
            {'user_id': 4, 'feature_name': 'Search', 'first_use_date': pd.Timestamp('2024-05-10')},
            {'user_id': 4, 'feature_name': 'Download', 'first_use_date': pd.Timestamp('2024-05-11')},
            {'user_id': 4, 'feature_name': 'Upload', 'first_use_date': pd.Timestamp('2024-05-13')},
        ]
        expected_df = pd.DataFrame(expected)
        # Check that all expected rows are present
        missing_rows = []
        for _, exp_row in expected_df.iterrows():
            mask = (
                (result_df['user_id'] == exp_row['user_id']) &
                (result_df['feature_name'] == exp_row['feature_name']) &
                (pd.to_datetime(result_df['first_use_date']) == exp_row['first_use_date'])
            )
            if not mask.any():
                missing_rows.append(exp_row.to_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 first use rows are present.",
            failure_msg
        )

    def test_no_extra_rows(self):
        """Check that no extra user-feature pairs are returned."""
        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)
        # Expected result count
        expected_count = 9
        condition = len(result_df) == expected_count
        _dynamic_test(
            self,
            condition,
            "No extra user-feature pairs returned.",
            f"Expected {expected_count} rows, got {len(result_df)}."
        )

    def test_columns_present(self):
        """Check that result has correct columns: user_id, feature_name, first_use_date."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        expected_cols = {'user_id', 'feature_name', 'first_use_date'}
        condition = set(columns) == expected_cols
        _dynamic_test(
            self,
            condition,
            "Result columns are correct.",
            f"Result columns are {columns}, but expected {expected_cols}."
        )

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

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Challenge: Feature Adoption Metrics

Ratkaisu

Challenge: Feature Adoption Metrics

Ratkaisu