import unittest
import psycopg2
import pandas as pd
import json

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()
        # Setup test data
        cls.cursor.execute("DROP TABLE IF EXISTS user_profiles;")
        cls.cursor.execute("""
            CREATE TABLE user_profiles (
                user_id SERIAL PRIMARY KEY,
                profile_data JSONB
            );
        """)
        # Insert test data
        test_data = [
            {"address": {"city": "Springfield"}},
            {"address": {"city": "Greenville"}},
            {"address": {"city": "Fairview"}},
            {"address": {"city": "Lakeside"}},
            {"address": {"city": "Hilltown"}}
        ]
        for d in test_data:
            cls.cursor.execute("INSERT INTO user_profiles (profile_data) VALUES (%s)", [json.dumps(d)])
        cls.connection.commit()

    @classmethod
    def tearDownClass(cls):
        try:
            cls.cursor.execute("ROLLBACK;")
        except Exception:
            pass
        cls.cursor.execute("DROP TABLE IF EXISTS user_profiles;")
        cls.connection.commit()
        cls.cursor.close()
        cls.connection.close()

    def test_returns_springfield_user(self):
        """Check that the user with address.city 'Springfield' is returned."""
        with open("query.sql", "r") as file:
            query = file.read()
        try:
            self.cursor.execute("ROLLBACK;")
        except Exception:
            pass
        try:
            self.cursor.execute(query)
            rows = self.cursor.fetchall()
            # Only one user should be returned
            condition = len(rows) == 1
            failure_msg = f"Expected 1 user, got {len(rows)}: {rows}"
            _dynamic_test(
                self,
                condition,
                "User with address.city 'Springfield' is returned.",
                failure_msg
            )
            # The returned user's city should be Springfield
            if rows:
                profile_data = rows[0][1]
                if isinstance(profile_data, dict):
                    city = profile_data.get('address',{}).get('city')
                else:
                    d = json.loads(profile_data)
                    city = d.get('address',{}).get('city')
                _dynamic_test(
                    self,
                    city == "Springfield",
                    "Returned user's city is Springfield.",
                    f"Returned user's city is '{city}', expected 'Springfield'."
                )
        except Exception as e:
            _dynamic_test(
                self,
                False,
                "User with address.city 'Springfield' is returned.",
                f"Query execution failed: {e}"
            )

    def test_returns_columns(self):
        """Check that the query returns both user_id and profile_data columns."""
        with open("query.sql", "r") as file:
            query = file.read()
        try:
            self.cursor.execute("ROLLBACK;")
        except Exception:
            pass
        try:
            self.cursor.execute(query)
            columns = [desc[0] for desc in self.cursor.description]
            expected = set(["user_id", "profile_data"])
            _dynamic_test(
                self,
                expected.issubset(set(columns)),
                "Both user_id and profile_data columns are present.",
                f"Returned columns are missing expected columns: {columns}"
            )
        except Exception as e:
            _dynamic_test(
                self,
                False,
                "Both user_id and profile_data columns are present.",
                f"Query execution failed: {e}"
            )

    def test_no_false_positives(self):
        """Ensure query does not return users whose address.city is not 'Springfield'."""
        with open("query.sql", "r") as file:
            query = file.read()
        try:
            self.cursor.execute("ROLLBACK;")
        except Exception:
            pass
        try:
            self.cursor.execute(query)
            rows = self.cursor.fetchall()
            for row in rows:
                profile_data = row[1]
                if isinstance(profile_data, dict):
                    city = profile_data.get('address',{}).get('city')
                else:
                    d = json.loads(profile_data)
                    city = d.get('address',{}).get('city')
                _dynamic_test(
                    self,
                    city == "Springfield",
                    "No users with city other than 'Springfield' returned.",
                    f"Returned user with city '{city}', but only 'Springfield' should be returned."
                )
        except Exception as e:
            _dynamic_test(
                self,
                False,
                "No users with city other than 'Springfield' returned.",
                f"Query execution failed: {e}"
            )

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 the use of built-in SQL functions to manipulate strings, dates, numbers, logical values, nulls, and JSON data. This module provides a hands-on approach to understanding and applying SQL functions in real-world scenarios, enhancing your data querying and transformation skills.

Explore SQL's powerful string functions for manipulating and analyzing textual data in databases.

Challenge: Find Users by City

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