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_count_europe_and_asia(self):
        """Check that the query counts countries from Europe and Asia only."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        result = self.cursor.fetchone()
        # Count how many countries are in Europe or Asia in the provided data
        self.cursor.execute("SELECT COUNT(*) FROM country WHERE continent='Europe' OR continent='Asia';")
        expected = self.cursor.fetchone()[0]
        actual = result[0] if result else None
        _dynamic_test(
            self,
            actual == expected,
            f"Correctly counted the number of countries in Europe and Asia: {expected}.",
            f"Incorrect count for countries in Europe and Asia. Expected {expected}, got {actual}."
        )

    def test_no_other_continents_included(self):
        """Check that only Europe and Asia countries are counted (not others)."""
        with open("query.sql", "r") as file:
            query = file.read()
        # Get the ids of countries counted by user query
        self.cursor.execute(f"SELECT id FROM country WHERE continent='Europe' OR continent='Asia';")
        valid_ids = set(row[0] for row in self.cursor.fetchall())
        # Confirm that the user query does not count more than these
        # We'll check the total count from the user query
        self.cursor.execute(query)
        user_count = self.cursor.fetchone()[0]
        # Now, check if the count for all countries is higher
        self.cursor.execute("SELECT COUNT(*) FROM country;")
        total_count = self.cursor.fetchone()[0]
        condition = user_count <= len(valid_ids) and user_count != total_count
        _dynamic_test(
            self,
            condition,
            "Query only includes countries from Europe and Asia.",
            "Query may be including countries from other continents."
        )

    def test_query_returns_single_value(self):
        """Check that the query returns exactly one row and one column."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        condition = len(rows) == 1 and len(rows[0]) == 1
        _dynamic_test(
            self,
            condition,
            "Query returns exactly one row and one column.",
            f"Query should return a single value, got {len(rows)} rows and {len(rows[0]) if rows else 0} columns."
        )

    def test_count_is_correct_value(self):
        """Check that the returned count is correct for the current data."""
        with open("query.sql", "r") as file:
            query = file.read()
        # Get expected count directly from the database
        self.cursor.execute("SELECT COUNT(*) FROM country WHERE continent='Europe' OR continent='Asia';")
        expected = self.cursor.fetchone()[0]
        self.cursor.execute(query)
        result = self.cursor.fetchone()
        actual = result[0] if result else None
        _dynamic_test(
            self,
            actual == expected,
            f"Returned count is correct: {expected}.",
            f"Returned count is incorrect. Expected {expected}, got {actual}."
        )

    def test_count_changes_if_data_changes(self):
        """Check that the count changes if a new Europe country is inserted."""
        with open("query.sql", "r") as file:
            query = file.read()
        # Get current count
        self.cursor.execute("SELECT COUNT(*) FROM country WHERE continent='Europe' OR continent='Asia';")
        before_insert = self.cursor.fetchone()[0]
        # Insert a new Europe country
        self.cursor.execute("INSERT INTO country (id, name, continent, Region, SurfaceArea, Capital, Population) VALUES (100, 'Testland', 'Europe', 'Test Region', 123, 'Testville', 1000);")
        self.connection.commit()
        try:
            self.cursor.execute(query)
            result = self.cursor.fetchone()
            after_insert = result[0] if result else None
        finally:
            self.cursor.execute("DELETE FROM country WHERE id=100;")
            self.connection.commit()
        _dynamic_test(
            self,
            after_insert == before_insert + 1,
            "Query dynamically counts new Europe country.",
            f"Query did not count new Europe country. Expected {before_insert + 1}, got {after_insert}."
        )

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

Explore the essentials of SQL — from querying data to integrating it into real-world applications. Whether you're just starting out in database management or aiming to use SQL for your application development projects, this course covers the essentials. You'll quickly learn how to leverage the full potential of SQL, from querying and managing data to seamlessly integrating it into real-world applications. By the end of the course, you'll have the confidence and skills to solve practical problems with SQL and enhance your development process.

In this section, we will learn how to use the SQL SELECT statement to get a single table column, multiple table columns, and all table columns. We will also find out how to return unique values.

In this section, we’ll explore how to organize and prioritize the data returned by a query using the ORDER BY clause. Placed at the end of a SELECT statement, this clause lets you sort your results by one or multiple columns, either in ascending or descending order, enabling more meaningful and easily interpretable data output.

In this section, we will learn how to filter returned data using the SELECT statement's WHERE clause. We will learn how to test for equality, inequality, more and less significant than value ranges, and NULL values.

In this section, we’ll explore how to refine your search conditions by combining WHERE clauses with the AND and OR operators. We’ll also cover how the NOT operator can be used to exclude certain results.

In this section, we will learn what the SQL aggregate functions are and how to use them to summarize table data.

Challenge: Continental Count

Solution