After exploring the basics of logical functions and their application in SQL, you are ready to take on a practical challenge that puts your understanding to the test. In real-world scenarios, it is common to categorize transactions by their amount to quickly identify high-value, medium-value, or low-value activities. This kind of classification is particularly useful for business analytics, fraud detection, and reporting.

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_all_rows_returned(self):
        """Check that all rows from transactions are returned."""
        with open("query.sql", "r") as file:
            query = file.read()

        self.cursor.execute("SELECT COUNT(*) FROM transactions;")
        expected_count = self.cursor.fetchone()[0]
        self.cursor.execute(query)
        rows = self.cursor.fetchall()
        _dynamic_test(
            self,
            len(rows) == expected_count,
            f"All {expected_count} rows are returned.",
            f"Expected {expected_count} rows, got {len(rows)}."
        )

    def test_amount_category_column_exists(self):
        """Check that amount_category column exists in result."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        _dynamic_test(
            self,
            "amount_category" in columns,
            "amount_category column is present.",
            "amount_category column is missing."
        )

    def test_high_category(self):
        """Transactions with amount >= 300 are labeled 'High'."""
        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)
        high_df = df[df["amount"] >= 300]
        condition = all(high_df["amount_category"] == "High")
        _dynamic_test(
            self,
            condition,
            "All transactions with amount >= 300 are labeled 'High'.",
            "Some transactions with amount >= 300 are not labeled 'High'."
        )

    def test_medium_category(self):
        """Transactions with amount >= 100 and < 300 are labeled 'Medium'."""
        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)
        medium_df = df[(df["amount"] >= 100) & (df["amount"] < 300)]
        condition = all(medium_df["amount_category"] == "Medium")
        _dynamic_test(
            self,
            condition,
            "All transactions with amount >= 100 and < 300 are labeled 'Medium'.",
            "Some transactions with amount >= 100 and < 300 are not labeled 'Medium'."
        )

    def test_low_category(self):
        """Transactions with amount < 100 are labeled 'Low'."""
        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)
        low_df = df[df["amount"] < 100]
        condition = all(low_df["amount_category"] == "Low")
        _dynamic_test(
            self,
            condition,
            "All transactions with amount < 100 are labeled 'Low'.",
            "Some transactions with amount < 100 are not labeled 'Low'."
        )

    def test_all_transactions_present(self):
        """Check that all transaction_ids are present in result."""
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        result_rows = self.cursor.fetchall()
        columns = [desc[0] for desc in self.cursor.description]
        result_df = pd.DataFrame(result_rows, columns=columns)
        self.cursor.execute("SELECT transaction_id FROM transactions;")
        expected_ids = set([row[0] for row in self.cursor.fetchall()])
        returned_ids = set(result_df["transaction_id"])
        condition = expected_ids == returned_ids
        _dynamic_test(
            self,
            condition,
            "All transaction_ids are present in result.",
            f"Missing transaction_ids: {expected_ids - returned_ids}"
        )

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

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: Categorize Transactions

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