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_completed_count(self):
        """Check if completed_count is correct."""
        with open("query.sql", "r") as file:
            query = file.read()

        self.cursor.execute(query)
        row = self.cursor.fetchone()
        completed_count = row[0]
        _dynamic_test(
            self,
            completed_count == 5,
            "completed_count is correct (5)",
            f"completed_count is not correct. Expected 5, got {completed_count}."
        )

    def test_pending_count(self):
        """Check if pending_count is correct."""
        with open("query.sql", "r") as file:
            query = file.read()

        self.cursor.execute(query)
        row = self.cursor.fetchone()
        pending_count = row[1]
        _dynamic_test(
            self,
            pending_count == 3,
            "pending_count is correct (3)",
            f"pending_count is not correct. Expected 3, got {pending_count}."
        )

    def test_failed_count(self):
        """Check if failed_count is correct."""
        with open("query.sql", "r") as file:
            query = file.read()

        self.cursor.execute(query)
        row = self.cursor.fetchone()
        failed_count = row[2]
        _dynamic_test(
            self,
            failed_count == 2,
            "failed_count is correct (2)",
            f"failed_count is not correct. Expected 2, got {failed_count}."
        )

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 course 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.

Master SQL's date and time functions to work with temporal data effectively.

Learn to use SQL's numeric functions for calculations and data analysis.

Harness SQL's logical functions to control query flow and make decisions based on data.

Master handling of NULL values in SQL using specialized functions.

Learn to work with JSON data in SQL using specialized functions for querying and manipulation.

Challenge: Summarize Transaction Status

Ratkaisu

Challenge: Summarize Transaction Status

Ratkaisu