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_columns_and_data_status_present(self):
        with open("query.sql", "r") as file:
            query = file.read()
        self.cursor.execute(query)
        columns = [desc[0] for desc in self.cursor.description]
        expected_columns = ['id', 'name', 'department', 'salary', 'years_at_company', 'data_status']
        condition = all(col in columns for col in expected_columns)
        _dynamic_test(
            self,
            condition,
            "All required columns including data_status are present.",
            f"Missing columns: {set(expected_columns) - set(columns)}"
        )

    def test_data_status_complete(self):
        # Insert a row with all columns NOT NULL
        self.cursor.execute("INSERT INTO employees (id, name, department, salary, years_at_company) VALUES (200, 'Alice', 'HR', 52000, 2)")
        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)
        row = df[df['id'] == 200].iloc[0]
        condition = row['data_status'] == 'Complete'
        _dynamic_test(
            self,
            condition,
            "data_status is 'Complete' when all fields are NOT NULL.",
            "data_status is not 'Complete' when all fields are NOT NULL."
        )

    def test_data_status_incomplete(self):
        # Insert a row with a NULL value
        self.cursor.execute("INSERT INTO employees (id, name, department, salary, years_at_company) VALUES (100, NULL, 'Engineering', 90000.00, 3)")
        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)
        row = df[df['id'] == 100].iloc[0]
        condition = row['data_status'] == 'Incomplete'
        _dynamic_test(
            self,
            condition,
            "data_status is 'Incomplete' when a field is NULL.",
            "data_status is not 'Incomplete' when a field is NULL."
        )

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

A comprehensive course designed to help you master the CASE WHEN statement in SQL, from basic conditional logic to advanced use cases in data analysis and reporting. Through engaging theory and real-world tasks, you'll learn to write expressive, powerful queries that adapt to complex business requirements.

Learn the basics of the CASE WHEN statement, its syntax, and how to use it for simple conditional logic in SQL queries.

Delve deeper into complex CASE WHEN expressions, including nested conditions and multiple criteria.

Apply CASE WHEN in WHERE and ORDER BY clauses to filter and sort data conditionally.

Combine CASE WHEN with aggregate functions for advanced reporting and analytics.

Challenge: Handle Missing Employee Data

Ratkaisu

Challenge: Handle Missing Employee Data

Ratkaisu