DanielMevs / Pytest-Practice---Real-World-Testing-Examples

Practical pytest examples demonstrating testing best practices for CSV validation, database operations, and API mocking

Pytest Practice - Real-World Testing Examples

A collection of practical pytest examples demonstrating testing best practices for data validation, database operations, and API interactions.

🎯 Overview

This repository showcases pytest testing techniques through realistic scenarios commonly encountered in QA automation and data engineering roles. Each example demonstrates different testing patterns and pytest features.

📁 Repository Structure

pytest-practice/
├── csv_handling/
│   ├── csv_handling.py          # CSV data validation testing
│   └── organizations-100.csv    # Sample CSV data for testing
├── database_handling/
│   ├── database_handling.py     # Database operations and SQL testing
│   └── Chinook_Sqlite.sqlite    # Sample SQLite database
├── mock_examples/
│   └── test_user_profile.py     # API testing with mocking
├── README.md                    # This file
└── LICENSE                      # MIT License

🧪 Testing Examples

1. CSV Data Validation (csv_handling.py)

Scenario: Validate CSV file structure for data pipeline ingestion

Key Features:

• pandas DataFrame validation
• Column header verification
• pytest fixtures for expected data
• Real file testing

def test_columns_have_right_field(data_columns):
    df = pd.read_csv('organizations-100.csv')
    assert list(df.columns) == data_columns

Skills Demonstrated: ✅ Data validation testing
✅ File handling in tests
✅ Fixture usage for test data
✅ pandas integration

2. Database Testing (database_handling.py)

Scenario: Test database aggregation queries and business logic

Key Features:

• SQLite database testing
• Complex SQL query validation
• Database connection management
• Multiple test scenarios
• Edge case handling

def get_top_customers_by_avg_purchases(connection, limit=5):
    # Complex aggregation query testing
    query = '''SELECT CustomerId, AVG(daily_count) AS avg_purchases_per_day
               FROM (SELECT CustomerId, InvoiceDate, COUNT(*) AS daily_count
                     FROM Invoice GROUP BY CustomerId, InvoiceDate)
               GROUP BY CustomerId ORDER BY avg_purchases_per_day DESC LIMIT ?'''
    return connection.execute(query, (limit,)).fetchall()

Skills Demonstrated: ✅ Database integration testing
✅ SQL query validation
✅ Connection fixture management
✅ Parametrized testing
✅ Edge case coverage

3. API Testing with Mocking (test_user_profile.py)

Scenario: Test API client with comprehensive error handling

Key Features:

• requests library mocking
• Multiple response scenarios
• Exception testing
• Parametrized test cases
• Mock verification

@patch('requests.get')
def test_user_successfully_gets_profile(mock_get, get_successful_user_data):
    mock_response = Mock()
    mock_response.status_code = 200
    mock_response.json.return_value = get_successful_user_data
    mock_get.return_value = mock_response
    
    result = get_user_profile(1)
    assert result == get_successful_user_data
    mock_get.assert_called_once_with("https://jsonplaceholder.typicode.com/users/1")

Skills Demonstrated: ✅ API testing with mocking
✅ External dependency isolation
✅ Exception handling validation
✅ Mock assertion verification
✅ Parametrized edge cases

🛠️ Technologies Used

• Python 3.x - Core programming language
• pytest - Testing framework with fixtures and parametrization
• pandas - Data manipulation and CSV handling
• sqlite3 - Database operations and testing
• unittest.mock - Mocking external dependencies
• requests - HTTP client library (mocked in tests)

🚀 Running the Tests

Prerequisites

pip install pytest pandas requests

Execute Tests

# Run all tests with verbose output
pytest -v

# Run specific test modules
pytest csv_handling/csv_handling.py -v
pytest database_handling/database_handling.py -v  
pytest mock_examples/test_user_profile.py -v

# Run tests by category
pytest csv_handling/ -v          # All CSV-related tests
pytest database_handling/ -v     # All database tests
pytest mock_examples/ -v         # All mocking examples

# Run with coverage (if pytest-cov installed)
pytest --cov=. --cov-report=html

Expected Output

============================= test session starts ==============================
collected 7 items

csv_handling.py::test_columns_have_right_field PASSED           [ 14%]
database_handling.py::test_get_top_customers PASSED             [ 28%]
database_handling.py::test_top_customers_different_limits PASSED [ 42%]
database_handling.py::test_empty_result_handling PASSED         [ 57%]
test_user_profile.py::test_user_successfully_gets_profile PASSED [ 71%]
test_user_profile.py::test_user_not_found_exception PASSED      [ 85%]
test_user_profile.py::test_api_server_error PASSED              [100%]

============================== 7 passed in 0.89s ==============================

💡 Testing Patterns Demonstrated

pytest Fixtures

@pytest.fixture()
def db_connection():
    conn = sqlite3.connect('Chinook_Sqlite.sqlite')
    yield conn
    conn.close()

Parametrized Testing

@pytest.mark.parametrize("user_id", [
    pytest.param("abc", id="non-numerical input"),
    pytest.param(10000, id="invalid user id"),
    pytest.param(-1, id="negative user id")
])

Exception Testing

def test_user_not_found_exception(mock_get):
    with pytest.raises(ValueError, match="User 1 not found"):
        get_user_profile(1)

Mock Verification

mock_get.assert_called_once_with("expected_url")

🎯 Real-World Applications

These testing patterns apply to common QA automation scenarios:

Data Pipeline Testing:

• CSV/JSON validation before ingestion
• Data transformation verification
• Schema compliance checking

Database Testing:

• Query result validation
• Data integrity verification
• Performance testing foundations

API Integration Testing:

• Service dependency isolation
• Error handling validation
• Contract testing foundations

📚 Learning Objectives

This repository demonstrates practical application of:

• Test-driven development practices
• External dependency mocking strategies
• Database testing methodologies
• Data validation techniques
• Error handling verification
• Test fixture management
• Parametrized test design

📊 Sample Data Sources

This repository uses educational datasets for realistic testing scenarios:

• Chinook Database: SQLite sample database from lerocha/chinook-database

  • Contains music store data (customers, invoices, tracks)
  • Widely used for database learning and testing examples

• Organizations CSV: Sample organizational data for CSV validation testing

  • Used to demonstrate data pipeline validation techniques

Note: All sample data is included for educational purposes and complete working examples.

🔗 Additional Resources

• pytest Documentation
• unittest.mock Guide
• pandas Testing Best Practices

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Note: This repository contains practical examples for learning pytest testing patterns. The sample data and database are included for educational purposes and demonstrate real-world testing scenarios commonly encountered in quality assurance and data engineering roles.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.