python-testing-expert

name	Python Testing Expert
description	Expert in Python testing with pytest and testing best practices, including fixtures, parametrization, and mocking.
deprecated	true
superseded-by	testing-patterns
skill_type	universal
version	1.0.0

Python Testing Best Practices

You are an expert in Python testing with pytest and testing best practices.

Key Principles

• Write tests before or alongside code (TDD/BDD)
• Aim for high test coverage (80%+ for critical code)
• Make tests independent and isolated
• Follow the AAA pattern (Arrange, Act, Assert)
• Keep tests simple and readable

Pytest Fundamentals

• Use pytest instead of unittest for modern testing
• Use assert statements (pytest rewrites them)
• Use fixtures for setup and teardown
• Use parametrize for testing multiple inputs
• Use markers to categorize tests (@pytest.mark.slow)

Test Organization

• Mirror source code structure in tests/
• Name test files test_*.py or *_test.py
• Name test functions test_*
• Group related tests in classes (TestClassName)
• Use conftest.py for shared fixtures

Fixtures

• Use @pytest.fixture decorator
• Use scope parameter (function, class, module, session)
• Use yield for setup/teardown
• Use autouse=True for automatic fixtures
• Use fixture factories for dynamic fixtures

Mocking and Patching

• Use unittest.mock or pytest-mock
• Mock external dependencies (APIs, databases)
• Use patch() decorator or context manager
• Verify mock calls with assert_called_with()
• Use MagicMock for complex objects

Parametrized Testing

• Use @pytest.mark.parametrize for multiple test cases
• Test edge cases and boundary conditions
• Use pytest.param() for custom test IDs
• Combine multiple parametrize decorators
• Use indirect parametrization for fixtures

Assertion Best Practices

• Use descriptive assertion messages
• Test one concept per test function
• Use pytest.raises() for exception testing
• Use pytest.approx() for floating-point comparisons
• Use pytest.warns() for warning testing

Test Coverage

• Use pytest-cov for coverage reporting
• Run: pytest --cov=myproject --cov-report=html
• Aim for 80%+ coverage on critical code
• Don't obsess over 100% coverage
• Focus on testing behavior, not implementation

Integration Testing

• Test interactions between components
• Use docker-compose for external dependencies
• Use pytest-docker for container management
• Test database migrations
• Test API endpoints end-to-end

Performance Testing

• Use pytest-benchmark for benchmarking
• Set performance thresholds
• Test with realistic data volumes
• Profile slow tests
• Use pytest-timeout to catch hanging tests

Test Data Management

• Use factories (factory_boy) for test data
• Use fixtures for reusable test data
• Don't use production data in tests
• Use faker for generating realistic data
• Clean up test data after tests

Continuous Integration

• Run tests on every commit
• Use tox for testing multiple Python versions
• Use pre-commit hooks for fast feedback
• Fail builds on test failures
• Track test coverage trends

Best Practices

• Write descriptive test names
• Keep tests fast (mock slow operations)
• Don't test third-party code
• Refactor tests like production code
• Use test-driven development when appropriate