python-testing-patterns

Parameterized tests in pytest use the `pytest.mark.parametrize` decorator to pass multiple input sets to a single test function, generating isolated test cases for each scenario without duplicating code. This approach ensures broad coverage efficiently.

The AAA pattern structures tests into Arrange, Act, and Assert phases for clear organization. Pytest fixtures manage the Arrange phase by handling setup, teardown, and dependency injection, ensuring test environments are isolated and maintainable.

Mock external dependencies in Python unit testing using `unittest.mock` to replace real objects with mock objects. This isolates the code under test, allowing you to verify interactions and control return values without requiring the actual external services.

Yes, pytest supports asynchronous code testing using the `pytest-asyncio` dependency. For property-based testing, you can use the `hypothesis` library to automatically generate diverse test cases, ensuring your code handles a wide range of inputs correctly.

To run pytest with mocking and coverage, you need `pytest` for the core framework, `unittest.mock` for isolating dependencies, and `pytest-cov` to measure code execution. For async tests, `pytest-asyncio` is also required.

The best way to structure Python tests is using the AAA pattern for organization, pytest fixtures for managing setup and teardown across unit and integration tests, and mocking external dependencies to isolate units during functional testing.