python-testing-patterns

Write test functions prefixed with test_ and use plain assert statements to verify behavior. Follow the Arrange-Act-Assert pattern: set up test data, execute the code under test, then verify results. Run tests with the pytest command.

Use unittest.mock's patch function or Mock objects to replace external calls like requests.get during tests. Configure return_value or side_effect on the mock to simulate responses, then assert the mock was called with expected arguments.

Pytest fixtures are declared with the @pytest.fixture decorator and injected into tests as parameters, supporting scopes like function, module, and session. They provide cleaner dependency injection and teardown via yield compared to xUnit-style setup methods.

Yes, pytest supports async tests through the pytest-asyncio plugin. Mark async test functions with @pytest.mark.asyncio and use await inside them. Async fixtures are also supported for setting up asynchronous resources.

Install pytest-cov and run pytest with the --cov flag targeting your package, such as pytest --cov=myapp. Add --cov-report=term-missing to see uncovered lines or --cov-fail-under=80 to enforce a minimum coverage threshold.

Use property-based testing with hypothesis when you want to verify invariants across many generated inputs, such as reversing a string twice returning the original. It complements example-based tests by discovering edge cases you did not anticipate.