python-testing-patterns

Write test functions prefixed with test_ and use plain assert statements to verify results. Follow the Arrange-Act-Assert pattern: set up test data, execute the code under test, then verify the output matches expectations.

Use unittest.mock's patch function or Mock objects to replace requests.get or requests.post during tests. Configure return_value and side_effect on the mock to simulate successful responses or HTTP errors without real network calls.

Pytest fixtures are functions decorated with @pytest.fixture that provide setup and teardown for tests, such as database connections or test data. Use scope parameters like session, module, or function to control how often fixtures are created.

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 can also be defined for asynchronous setup and teardown.

Install pytest-cov and run pytest with the --cov flag targeting your package, such as pytest --cov=myapp tests/. Add --cov-report=term-missing to see uncovered lines and --cov-fail-under=80 to enforce a minimum 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 catching edge cases you did not anticipate.