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 the results.

Use unittest.mock's patch to replace requests.get or requests.post with a Mock object whose return_value simulates the HTTP response. Configure json.return_value and raise_for_status to mimic success or error responses without network calls.

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 the test; async fixtures are also supported for setup of asynchronous resources.

Install pytest-cov and run pytest with --cov=yourpackage to measure coverage. Add --cov-report=term-missing to see uncovered lines or --cov-fail-under=80 to enforce a minimum coverage threshold in CI.

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 exploring edge cases you did not anticipate.