python-testing-patterns

Write functions prefixed with test_ that follow the Arrange-Act-Assert pattern, then run them with the pytest command. Use plain assert statements for verification and pytest.raises to check that exceptions are thrown correctly.

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 on the mock, then assert the mock was called with the expected URL.

Fixture scopes control how often a fixture is created: function scope runs per test, module scope once per file, and session scope once per test run. Use broader scopes for expensive resources like database connections or app configuration.

Yes, with the pytest-asyncio plugin. Mark coroutine tests with @pytest.mark.asyncio and await the async code directly inside the test. Async fixtures are also supported for setting up resources like clients.

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 hypothesis for property-based testing 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.