python-testing-patterns

Define a function decorated with @pytest.fixture that performs setup, yields the resource to the test, then runs teardown code after the yield. Use scope parameters like "session" or "module" to control how often the fixture is created.

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.

Yes, pytest supports async tests through the pytest-asyncio plugin. Mark test functions with @pytest.mark.asyncio and use await inside them; async fixtures can also yield resources for asynchronous setup and teardown.

Use @pytest.mark.parametrize with a list of input and expected value tuples. Each tuple generates a separate test case, and pytest.param lets you assign custom IDs for readable test output.

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 monkeypatch for modifying environment variables, dictionary entries, or object attributes that pytest automatically reverts after the test. Use unittest.mock when you need to assert call counts, arguments, or simulate complex object behavior.