python-testing-patterns

Unit tests in pytest verify individual functions or methods in isolation. Use test functions prefixed with `test_`, organize them in conftest.py files, and run pytest from your project root. Pytest automatically discovers and executes matching test files, reporting pass/fail results and coverage metrics.

Fixtures are reusable test components that set up preconditions before tests run and clean up afterward. Define them with @pytest.fixture decorator in conftest.py, specify scope (function, class, module, session), and pass them as arguments to test functions. Scoped fixtures reduce redundant setup and manage resource lifecycles.

Mocking replaces real objects with controlled test doubles using unittest.mock. Import Mock, patch decorators, or use monkeypatch fixtures to substitute functions, methods, or modules. Configure return values and assertions to verify your code calls dependencies correctly without executing actual external code.

Yes, pytest-asyncio enables async test functions. Mark test functions with @pytest.mark.asyncio, write async def test functions, and use await for async calls. Fixtures can also be async, allowing you to test coroutines, async context managers, and concurrent operations within your test suite.

Parameterization runs one test function against multiple input sets using @pytest.mark.parametrize. Pass argument names and values as tuples or lists, and pytest generates separate test cases for each combination. This reduces code duplication and ensures consistent edge-case coverage.

Configure your CI system to install pytest and test dependencies, then execute pytest as a build step. Pytest outputs machine-readable reports (JUnit XML, JSON) that CI systems parse for pass/fail gates. Run tests on every commit to catch regressions early and maintain code quality automatically.