python-testing

Pytest tests follow the AAA structure: arrange test data, act on the code, assert expected results. Organize tests in a tests/ directory mirroring your source layout, use test_ prefixes for files and functions, leverage fixtures for reusable setup, and parameterize tests to cover multiple scenarios efficiently.

Use pytest-mock to patch external dependencies, replacing them with controlled test doubles. This isolates the code under test, prevents side effects like database calls or API requests, and lets you verify interactions with mocked objects through assertion methods.

Test coverage measures the percentage of code executed by your test suite. Use coverage tools with pytest to identify untested lines, then write additional tests for critical paths. Aim for high coverage on business logic while balancing effort against diminishing returns on edge cases.

Yes. Pytest handles async functions through plugins like pytest-asyncio. Mark async tests with @pytest.mark.asyncio, use async fixtures for setup, and await coroutines within your test functions to verify async behavior reliably.

Pytest offers simpler syntax, powerful fixtures, and better parameterization than unittest. While unittest is Python's standard library, pytest is lighter and scales better for complex test suites, making it the framework-first recommendation for new projects.

Well-designed code is testable code. If tests are difficult to write, it often signals tight coupling or missing abstractions. Consider separating concerns, injecting dependencies, and avoiding hardcoded values—practices that make testing straightforward and improve overall design.