write-pytest

To write pytest tests with fixtures and parameterization, you generate unit tests that cover a module's public API using reusable setup components and data-driven inputs. This ensures reliable behavior validation across diverse scenarios without repeating setup code.

The best way to run integration tests without mocking the database is using testcontainers-python. This approach spins up real dependencies in temporary Docker containers, validating actual data access layer behavior and ensuring ABI stability without brittle mocks.

To test asynchronous Python code correctly in pytest, you apply async correctness patterns that validate coroutine behavior without blocking. This ensures your test suite accurately handles concurrent execution paths and avoids false positives in async functions.

You should avoid time.sleep in pytest tests because it creates slow, brittle feedback loops by arbitrarily pausing execution. Enforcing a no time.sleep requirement ensures fast feedback and reliable test timing by properly synchronizing test assertions with actual state changes.

Yes, you can use testcontainers-python to validate services and data access layers. It provisions real, ephemeral dependencies for your integration tests, ensuring your public APIs interact correctly with actual database instances rather than mocked interfaces.

To structure pytest unit tests for a growing Python codebase, you apply clear test naming conventions, organize tests by module, and enforce coverage expectations. Using fixtures and parameterization keeps test logic maintainable as the public API expands.