python-testing-patterns

Pytest fixtures enable reusable test setup by defining functions injected by name into tests. They manage setup and teardown state, allowing modular dependency control across unit and integration test suites.

Parameterization in pytest runs a single test against multiple inputs. Use it to cover diverse scenarios by decorating tests with parameters, reducing duplication while validating behavior across various data combinations.

Mocking isolates code by replacing dependencies with controlled objects. Use mocking patterns to simulate external calls, ensuring tests remain fast and deterministic without requiring live connections.

Pytest supports async tests through specific patterns. You can write tests for asynchronous functions by applying async testing techniques, ensuring proper await handling and event loop management.

Property-based testing generates diverse inputs automatically to find edge cases. Using Hypothesis, you define properties your code should satisfy, and the framework runs tests against generated data, improving confidence.

Monkeypatching dynamically alters attributes or environment variables during test execution. Use it within pytest to temporarily modify code behavior, ensuring tests remain isolated and original states are restored.