python-testing

To write a basic pytest test, create a function prefixed with test_ and use standard assert statements to validate Python code behavior, allowing pytest to automatically discover and execute the test.

The best way to apply Test-Driven Development is using the red-green-refactor cycle: write a failing pytest test first, implement the minimum Python code to pass it, then refactor the codebase for maintainability.

Pytest fixtures provide a reusable baseline for tests by initializing resources, databases, or states, allowing you to inject these dependencies into test functions via arguments for clear test organization.

Mocking isolates your code by replacing external dependencies with simulated objects, allowing you to use patching to verify interactions and test specific behaviors without calling actual external services.

Yes, you can test asynchronous Python code with pytest by writing async test functions, enabling you to validate the behavior and reliability of concurrent operations using async testing strategies.

You should use parametrization when you need to run the same test logic against multiple input combinations, allowing pytest to generate distinct test cases for comprehensive coverage and maintainability.