pytest

Pytest fixtures handle setup and teardown for Python tests via dependency injection. You define fixtures with various scopes to manage database initialization and resource cleanup, ensuring maintainable and isolated test environments across your codebase.

Mocking external API calls in Python integration testing is best handled using the unittest.mock library. It replaces actual dependencies with mock objects to isolate test components, ensuring reliable and focused test execution without network dependency.

Parametrization in Python testing executes a single test function multiple times with different input arguments. It eliminates redundant code by defining datasets directly on the test function, ensuring broad behavioral coverage efficiently across varied scenarios.

Yes, you can write asynchronous tests with pytest for Python applications. The framework supports asynchronous testing scenarios, allowing you to validate async functions and coroutines seamlessly to ensure code reliability in concurrent environments.

You categorize and selectively execute unit tests in Python by applying markers. Markers label specific test functions, enabling targeted test runs via the command line to isolate integration testing from basic unit testing quickly and efficiently.

Python test structures fail to handle expected exceptions when assertions are missing. Pytest provides specific assertion mechanisms to verify expected exceptions, ensuring your tests correctly capture and validate failure scenarios instead of passing erroneously.