pytest

Pytest fixtures handle setup, teardown, and dependency injection for Python testing. You define fixture functions and inject them into tests by name, controlling their lifecycle through different scopes to manage resources efficiently across test runs.

The best way to run a Python test with multiple data sets is parametrization. Using the @pytest.mark.parametrize decorator, you provide variable arguments and expected outcomes to execute the same test logic across different inputs automatically.

To mock external dependencies for isolated Python testing, use the unittest.mock library. This allows you to replace real external calls with mock objects, ensuring your tests remain isolated and focus solely on the code under test.

Yes, you can use pytest for testing asynchronous code. The framework provides specific patterns and examples to handle async testing scenarios, allowing you to validate your asynchronous Python functions reliably alongside standard synchronous tests.

Pytest markers allow you to categorize and selectively skip tests. By applying custom markers to test functions, you can filter test execution runs by category or bypass specific tests entirely based on runtime conditions or environment requirements.