pytest

Python unit testing with pytest fixtures involves defining reusable setup functions in conftest.py to provide baseline data or states to your test cases. This approach streamlines test structure and ensures consistent test execution across your application.

Mocking API calls in Python tests is best handled using unittest.mock to replace external dependencies with controlled responses. This technique isolates the code being tested, ensuring reliable and fast test execution without hitting actual endpoints.

Parametrization in pytest allows running a single test function against multiple sets of data inputs. It works by applying decorators to pass different arguments, ensuring efficient test suite management and broad coverage without duplicating test code.

Yes, pytest supports asynchronous test functions to verify concurrent code and async API integrations. It handles the execution of async test cases natively, allowing you to validate non-blocking operations within your integration tests.

Categorizing tests with pytest markers involves assigning custom tags to test functions to group them logically. You can then filter and execute specific marker subsets during test runs, improving efficiency by targeting only relevant tests.

Yes, pytest integrates with coverage tools to measure code execution during test runs. This integration generates detailed reports on untested code paths, ensuring comprehensive validation and improving overall application reliability.