python-testing

Python testing with TDD follows the red-green-refactor cycle: write a failing test for the desired behavior first, implement the minimum code to pass, then refactor. Pytest fixtures handle setup, teardown, and dependency injection for maintainable test organization.

Testing async Python code uses the pytest-asyncio framework to execute coroutines. This Skill provides detailed guidance on structuring async tests, handling event loops, and validating asynchronous behavior alongside standard pytest assertions.

Mocking in Python testing uses unittest.mock to replace dependencies and simulate side effects. This Skill covers creating mock objects, asserting call parameters, and managing exception handling to isolate units of code during test execution.

Pytest parametrization allows running a single test function across multiple input datasets. Combined with fixtures, parametrization provides scalable test coverage by injecting varying arguments and expected outcomes into identical test logic structures.

Custom pytest markers are needed when organizing tests by behavior, environment, or execution speed. This Skill explains applying markers to selectively run specific test subsets, such as integration versus unit tests, during development workflows.

Code coverage in Python testing measures the percentage of code executed during test runs. This Skill ensures code quality by detailing coverage requirements, guiding test organization strategies, and enforcing best practices for maintainable test suites.