testing

Unit tests with Pytest involve writing test functions that assert expected behavior. Create a test file, import your function, and use `assert` statements to verify outputs. Pytest automatically discovers and runs test files matching the `test_*.py` naming convention.

Code coverage measures how much of your codebase tests exercise. Use pytest-cov to generate coverage reports and identify untested lines. Run `pytest --cov` to see coverage percentages and locate gaps in your test suite.

Mocking isolates tests from external dependencies using `unittest.mock`. Import `patch` or `Mock`, replace the API call in your test, and verify the mock was called correctly. This ensures tests run reliably without hitting real services.

Yes, Pytest's parametrization feature lets you run one test function with different inputs. Use `@pytest.mark.parametrize` to define multiple test cases, reducing code duplication and improving test coverage across scenarios.

Organize tests in a dedicated `tests/` directory mirroring your project structure. Separate unit tests from integration tests using subdirectories or naming conventions. Use Pytest fixtures to manage test data and setup, improving maintainability and clarity.

Unit tests verify individual functions in isolation using mocks for dependencies. Integration tests verify multiple components work together with real or semi-real interactions. Both are essential; use unit tests for speed and integration tests for confidence in system behavior.