pytest

To structure Python tests using pytest, you write functions with standard assertions and handle exceptions directly. This approach eliminates boilerplate, allowing you to validate code behavior and expected errors quickly.

Pytest fixtures provide setup and teardown mechanisms along with dependency injection. You define fixture functions with specific scopes to initialize states and supply reusable test data across your test suite.

You can run the same Python test with multiple data sets using the @pytest.mark.parametrize decorator. This parametrization feature executes your test function across varied inputs to ensure broad coverage.

To mock external dependencies in a pytest test, you use unittest.mock.patch. This technique replaces real objects with mock instances, isolating your unit tests from databases, APIs, and other external systems.

Pytest supports testing asynchronous Python code by providing specific patterns for async scenarios. This allows you to validate coroutines and asynchronous application behavior within the standard test runner.

You skip or categorize tests in pytest using custom markers. Markers enable conditional skipping based on environment conditions and allow selective execution by filtering specific test categories via command-line options.