Which test runner does this skill focus on?

It primarily focuses on pytest, the industry standard for Python, covering its advanced features like fixtures, markers, and parameterization.

Can I use this for integration testing?

Absolutely. The skill covers mocking external services and setting up complex, multi-scoped fixtures suitable for integration and end-to-end tests.

Is it suitable for beginners to TDD?

Yes, it provides clear examples of the Arrange-Act-Assert pattern and exception testing, making it an excellent resource for learning Test-Driven Development.

How does it handle external dependencies?

It provides detailed patterns for using the unittest.mock library and pytest's monkeypatch fixture to isolate your code from databases, APIs, and environment variables.

Does this skill support asynchronous Python code?

Yes, it includes specific patterns for testing async functions and concurrent operations using pytest-asyncio best practices.

Python Testing Patterns

Name: Python Testing Patterns
Author: lifangda

bylifangda

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Seguridad y Pruebas

Implements professional testing strategies using pytest, mocking, and advanced fixtures to ensure high-quality Python codebases.

Acerca de

Python Testing Patterns is a specialized skill designed to streamline the creation of robust, maintainable test suites. It provides structured guidance on the AAA (Arrange, Act, Assert) pattern, dependency mocking with unittest.mock, and advanced pytest features like fixtures and parameterization. Whether you are implementing Test-Driven Development (TDD), testing complex asynchronous operations, or setting up integration tests for APIs, this skill ensures your Python projects maintain high code coverage and reliability through industry-standard practices.

Características Principales

Parameterized testing configurations for efficient edge-case coverage
16 GitHub stars
Asynchronous testing strategies for non-blocking Python code
Comprehensive mocking patterns for external APIs and dependencies
Environment and exception testing using monkeypatch and pytest.raises
Advanced pytest fixture management for modular setup and teardown

Casos de Uso

Developing unit and integration tests for Python backend services
Implementing Test-Driven Development (TDD) workflows in new projects
Mocking external database or API dependencies for hermetic test environments

Acerca de

Características Principales

Parameterized testing configurations for efficient edge-case coverage
16 GitHub stars
Asynchronous testing strategies for non-blocking Python code
Comprehensive mocking patterns for external APIs and dependencies
Environment and exception testing using monkeypatch and pytest.raises
Advanced pytest fixture management for modular setup and teardown

Casos de Uso

Developing unit and integration tests for Python backend services
Implementing Test-Driven Development (TDD) workflows in new projects
Mocking external database or API dependencies for hermetic test environments