python-testing-patterns

# Python Testing Patterns Comprehensive guide to implementing robust testing strategies in Python using pytest, fixtures, mocking, parameterization, and test-driven development practices. ## When to Use This Skill - Writing unit tests for Python code - Setting up test suites and test infrastructure - Implementing test-driven development (TDD) - Creating integration tests for APIs and services - Mocking external dependencies and services - Testing async code and concurrent operations - Setting up continuous testing in CI/CD - Implementing property-based testing - Testing database operations - Debugging failing tests ## Core Concepts ### 1. Test Types - **Unit Tests**: Test individual functions/classes in isolation - **Integration Tests**: Test interaction between components - **Functional Tests**: Test complete features end-to-end - **Performance Tests**: Measure speed and resource usage ### 2. Test Structure (AAA Pattern) - **Arrange**: Set up test data and preconditions - **Act**: Execute the code under test - **Assert**: Verify the results ### 3. Test Coverage - Measure what code is exercised by tests - Identify untested code paths - Aim for meaningful coverage, not just high percentages ### 4. Test Isolation - Tests should be independent - No shared state between tests - Each test should clean up after itself ## Quick Start ```python # test_example.py def add(a, b): return a + b def test_add(): """Basic test example.""" result = add(2, 3) ass...