python-testing-patterns

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Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

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SKILL.md Content

Python Testing Patterns

Master pytest fundamentals through essential patterns and practical examples, from basic unit tests to mocking external dependencies.

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)
    assert result == 5

def test_add_negative():
    """Test with negative numbers."""
    assert add(-1, 1) == 0

# Run with: uv run pytest test_example.py

Fundamental Patterns

Pattern 1: Basic pytest Tests

python
# test_calculator.py
import pytest

class Calculator:
    """Simple calculator for testing."""

    def add(self, a: float, b: float) -> float:
        return a + b

    def divide(self, a: float, b: float) -> float:
        if b == 0:
            raise ValueError("Cannot divide by zero")
        return a / b


def test_addition():
    """Test addition."""
    calc = Calculator()
    assert calc.add(2, 3) == 5
    assert calc.add(-1, 1) == 0


def test_division_by_zero():
    """Test division by zero raises error."""
    calc = Calculator()
    with pytest.raises(ValueError, match="Cannot divide by zero"):
        calc.divide(5, 0)

Pattern 2: Fixtures for Setup and Teardown

python
# test_database.py
import pytest

@pytest.fixture
def db():
    """Fixture that provides connected database."""
    # Setup
    database = {"connected": True}
    yield database
    # Teardown
    database["connected"] = False


def test_database_query(db):
    """Test using fixture."""
    assert db["connected"] is True

Pattern 3: Parametrised Tests

python
# test_validation.py
import pytest

@pytest.mark.parametrize("email,expected", [
    ("user@example.com", True),
    ("invalid.email", False),
])
def test_email_validation(email, expected):
    """Test email validation with various inputs."""
    assert is_valid(email) == expected

Pattern 4: Mocking with unittest.mock

python
# test_api_client.py
import pytest
from unittest.mock import Mock, patch

def test_api_call():
    """Test API call with mock."""
    mock_response = Mock()
    mock_response.json.return_value = {"id": 1}

    with patch("requests.get", return_value=mock_response):
        result = get_user(1)
        assert result["id"] == 1

Pattern 5: Testing Exceptions

python
# test_exceptions.py
import pytest

def test_exception():
    """Test exception is raised."""
    with pytest.raises(ValueError, match="error message"):
        divide(10, 0)

Advanced & Specialised Patterns

For additional testing patterns and advanced techniques, see the reference files:
  • Logging, Async, Fixtures & more: Patterns 6-12 cover specialised testing scenarios, including logging, async, and data validation
  • Test Debugging: Debugging techniques and pytest commands
  • Type-Safe Testing: Protocol-based type checking for mocks
  • Configuration & Dependencies: Coverage reporting, database testing, CI/CD, pytest configuration
  • Testing Anti-Patterns: Common pitfalls to avoid (production pollution, mock testing, etc.)

Testing Best Practices

Test Organisation

python
# tests/
#   __init__.py
#   conftest.py           # Shared fixtures
#   test_unit/            # Unit tests
#   test_integration/     # Integration tests
#   test_e2e/            # End-to-end tests

Test Naming

python
# Good test names describe what is tested and expected outcome
def test_user_creation_with_valid_data():
    pass

def test_login_fails_with_invalid_password():
    pass

# Bad test names
def test_1():  # Not descriptive
    pass

def test_user():  # Too vague
    pass

Test Markers

python
import pytest

@pytest.mark.slow
def test_slow_operation():
    pass

@pytest.mark.integration
def test_database_integration():
    pass

# Run with:
# uv run pytest -m slow          # Run only slow tests
# uv run pytest -m "not slow"    # Skip slow tests

Best Practices Summary

  1. Write tests first (TDD) or alongside code
  2. One assertion per test when possible
  3. Use descriptive test names that explain behaviour
  4. Keep tests independent and isolated
  5. Use fixtures for setup and teardown
  6. Mock external dependencies appropriately
  7. Parametrise tests to reduce duplication
  8. Test edge cases and error conditions
  9. Measure coverage but focus on quality
  10. Run tests in CI/CD on every commit

Quick Reference

  • pytest documentation: https://docs.pytest.org/
  • unittest.mock: https://docs.python.org/3/library/unittest.mock.html
  • hypothesis: Property-based testing library
  • pytest-asyncio: Testing async code
  • pytest-cov: Coverage measurement and reporting
  • pytest-mock: Enhanced mocking with spec support
  • pydantic: Data validation and schema testing
  • pandera: Schema validation for pandas DataFrames