Python Testing Patterns

Python Testing Patterns

Comprehensive guide to implementing robust testing strategies in Python using pytest, fixtures, mocking, parameterization, and property-based testing.

When to Use This Skill

• Writing unit tests for Python functions and classes
• Setting up comprehensive test suites and infrastructure
• Implementing test-driven development (TDD) workflows
• Creating integration tests for APIs, databases, and services
• Mocking external dependencies and third-party services
• Testing async code and concurrent operations
• Implementing property-based testing with Hypothesis
• Setting up CI/CD test automation
• Debugging failing tests and improving test coverage

Core Concepts

Test Discovery: Files matching test_*.py or *_test.py, functions starting with test_

Fixtures: Reusable test resources with setup and teardown

• Scopes: function (default), class, module, session
• Composition: Build complex fixtures from simple ones
• Share via conftest.py for project-wide availability

Assertions: Use assert statements, pytest.raises() for exceptions

Organization: Separate unit/, integration/, e2e/ directories

Quick Reference

Load detailed references for specific topics:

Workflow

1. Basic Test Setup

# test_example.py
import pytest

def test_something():
    """Descriptive test name."""
    # Arrange
    expected = 5

    # Act
    result = 2 + 3

    # Assert
    assert result == expected

Run tests:

pytest                    # Run all tests
pytest -v                 # Verbose output
pytest tests/unit/        # Specific directory
pytest -k "test_user"     # Match pattern
pytest -m unit            # Run marked tests

2. Using Fixtures

@pytest.fixture
def sample_data():
    """Provide test data."""
    data = {"key": "value"}
    yield data
    # Cleanup if needed

def test_with_fixture(sample_data):
    assert sample_data["key"] == "value"

3. Parametrized Tests

@pytest.mark.parametrize("input,expected", [
    (2, 4),
    (3, 9),
    (4, 16),
])
def test_square(input, expected):
    assert input ** 2 == expected

4. Mocking External Dependencies

from unittest.mock import patch

@patch("module.external_api_call")
def test_with_mock(mock_api):
    mock_api.return_value = {"status": "ok"}

    result = my_function()

    assert result["status"] == "ok"
    mock_api.assert_called_once()

5. Coverage Measurement

pytest --cov=src --cov-report=term-missing
pytest --cov=src --cov-report=html
pytest --cov=src --cov-fail-under=80

6. Test Configuration

pytest.ini:

[pytest]
testpaths = tests
python_files = test_*.py
addopts = -v --strict-markers --cov=src
markers =
    unit: Unit tests
    integration: Integration tests
    slow: Slow tests

Common Patterns

Exception testing:

with pytest.raises(ValueError, match="error message"):
    function_that_raises()

Async testing:

@pytest.mark.asyncio
async def test_async_function():
    result = await async_operation()
    assert result is not None

Temporary files:

def test_file_operation(tmp_path):
    test_file = tmp_path / "test.txt"
    test_file.write_text("content")
    assert test_file.read_text() == "content"

Markers for test selection:

@pytest.mark.slow
@pytest.mark.integration
def test_database_operation():
    pass

Common Mistakes

1. Not using fixtures: Repeating setup code across tests

   • Solution: Create fixtures in conftest.py

2. Tests depending on order: Global state pollution

   • Solution: Ensure test independence with proper fixtures

3. Over-mocking: Mocking internal implementation

   • Solution: Mock only external boundaries (APIs, databases)

4. Missing edge cases: Only testing happy path

   • Solution: Test boundary conditions, errors, and invalid inputs

5. Slow tests: Running full integration tests frequently

   • Solution: Separate unit/integration, use markers, optimize fixtures

6. Ignoring coverage gaps: Not measuring test coverage

   • Solution: Use pytest-cov and track metrics

7. Poor test names: Generic names like test_1()

   • Solution: Use descriptive names: test_<behavior>_<condition>_<expected>

8. No cleanup: Resources not released

   • Solution: Use fixtures with proper teardown (yield pattern)

Resources

• pytest: https://docs.pytest.org/
• unittest.mock: https://docs.python.org/3/library/unittest.mock.html
• pytest-asyncio: Testing async code
• pytest-cov: Coverage reporting
• pytest-mock: pytest wrapper for mock
• Hypothesis: https://hypothesis.readthedocs.io/
• pytest-xdist: Parallel test execution
• testcontainers: Docker containers for testing

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