Python Testing

Python Testing Patterns

Comprehensive testing strategies for Python applications using pytest, TDD methodology, and best practices.

Full code examples: references/code-examples.md

When to Activate

• Writing new Python code (follow TDD: red, green, refactor)
• Designing test suites for Python projects
• Reviewing Python test coverage
• Setting up testing infrastructure

Core Testing Philosophy

Test-Driven Development (TDD)

Follow the TDD cycle:

1. Red: Write a failing test for the desired behavior
2. Green: Write minimal code to make the test pass
3. Refactor: Improve code while keeping tests green

# Step 1: Write failing test (Red)
def test_add_numbers():
    result = add(2, 3)
    assert result == 5

# Step 2: Write minimal implementation (Green)
def add(a, b):
    return a + b

# Step 3: Refactor if needed

Coverage Requirements

• Target: 80%+ code coverage
• Critical paths: 100% coverage required

pytest --cov=mypackage --cov-report=term-missing --cov-report=html

pytest Fundamentals

Basic Test Structure

import pytest

def test_addition():
    assert 2 + 2 == 4

def test_string_uppercase():
    assert "hello".upper() == "HELLO"

Assertions

# Exception testing
with pytest.raises(ValueError, match="invalid input"):
    raise ValueError("invalid input provided")

# Check exception attributes
with pytest.raises(ValueError) as exc_info:
    raise ValueError("error message")
assert str(exc_info.value) == "error message"

Fixtures

Fixtures provide reusable test setup. See references/code-examples.md for full examples.

Key Concepts

• Basic: Return test data with @pytest.fixture
• Setup/Teardown: Use yield to split setup and cleanup
• Scopes: function (default), module, session
• Parameters: @pytest.fixture(params=[...]) for multi-run fixtures
• Autouse: autouse=True runs automatically for every test
• Shared: Put shared fixtures in conftest.py

@pytest.fixture
def database():
    db = Database(":memory:")
    db.create_tables()
    yield db
    db.close()

def test_query(database):
    result = database.query("SELECT * FROM users")
    assert len(result) > 0

Parametrization

Run the same test with different inputs:

@pytest.mark.parametrize("a,b,expected", [
    (2, 3, 5),
    (0, 0, 0),
    (-1, 1, 0),
    (100, 200, 300),
])
def test_add(a, b, expected):
    assert add(a, b) == expected

Use ids= for readable test names:

@pytest.mark.parametrize("input,expected", [
    ("valid@email.com", True),
    ("invalid", False),
], ids=["valid-email", "missing-at"])
def test_email_validation(input, expected):
    assert is_valid_email(input) is expected

Markers and Test Selection

Custom Markers

@pytest.mark.slow
def test_slow_operation():
    time.sleep(5)

@pytest.mark.integration
def test_api_integration():
    response = requests.get("https://api.example.com")
    assert response.status_code == 200

Running by Marker

pytest -m "not slow"              # Skip slow tests
pytest -m integration             # Only integration tests
pytest -m "unit and not slow"     # Combine markers

Marker Registration (pytest.ini)

[pytest]
markers =
    slow: marks tests as slow
    integration: marks tests as integration tests
    unit: marks tests as unit tests

Mocking and Patching

Mock external dependencies to keep tests fast and isolated. See references/code-examples.md for all patterns.

Core Patterns

from unittest.mock import patch, Mock

@patch("mypackage.external_api_call")
def test_with_mock(api_call_mock):
    api_call_mock.return_value = {"status": "success"}
    result = my_function()
    api_call_mock.assert_called_once()
    assert result["status"] == "success"

Key mocking techniques:

• return_value — set what the mock returns
• side_effect = Exception(...) — make the mock raise
• autospec=True — catch API misuse
• mock_open — mock file operations
• PropertyMock — mock properties

Testing Async Code

@pytest.mark.asyncio
async def test_async_function():
    result = await async_add(2, 3)
    assert result == 5

Use assert_awaited_once() instead of assert_called_once() for async mocks.

Test Organization

tests/
├── conftest.py           # Shared fixtures
├── unit/                 # Unit tests
│   ├── test_models.py
│   └── test_services.py
├── integration/          # Integration tests
│   └── test_api.py
└── e2e/                  # End-to-end tests
    └── test_user_flow.py

Group related tests in classes:

class TestUserService:
    @pytest.fixture(autouse=True)
    def setup(self):
        self.service = UserService()

    def test_create_user(self):
        user = self.service.create_user("Alice")
        assert user.name == "Alice"

Best Practices

Do

• Follow TDD: write tests before code (red-green-refactor)
• Test one behavior per test
• Use descriptive names: test_user_login_with_invalid_credentials_fails
• Use fixtures to eliminate duplication
• Mock external dependencies (network, database, filesystem)
• Test edge cases: empty inputs, None values, boundary conditions
• Aim for 80%+ coverage on critical paths
• Keep tests fast; use marks to separate slow tests

Avoid

• Testing implementation details instead of behavior
• Complex conditionals in tests
• Ignoring test failures
• Testing third-party library internals
• Sharing mutable state between tests
• Catching exceptions in tests (use pytest.raises instead)
• Over-specific mocks that make tests brittle

pytest Configuration (pyproject.toml)

[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]
python_classes = ["Test*"]
python_functions = ["test_*"]
addopts = [
    "--strict-markers",
    "--cov=mypackage",
    "--cov-report=term-missing",
]
markers = [
    "slow: marks tests as slow",
    "integration: marks tests as integration tests",
    "unit: marks tests as unit tests",
]

Running Tests

pytest                              # Run all tests
pytest tests/test_utils.py          # Run specific file
pytest tests/test_utils.py::test_fn # Run specific test
pytest -v                           # Verbose output
pytest --cov=mypackage              # With coverage
pytest -m "not slow"                # Skip slow tests
pytest -x                           # Stop on first failure
pytest --maxfail=3                  # Stop after N failures
pytest --lf                         # Run last failed
pytest -k "test_user"               # Run by pattern
pytest --pdb                        # Debugger on failure

Quick Reference

Tests are code too — keep them clean, readable, and maintainable.