python-testing-patterns

name	python-testing-patterns
description	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.

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

# 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: pytest test_example.py

Fundamental Patterns

Pattern 1: Basic pytest Tests

# test_calculator.py
import pytest

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

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

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

    def multiply(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
    assert calc.add(0, 0) == 0


def ():
    
    calc = Calculator()
     calc.subtract(, ) == 
     calc.subtract(, ) == -


 ():
    
    calc = Calculator()
     calc.multiply(, ) == 
     calc.multiply(, ) == 


 ():
    
    calc = Calculator()
     calc.divide(, ) == 
     calc.divide(, ) == 


 ():
    
    calc = Calculator()
     pytest.raises(ValueError, =):
        calc.divide(, )

Pattern 2: Fixtures for Setup and Teardown

# test_database.py
import pytest
from typing import Generator

class Database:
    """Simple database class."""

    def __init__(self, connection_string: str):
        self.connection_string = connection_string
        self.connected = False

    def connect(self):
        """Connect to database."""
        self.connected = True

    def disconnect(self):
        """Disconnect from database."""
        self.connected = False

    def query(self, sql: str) -> list:
        """Execute query."""
        if not self.connected:
            raise RuntimeError("Not connected")
        return [{"id": 1, "name": "Test"}]


@pytest.fixture
def db() -> Generator[Database, None, None]:
    """Fixture that provides connected database."""
    # Setup
    database = Database()
    database.connect()

    
     database

    
    database.disconnect()


 ():
    
    results = db.query()
     (results) == 
     results[][] == 



 ():
    
     {
        : ,
        : ,
        : 
    }



 ():
    
    
    client = {: app_config, : }
     client
    
    client[] = 


 ():
    
     api_client[] == 
     api_client[][]  

Pattern 3: Parameterized Tests

# test_validation.py
import pytest

def is_valid_email(email: str) -> bool:
    """Check if email is valid."""
    return "@" in email and "." in email.split("@")[1]


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


@pytest.mark.parametrize("a,b,expected", [
    (2, 3, 5),
    (0, 0, 0),
    (-1, 1, ),
    (),
    (),
])
 ():
    
     test_calculator  Calculator
    calc = Calculator()
     calc.add(a, b) == expected




 ():
    
     (value > ) == expected

Pattern 4: Mocking with unittest.mock

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

class APIClient:
    """Simple API client."""

    def __init__(self, base_url: str):
        self.base_url = base_url

    def get_user(self, user_id: int) -> dict:
        """Fetch user from API."""
        response = requests.get(f"{self.base_url}/users/{user_id}")
        response.raise_for_status()
        return response.json()

    def create_user(self, data: dict) -> dict:
        """Create new user."""
        response = requests.post(f"{self.base_url}/users", json=data)
        response.raise_for_status()
        return response.json()


def test_get_user_success():
    """Test successful API call with mock."""
    client = APIClient("https://api.example.com")

    mock_response = Mock()
    mock_response.json.return_value = {"id": 1, "name": "John Doe"}
    mock_response.raise_for_status.return_value = None

    with patch("requests.get", return_value=mock_response) as mock_get:
        user = client.get_user()

         user[] == 
         user[] == 
        mock_get.assert_called_once_with()


 ():
    
    client = APIClient()

    mock_response = Mock()
    mock_response.raise_for_status.side_effect = requests.HTTPError()

     patch(, return_value=mock_response):
         pytest.raises(requests.HTTPError):
            client.get_user()



 ():
    
    client = APIClient()

    mock_post.return_value.json.return_value = {: , : }
    mock_post.return_value.raise_for_status.return_value = 

    user_data = {: , : }
    result = client.create_user(user_data)

     result[] == 
    mock_post.assert_called_once()
    call_args = mock_post.call_args
     call_args.kwargs[] == user_data

Pattern 5: Testing Exceptions

# test_exceptions.py
import pytest

def divide(a: float, b: float) -> float:
    """Divide a by b."""
    if b == 0:
        raise ZeroDivisionError("Division by zero")
    if not isinstance(a, (int, float)) or not isinstance(b, (int, float)):
        raise TypeError("Arguments must be numbers")
    return a / b


def test_zero_division():
    """Test exception is raised for division by zero."""
    with pytest.raises(ZeroDivisionError):
        divide(10, 0)


def test_zero_division_with_message():
    """Test exception message."""
    with pytest.raises(ZeroDivisionError, match="Division by zero"):
        divide(5, 0)


def test_type_error():
    """Test type error exception."""
    with pytest.raises(TypeError, match="must be numbers"):
        divide("10", 5)


def ():
    
     pytest.raises(ValueError)  exc_info:
        ()

       (exc_info.value)

For advanced patterns including async testing, monkeypatching, temporary files, conftest setup, property-based testing, database testing, CI/CD integration, and configuration files, see references/advanced-patterns.md

Test Design Principles

One Behavior Per Test

Each test should verify exactly one behavior. This makes failures easy to diagnose and tests easy to maintain.

# BAD - testing multiple behaviors
def test_user_service():
    user = service.create_user(data)
    assert user.id is not None
    assert user.email == data["email"]
    updated = service.update_user(user.id, {"name": "New"})
    assert updated.name == "New"

# GOOD - focused tests
def test_create_user_assigns_id():
    user = service.create_user(data)
    assert user.id is not None

def test_create_user_stores_email():
    user = service.create_user(data)
    assert user.email == data["email"]

def test_update_user_changes_name():
    user = service.create_user(data)
    updated = service.update_user(user.id, {"name": "New"})
    assert updated.name == "New"

Test Error Paths

Always test failure cases, not just happy paths.

def test_get_user_raises_not_found():
    with pytest.raises(UserNotFoundError) as exc_info:
        service.get_user("nonexistent-id")

    assert "nonexistent-id" in str(exc_info.value)

def test_create_user_rejects_invalid_email():
    with pytest.raises(ValueError, match="Invalid email format"):
        service.create_user({"email": "not-an-email"})

Testing Best Practices

Test Organization

# tests/
#   __init__.py
#   conftest.py           # Shared fixtures
#   test_unit/            # Unit tests
#     test_models.py
#     test_utils.py
#   test_integration/     # Integration tests
#     test_api.py
#     test_database.py
#   test_e2e/            # End-to-end tests
#     test_workflows.py

Test Naming Convention

A common pattern: test_<unit>_<scenario>_<expected_outcome>. Adapt to your team's preferences.

# Pattern: test_<unit>_<scenario>_<expected>
def test_create_user_with_valid_data_returns_user():
    ...

def test_create_user_with_duplicate_email_raises_conflict():
    ...

def test_get_user_with_unknown_id_returns_none():
    ...

# Good test names - clear and descriptive
def test_user_creation_with_valid_data():
    """Clear name describes what is being tested."""
    pass

def test_login_fails_with_invalid_password():
    """Name describes expected behavior."""
    pass

def test_api_returns_404_for_missing_resource():
    """Specific about inputs and expected outcomes."""
    pass

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

def test_user():  # Too vague
    pass

def test_function():  # Doesn't explain what's tested
    pass

Testing Retry Behavior

Verify that retry logic works correctly using mock side effects.

from unittest.mock import Mock

def test_retries_on_transient_error():
    """Test that service retries on transient failures."""
    client = Mock()
    # Fail twice, then succeed
    client.request.side_effect = [
        ConnectionError("Failed"),
        ConnectionError("Failed"),
        {"status": "ok"},
    ]

    service = ServiceWithRetry(client, max_retries=3)
    result = service.fetch()

    assert result == {"status": "ok"}
    assert client.request.call_count == 3

def test_gives_up_after_max_retries():
    """Test that service stops retrying after max attempts."""
    client = Mock()
    client.request.side_effect = ConnectionError("Failed")

    service = ServiceWithRetry(client, max_retries=3)

    with pytest.raises(ConnectionError):
        service.fetch()

    assert client.request.call_count == 3

def test_does_not_retry_on_permanent_error():
    """Test that permanent errors are not retried."""
    client = Mock()
    client.request.side_effect = ValueError("Invalid input")

    service = ServiceWithRetry(client, max_retries=3)

    with pytest.raises(ValueError):
        service.fetch()

    # Only called once - no retry for ValueError
    assert client.request.call_count == 1

Mocking Time with Freezegun

Use freezegun to control time in tests for predictable time-dependent behavior.

from freezegun import freeze_time
from datetime import datetime, timedelta

@freeze_time("2026-01-15 10:00:00")
def test_token_expiry():
    """Test token expires at correct time."""
    token = create_token(expires_in_seconds=3600)
    assert token.expires_at == datetime(2026, 1, 15, 11, 0, 0)

@freeze_time("2026-01-15 10:00:00")
def test_is_expired_returns_false_before_expiry():
    """Test token is not expired when within validity period."""
    token = create_token(expires_in_seconds=3600)
    assert not token.is_expired()

@freeze_time("2026-01-15 12:00:00")
def test_is_expired_returns_true_after_expiry():
    """Test token is expired after validity period."""
    token = Token(expires_at=datetime(2026, 1, 15, 11, 30, 0))
    assert token.is_expired()

def test_with_time_travel():
    """Test behavior across time using freeze_time context."""
    with freeze_time("2026-01-01") as frozen_time:
        item = create_item()
        assert item.created_at == datetime(, , )

        
        frozen_time.move_to()
         item.age_days == 

Test Markers

# test_markers.py
import pytest

@pytest.mark.slow
def test_slow_operation():
    """Mark slow tests."""
    import time
    time.sleep(2)


@pytest.mark.integration
def test_database_integration():
    """Mark integration tests."""
    pass


@pytest.mark.skip(reason="Feature not implemented yet")
def test_future_feature():
    """Skip tests temporarily."""
    pass


@pytest.mark.skipif(os.name == "nt", reason="Unix only test")
def test_unix_specific():
    """Conditional skip."""
    pass


@pytest.mark.xfail(reason="Known bug #123")
def test_known_bug():
    """Mark expected failures."""
    assert False


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

Coverage Reporting

# Install coverage
pip install pytest-cov

# Run tests with coverage
pytest --cov=myapp tests/

# Generate HTML report
pytest --cov=myapp --cov-report=html tests/

# Fail if coverage below threshold
pytest --cov=myapp --cov-fail-under=80 tests/

# Show missing lines
pytest --cov=myapp --cov-report=term-missing tests/

For advanced patterns (async testing, monkeypatching, property-based testing, database testing, CI/CD integration, and configuration), see references/advanced-patterns.md