pytest-patterns

What this skill does

# Pytest Patterns - Comprehensive Testing Guide

A comprehensive skill for mastering Python testing with pytest. This skill covers everything from basic test structure to advanced patterns including fixtures, parametrization, mocking, test organization, coverage analysis, and CI/CD integration.

## When to Use This Skill

Use this skill when:

- Writing tests for Python applications (web apps, APIs, CLI tools, libraries)
- Setting up test infrastructure for a new Python project
- Refactoring existing tests to be more maintainable and efficient
- Implementing test-driven development (TDD) workflows
- Creating fixture patterns for database, API, or external service testing
- Organizing large test suites with hundreds or thousands of tests
- Debugging failing tests or improving test reliability
- Setting up continuous integration testing pipelines
- Measuring and improving code coverage
- Writing integration, unit, or end-to-end tests
- Testing async Python code
- Mocking external dependencies and services

## Core Concepts

### What is pytest?

pytest is a mature, full-featured Python testing framework that makes it easy to write simple tests, yet scales to support complex functional testing. It provides:

- **Simple syntax**: Use plain `assert` statements instead of special assertion methods
- **Powerful fixtures**: Modular, composable test setup and teardown
- **Parametrization**: Run the same test with different inputs
- **Plugin ecosystem**: Hundreds of plugins for extended functionality
- **Detailed reporting**: Clear failure messages and debugging information
- **Test discovery**: Automatic test collection following naming conventions

### pytest vs unittest

```python
# unittest (traditional)
import unittest

class TestMath(unittest.TestCase):
    def test_addition(self):
        self.assertEqual(2 + 2, 4)

# pytest (simpler)
def test_addition():
    assert 2 + 2 == 4
```

### Test Discovery Rules

pytest automatically discovers tests by following these conventions:

1. **Test files**: `test_*.py` or `*_test.py`
2. **Test functions**: Functions prefixed with `test_`
3. **Test classes**: Classes prefixed with `Test` (no `__init__` method)
4. **Test methods**: Methods prefixed with `test_` inside Test classes

## Fixtures - The Heart of pytest

### What are Fixtures?

Fixtures provide a fixed baseline for tests to run reliably and repeatably. They handle setup, provide test data, and perform cleanup.

### Basic Fixture Pattern

```python
import pytest

@pytest.fixture
def sample_data():
    """Provides sample data for testing."""
    return {"name": "Alice", "age": 30}

def test_data_access(sample_data):
    assert sample_data["name"] == "Alice"
    assert sample_data["age"] == 30
```

### Fixture Scopes

Fixtures can have different scopes controlling how often they're created:

- **function** (default): Created for each test function
- **class**: Created once per test class
- **module**: Created once per test module
- **package**: Created once per test package
- **session**: Created once per test session

```python
@pytest.fixture(scope="session")
def database_connection():
    """Database connection created once for entire test session."""
    conn = create_db_connection()
    yield conn
    conn.close()  # Cleanup after all tests

@pytest.fixture(scope="module")
def api_client():
    """API client created once per test module."""
    client = APIClient()
    client.authenticate()
    yield client
    client.logout()

@pytest.fixture  # scope="function" is default
def temp_file():
    """Temporary file created for each test."""
    import tempfile
    f = tempfile.NamedTemporaryFile(mode='w', delete=False)
    yield f.name
    os.unlink(f.name)
```

### Fixture Dependencies

Fixtures can depend on other fixtures, creating a dependency graph:

```python
@pytest.fixture
def database():
    db = Database()
    db.connect()
    yield db
    db.disconnect()

@pytest.fixture
def user_repository(database):
    """Depends on database fixture."""
    return UserRepository(database)

@pytest.fixture
def sample_user(user_repository):
    """Depends on user_repository, which depends on database."""
    user = user_repository.create(name="Test User")
    yield user
    user_repository.delete(user.id)

def test_user_operations(sample_user):
    """Uses sample_user fixture (which uses user_repository and database)."""
    assert sample_user.name == "Test User"
```

### Autouse Fixtures

Fixtures that run automatically without being explicitly requested:

```python
@pytest.fixture(autouse=True)
def reset_database():
    """Runs before every test automatically."""
    clear_database()
    seed_test_data()

@pytest.fixture(autouse=True, scope="session")
def configure_logging():
    """Configure logging once for entire test session."""
    import logging
    logging.basicConfig(level=logging.DEBUG)
```

### Fixture Factories

Fixtures that return functions for creating test data:

```python
@pytest.fixture
def make_user():
    """Factory fixture for creating users."""
    users = []

    def _make_user(name, email=None):
        user = User(name=name, email=email or f"{name}@example.com")
        users.append(user)
        return user

    yield _make_user

    # Cleanup all created users
    for user in users:
        user.delete()

def test_multiple_users(make_user):
    user1 = make_user("Alice")
    user2 = make_user("Bob", email="[email protected]")
    assert user1.name == "Alice"
    assert user2.email == "[email protected]"
```

## Parametrization - Testing Multiple Cases

### Basic Parametrization

Run the same test with different inputs:

```python
import pytest

@pytest.mark.parametrize("input_value,expected", [
    (2, 4),
    (3, 9),
    (4, 16),
    (5, 25),
])
def test_square(input_value, expected):
    assert input_value ** 2 == expected
```

### Multiple Parameters

```python
@pytest.mark.parametrize("x", [0, 1])
@pytest.mark.parametrize("y", [2, 3])
def test_combinations(x, y):
    """Runs 4 times: (0,2), (0,3), (1,2), (1,3)."""
    assert x < y
```

### Parametrizing with IDs

Make test output more readable:

```python
@pytest.mark.parametrize("test_input,expected", [
    pytest.param("3+5", 8, id="addition"),
    pytest.param("2*4", 8, id="multiplication"),
    pytest.param("10-2", 8, id="subtraction"),
])
def test_eval(test_input, expected):
    assert eval(test_input) == expected

# Output:
# test_eval[addition] PASSED
# test_eval[multiplication] PASSED
# test_eval[subtraction] PASSED
```

### Parametrizing Fixtures

Create fixture instances with different values:

```python
@pytest.fixture(params=["mysql", "postgresql", "sqlite"])
def database_type(request):
    """Test runs three times, once for each database."""
    return request.param

def test_database_connection(database_type):
    conn = connect_to_database(database_type)
    assert conn.is_connected()
```

### Combining Parametrization and Marks

```python
@pytest.mark.parametrize("test_input,expected", [
    ("[email protected]", True),
    ("invalid-email", False),
    pytest.param("edge@case", True, marks=pytest.mark.xfail),
    pytest.param("[email protected]", True, marks=pytest.mark.slow),
])
def test_email_validation(test_input, expected):
    assert is_valid_email(test_input) == expected
```

### Indirect Parametrization

Pass parameters through fixtures:

```python
@pytest.fixture
def database(request):
    """Create database based on parameter."""
    db_type = request.param
    db = Database(db_type)
    db.connect()
    yield db
    db.close()

@pytest.mark.parametrize("database", ["mysql", "postgres"], indirect=True)
def test_database_operations(database):
    """database fixture receives the parameter value."""
    assert database.is_connected()
    database.execute("SELECT 1")
```

## Mocking and Monkeypatching

### Using pytest's monkeypatch

The `monkeypatch` fixture provides safe patching that's automatically undone:

```python
def test_get_user_env(monkeypatch):
    """Test env