python-anti-patterns

SKILL.md

Python Anti-Patterns Checklist

A reference checklist of common mistakes and anti-patterns in Python code. Review this before finalizing implementations to catch issues early.

When to Use This Skill

Reviewing code before merge

Debugging mysterious issues

Teaching or learning Python best practices

Establishing team coding standards

Refactoring legacy code

Note: This skill focuses on what to avoid. For guidance on positive patterns and architecture, see the python-design-patterns skill.

Infrastructure Anti-Patterns

Scattered Timeout/Retry Logic
# BAD: Timeout logic duplicated everywhere
def fetch_user(user_id):
    try:
        return requests.get(url, timeout=30)
    except Timeout:
        logger.warning("Timeout fetching user")
        return None

def fetch_orders(user_id):
    try:
        return requests.get(url, timeout=30)
    except Timeout:
        logger.warning("Timeout fetching orders")
        return None

Fix: Centralize in decorators or client wrappers.

# GOOD: Centralized retry logic
@retry(stop=stop_after_attempt(3), wait=wait_exponential())
def http_get(url: str) -> Response:
    return requests.get(url, timeout=30)

Double Retry

# BAD: Retrying at multiple layers
@retry(max_attempts=3)  # Application retry
def call_service():
    return client.request()  # Client also has retry configured!

Fix: Retry at one layer only. Know your infrastructure's retry behavior.

Hard-Coded Configuration

# BAD: Secrets and config in code
DB_HOST = "prod-db.example.com"
API_KEY = "sk-12345"

def connect():
    return psycopg.connect(f"host={DB_HOST}...")

Fix: Use environment variables with typed settings.

# GOOD
from pydantic_settings import BaseSettings

class Settings(BaseSettings):
    db_host: str = Field(alias="DB_HOST")
    api_key: str = Field(alias="API_KEY")

settings = Settings()

Architecture Anti-Patterns

Exposed Internal Types

# BAD: Leaking ORM model to API
@app.get("/users/{id}")
def get_user(id: str) -> UserModel:  # SQLAlchemy model
    return db.query(UserModel).get(id)

Fix: Use DTOs/response models.

# GOOD
@app.get("/users/{id}")
def get_user(id: str) -> UserResponse:
    user = db.query(UserModel).get(id)
    return UserResponse.from_orm(user)

Mixed I/O and Business Logic

# BAD: SQL embedded in business logic
def calculate_discount(user_id: str) -> float:
    user = db.query("SELECT * FROM users WHERE id = ?", user_id)
    orders = db.query("SELECT * FROM orders WHERE user_id = ?", user_id)
    # Business logic mixed with data access
    if len(orders) > 10:
        return 0.15
    return 0.0

Fix: Repository pattern. Keep business logic pure.

# GOOD
def calculate_discount(user: User, orders: list[Order]) -> float:
    # Pure business logic, easily testable
    if len(orders) > 10:
        return 0.15
    return 0.0

Error Handling Anti-Patterns

Bare Exception Handling

# BAD: Swallowing all exceptions
try:
    process()
except Exception:
    pass  # Silent failure - bugs hidden forever

Fix: Catch specific exceptions. Log or handle appropriately.

# GOOD
try:
    process()
except ConnectionError as e:
    logger.warning("Connection failed, will retry", error=str(e))
    raise
except ValueError as e:
    logger.error("Invalid input", error=str(e))
    raise BadRequestError(str(e))

Ignored Partial Failures

# BAD: Stops on first error
def process_batch(items):
    results = []
    for item in items:
        result = process(item)  # Raises on error - batch aborted
        results.append(result)
    return results

Fix: Capture both successes and failures.

# GOOD
def process_batch(items) -> BatchResult:
    succeeded = {}
    failed = {}
    for idx, item in enumerate(items):
        try:
            succeeded[idx] = process(item)
        except Exception as e:
            failed[idx] = e
    return BatchResult(succeeded, failed)

Missing Input Validation

# BAD: No validation
def create_user(data: dict):
    return User(**data)  # Crashes deep in code on bad input

Fix: Validate early at API boundaries.

# GOOD
def create_user(data: dict) -> User:
    validated = CreateUserInput.model_validate(data)
    return User.from_input(validated)

Resource Anti-Patterns

Unclosed Resources

# BAD: File never closed
def read_file(path):
    f = open(path)
    return f.read()  # What if this raises?

Fix: Use context managers.

# GOOD
def read_file(path):
    with open(path) as f:
        return f.read()

Blocking in Async

# BAD: Blocks the entire event loop
async def fetch_data():
    time.sleep(1)  # Blocks everything!
    response = requests.get(url)  # Also blocks!

Fix: Use async-native libraries.

# GOOD
async def fetch_data():
    await asyncio.sleep(1)
    async with httpx.AsyncClient() as client:
        response = await client.get(url)

Type Safety Anti-Patterns

Missing Type Hints

# BAD: No types
def process(data):
    return data["value"] * 2

Fix: Annotate all public functions.

# GOOD
def process(data: dict[str, int]) -> int:
    return data["value"] * 2

Untyped Collections

# BAD: Generic list without type parameter
def get_users() -> list:
    ...

Fix: Use type parameters.

# GOOD
def get_users() -> list[User]:
    ...

Testing Anti-Patterns

Only Testing Happy Paths

# BAD: Only tests success case
def test_create_user():
    user = service.create_user(valid_data)
    assert user.id is not None

Fix: Test error conditions and edge cases.

# GOOD
def test_create_user_success():
    user = service.create_user(valid_data)
    assert user.id is not None

def test_create_user_invalid_email():
    with pytest.raises(ValueError, match="Invalid email"):
        service.create_user(invalid_email_data)

def test_create_user_duplicate_email():
    service.create_user(valid_data)
    with pytest.raises(ConflictError):
        service.create_user(valid_data)

Over-Mocking

# BAD: Mocking everything
def test_user_service():
    mock_repo = Mock()
    mock_cache = Mock()
    mock_logger = Mock()
    mock_metrics = Mock()
    # Test doesn't verify real behavior

Fix: Use integration tests for critical paths. Mock only external services.

Quick Review Checklist

Before finalizing code, verify:

 No scattered timeout/retry logic (centralized)

 No double retry (app + infrastructure)

 No hard-coded configuration or secrets

 No exposed internal types (ORM models, protobufs)

 No mixed I/O and business logic

 No bare except Exception: pass

 No ignored partial failures in batches

 No missing input validation

 No unclosed resources (using context managers)

 No blocking calls in async code

 All public functions have type hints

 Collections have type parameters

 Error paths are tested

 Edge cases are covered

Common Fixes Summary

Anti-Pattern
Fix

Scattered retry logic
Centralized decorators

Hard-coded config
Environment variables + pydantic-settings

Exposed ORM models
DTO/response schemas

Mixed I/O + logic
Repository pattern

Bare except
Catch specific exceptions

Batch stops on error
Return BatchResult with successes/failures

No validation
Validate at boundaries with Pydantic

Unclosed resources
Context managers

Blocking in async
Async-native libraries

Missing types
Type annotations on all public APIs

Only happy path tests
Test errors and edge cases