Python Anti-Patterns Checklist

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

Infrastructure Anti-Patterns

Scattered Timeout/Retry Logic

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# 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!

Hard-Coded Configuration

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# 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

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# 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

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# 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

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# 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

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# 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

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# 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

Quick Review Checklist

• 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