python-anti-patterns

name python-anti-patterns description Use this skill when reviewing Python code for common anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when debugging issues that might stem from known bad practices. 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