python-anti-patterns

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.

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

Core Concepts

1. Centralize Cross-Cutting Concerns

Timeouts, retries, and configuration should live in one place, not scattered across every call site.

2. Separate Layers

Keep I/O, business logic, and API concerns in distinct layers. Don't mix SQL into business functions or leak ORM models to API consumers.

3. Handle Failures Explicitly

Catch specific exceptions, preserve partial results in batch operations, and validate inputs at boundaries.

4. Use the Type System

Annotate all public functions, use typed collections, and let static analysis catch bugs before runtime.

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}...")