Python Coding Standards

Core Principles

1. Simplicity: Simple, understandable code
2. Readability: Readability over cleverness
3. Maintainability: Code that's easy to maintain
4. Testability: Code that's easy to test
5. DRY: Don't Repeat Yourself - but don't overdo it

General Rules

• Early Returns: Use early returns to avoid nesting
• Descriptive Names: Meaningful names for variables and functions
• Minimal Changes: Only change relevant code parts
• No Over-Engineering: No unnecessary complexity
• Minimal Comments: Code should be self-explanatory. No redundant comments!

Naming Conventions

Project Structure

myproject/
├── src/
│   ├── __init__.py
│   ├── main.py              # Entry point
│   ├── config.py            # Settings, env vars
│   ├── models.py            # Domain models (dataclasses/Pydantic)
│   ├── schemas.py           # Request/response DTOs
│   ├── services/
│   │   ├── __init__.py
│   │   └── user_service.py  # Business logic
│   └── repositories/
│       ├── __init__.py
│       └── user_repo.py     # Data access
├── tests/
│   ├── __init__.py
│   ├── test_services.py
│   └── test_repositories.py
├── pyproject.toml
└── README.md

Code Style (PEP 8 + PEP 484)

from dataclasses import dataclass

@dataclass
class User:
    id: str
    name: str
    email: str
    age: int | None = None  # Python 3.10+ union syntax

def get_user_by_id(user_id: str) -> User | None:
    if not user_id:
        raise ValueError("user_id cannot be empty")
    # implementation...

Best Practices

# Type hints everywhere
def process_items(items: list[str]) -> dict[str, int]:
    return {item: len(item) for item in items}

# Pydantic v2 for validation
from pydantic import BaseModel, Field, field_validator, EmailStr

class UserCreate(BaseModel):
    name: str = Field(..., min_length=2, max_length=50)
    email: EmailStr
    age: int | None = Field(None, ge=0, le=150)

    @field_validator('name')
    @classmethod
    def name_must_be_alphanumeric(cls, v: str) -> str:
        if not v.replace(' ', '').isalnum():
            raise ValueError('Name must be alphanumeric')
        return v.strip()

# Context managers
with open('file.txt', 'r') as f:
    content = f.read()

# Prefer pathlib over os.path
from pathlib import Path
config_path = Path(__file__).parent / 'config.yaml'

Async/Await

# Async function with proper typing
async def fetch_user(user_id: str) -> User | None:
    async with httpx.AsyncClient() as client:
        response = await client.get(f"/users/{user_id}")
        return User(**response.json()) if response.status_code == 200 else None

# Don't block async functions
async def process_data():
    # BAD - blocks the event loop
    time.sleep(1)

    # GOOD - async sleep
    await asyncio.sleep(1)

# Gather for concurrent operations
async def fetch_all_users(user_ids: list[str]) -> list[User]:
    tasks = [fetch_user(uid) for uid in user_ids]
    return await asyncio.gather(*tasks)

Exception Handling

# Custom exceptions for domain errors
class UserNotFoundError(Exception):
    def __init__(self, user_id: str):
        self.user_id = user_id
        super().__init__(f"User not found: {user_id}")

# Raise vs Return None
def get_user_strict(user_id: str) -> User:
    """Raises if not found - use when user MUST exist."""
    user = repository.get(user_id)
    if not user:
        raise UserNotFoundError(user_id)
    return user

def get_user_optional(user_id: str) -> User | None:
    """Returns None if not found - use when absence is expected."""
    return repository.get(user_id)

Comments - Less is More

# BAD - redundant comment
# Get the user from database
user = repository.get_user(user_id)

# GOOD - self-explanatory code, no comment needed
user = repository.get_user(user_id)

# GOOD - comment explains WHY (not obvious)
# Rate limit: Azure API allows max 1000 requests/min
await rate_limiter.acquire()

Recommended Tooling

Production Best Practices

1. Type hints everywhere - Parameters, return types, variables where helpful
2. Pydantic for validation - Don't validate manually, use Pydantic models
3. Async for I/O - Use async/await for network, database, file operations
4. No blocking in async - Never use time.sleep() or sync I/O in async functions
5. Structured logging - Use logging module with JSON format, not print()
6. Environment variables - Use pydantic-settings for config, never hardcode secrets
7. Dependency injection - Pass dependencies explicitly, makes testing easier
8. Custom exceptions - Domain-specific errors, not generic Exception
9. Connection pooling - Reuse database/HTTP connections, don't create per request
10. Graceful shutdown - Handle SIGTERM, close connections properly