Python Code Quality

Write high-quality Python code with consistent style, strong type safety, and awareness of common pitfalls. These patterns make codebases maintainable, self-documenting, and easier to debug.

When to Use This Skill

• Setting up linting and formatting for a new project
• Writing or reviewing docstrings
• Establishing team coding standards
• Configuring ruff, mypy, or pyright
• Reviewing code for style consistency
• Adding type hints to existing code
• Creating generic, reusable classes
• Defining structural interfaces with protocols
• Configuring mypy or pyright for strict checking
• Reviewing code before merge
• Debugging mysterious issues
• Refactoring legacy code

Core Concepts

Style — Let tools handle formatting debates. Follow PEP 8 naming. Document public APIs with docstrings.

Type Safety — Type annotations are enforced documentation. Use generics for reusable code. Use protocols for structural interfaces.

Anti-Patterns — Know what to avoid. A checklist review before merging catches most recurring issues.

---

Style & Formatting

Pattern 1: Modern Tooling (ruff)

Use ruff as an all-in-one linter and formatter. It replaces flake8, isort, and black with a single fast tool.

# pyproject.toml
[tool.ruff]
line-length = 120
target-version = "py312"

[tool.ruff.lint]
select = [
    "E",    # pycodestyle errors
    "W",    # pycodestyle warnings
    "F",    # pyflakes
    "I",    # isort
    "B",    # flake8-bugbear
    "C4",   # flake8-comprehensions
    "UP",   # pyupgrade
    "SIM",  # flake8-simplify
]
ignore = ["E501"]  # Line length handled by formatter

[tool.ruff.format]
quote-style = "double"
indent-style = "space"

ruff check --fix .  # Lint and auto-fix
ruff format .       # Format code

Pattern 2: Naming Conventions

Follow PEP 8 with emphasis on clarity over brevity.

# Files and modules: snake_case
user_repository.py
order_processing.py

# Classes: PascalCase (acronyms stay uppercase)
class UserRepository: ...
class HTTPClientFactory: ...

# Functions and variables: snake_case
def get_user_by_email(email: str) -> User | None:
    retry_count = 3

# Module-level constants: SCREAMING_SNAKE_CASE
MAX_RETRY_ATTEMPTS = 3
DEFAULT_TIMEOUT_SECONDS = 30

Pattern 3: Import Organization

Group: standard library → third-party → local. Use absolute imports exclusively.

# Standard library
import os
from collections.abc import Callable
from typing import Any

# Third-party
import httpx
from pydantic import BaseModel

# Local
from myproject.models import User
from myproject.services import UserService

# Preferred: absolute imports
from myproject.utils import retry_decorator

# Avoid: relative imports
from ..utils import retry_decorator

Pattern 4: Google-Style Docstrings

Write docstrings for all public classes, methods, and functions.

# Simple function: one-liner is fine
def get_user(user_id: str) -> User:
    """Retrieve a user by their unique identifier."""
    ...

# Complex function: full format
def process_batch(
    items: list[Item],
    max_workers: int = 4,
    on_progress: Callable[[int, int], None] | None = None,
) -> BatchResult:
    """Process items concurrently using a worker pool.

    Args:
        items: The items to process. Must not be empty.
        max_workers: Maximum concurrent workers. Defaults to 4.
        on_progress: Optional callback receiving (completed, total) counts.

    Returns:
        BatchResult containing succeeded items and any failures.

    Raises:
        ValueError: If items is empty.

    Example:
        >>> result = process_batch(items, max_workers=8)
        >>> print(f"Processed {len(result.succeeded)} items")
    """
    ...

# Class docstring
class UserService:
    """Service for managing user operations.

    Attributes:
        repository: The data access layer for user persistence.

    Example:
        >>> service = UserService(repository, logger)
        >>> user = service.create_user(CreateUserInput(...))
    """

Pattern 5: Line Length and Formatting

120 characters. Let ruff handle it; use logical breaks for readability.

# Multi-line function signatures
def create_user(
    email: str,
    name: str,
    role: UserRole = UserRole.MEMBER,
    notify: bool = True,
) -> User:
    ...

# Method chains
result = (
    db.query(User)
    .filter(User.active == True)
    .order_by(User.created_at.desc())
    .limit(10)
    .all()
)

---

Type Safety

Pattern 6: Annotate All Public Signatures

Every public function, method, and class should have type annotations.

def get_user(user_id: str) -> User:
    ...

def process_batch(
    items: list[Item],
    max_workers: int = 4,
) -> BatchResult[ProcessedItem]:
    ...

class UserRepository:
    def __init__(self, db: Database) -> None:
        self._db = db

    async def find_by_id(self, user_id: str) -> User | None:
        ...

    async def save(self, user: User) -> User:
        ...

Pattern 7: Modern Union Syntax

# Preferred (Python 3.10+)
def find_user(user_id: str) -> User | None: ...
def parse_value(v: str) -> int | float | str: ...

# Older style (needed for Python 3.9)
from typing import Optional, Union
def find_user(user_id: str) -> Optional[User]: ...

Pattern 8: Type Narrowing with Guards

def process_user(user_id: str) -> UserData:
    user = find_user(user_id)
    if user is None:
        raise UserNotFoundError(f"User {user_id} not found")
    # Type checker knows user is User here
    return UserData(name=user.name, email=user.email)

def process_items(items: list[Item | None]) -> list[ProcessedItem]:
    valid_items = [item for item in items if item is not None]
    # valid_items is now list[Item]
    return [process(item) for item in valid_items]

Pattern 9: Generic Classes

from typing import TypeVar, Generic

T = TypeVar("T")
E = TypeVar("E", bound=Exception)

class Result(Generic[T, E]):
    """Represents either a success value or an error."""

    def __init__(self, value: T | None = None, error: E | None = None) -> None:
        if (value is None) == (error is None):
            raise ValueError("Exactly one of value or error must be set")
        self._value = value
        self._error = error

    @property
    def is_success(self) -> bool:
        return self._error is None

    def unwrap(self) -> T:
        if self._error is not None:
            raise self._error
        return self._value  # type: ignore[return-value]

    def unwrap_or(self, default: T) -> T:
        if self._error is not None:
            return default
        return self._value  # type: ignore[return-value]

# Usage preserves types
def parse_config(path: str) -> Result[Config, ConfigError]:
    try:
        return Result(value=Config.from_file(path))
    except ConfigError as e:
        return Result(error=e)

Pattern 10: Generic Repository

from abc import ABC, abstractmethod

T = TypeVar("T")
ID = TypeVar("ID")

class Repository(ABC, Generic[T, ID]):
    @abstractmethod
    async def get(self, id: ID) -> T | None: ...

    @abstractmethod
    async def save(self, entity: T) -> T: ...

    @abstractmethod
    async def delete(self, id: ID) -> bool: ...

class UserRepository(Repository[User, str]):
    async def get(self, id: str) -> User | None:
        row = await self._db.fetchrow("SELECT * FROM users WHERE id = $1", id)
        return User(**row) if row else None

    async def save(self, entity: User) -> User: ...
    async def delete(self, id: str) -> bool: ...

Pattern 11: TypeVar with Bounds

from pydantic import BaseModel

ModelT = TypeVar("ModelT", bound=BaseModel)

def validate_and_create(model_cls: type[ModelT], data: dict) -> ModelT:
    return model_cls.model_validate(data)

# Works with any BaseModel subclass; type error for non-BaseModel types
user = validate_and_create(User, {"name": "Alice", "email": "a@b.com"})

Pattern 12: Protocols for Structural Typing

from typing import Protocol, runtime_checkable

@runtime_checkable
class Serializable(Protocol):
    def to_dict(self) -> dict: ...
    @classmethod
    def from_dict(cls, data: dict) -> "Serializable": ...

# User satisfies Serializable without inheriting from it
class User:
    def to_dict(self) -> dict:
        return {"id": self.id, "name": self.name}

    @classmethod
    def from_dict(cls, data: dict) -> "User":
        return cls(id=data["id"], name=data["name"])

def serialize(obj: Serializable) -> str:
    return json.dumps(obj.to_dict())

# Common protocol patterns
class Closeable(Protocol):
    def close(self) -> None: ...

class AsyncCloseable(Protocol):
    async def close(self) -> None: ...

class HasId(Protocol):
    @property
    def id(self) -> str: ...

Pattern 13: Type Aliases and Callable Types

# Python 3.10+ simple aliases
type UserId = str
type UserDict = dict[str, Any]

# Python 3.9-3.11 style
from typing import TypeAlias
UserId: TypeAlias = str

# Callable types
from collections.abc import Callable, Awaitable

ProgressCallback = Callable[[int, int], None]  # (current, total)
AsyncHandler = Callable[[Request], Awaitable[Response]]

# With named parameters (use Protocol)
class OnProgress(Protocol):
    def __call__(self, current: int, total: int, *, message: str = "") -> None: ...

Type Checker Configuration

# pyproject.toml - mypy strict mode
[tool.mypy]
python_version = "3.12"
strict = true
warn_return_any = true
warn_unused_ignores = true
disallow_untyped_defs = true
no_implicit_optional = true

[[tool.mypy.overrides]]
module = "tests.*"
disallow_untyped_defs = false

# Alternative: pyright
[tool.pyright]
pythonVersion = "3.12"
typeCheckingMode = "strict"

For existing codebases, enable strict mode per-module incrementally.

---

Anti-Patterns Checklist

Infrastructure Anti-Patterns

Scattered Timeout/Retry Logic

# BAD: Duplicated everywhere
def fetch_user(user_id):
    try:
        return requests.get(url, timeout=30)
    except Timeout:
        return None

# GOOD: Centralized
@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 retries!

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

Hard-Coded Configuration

# BAD
DB_HOST = "prod-db.example.com"
API_KEY = "sk-12345"

# GOOD
from pydantic_settings import BaseSettings
class Settings(BaseSettings):
    db_host: str = Field(alias="DB_HOST")
    api_key: str = Field(alias="API_KEY")

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)

# 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)
    if len(orders) > 10:
        return 0.15
    return 0.0

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

Error Handling Anti-Patterns

Bare Exception Handling

# BAD: Silent failure
try:
    process()
except Exception:
    pass  # Bugs hidden forever

# GOOD
try:
    process()
except ConnectionError as e:
    logger.warning("Connection failed", error=str(e))
    raise
except ValueError as 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 → batch aborted
        results.append(result)
    return results

# 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
def create_user(data: dict):
    return User(**data)  # Crashes deep in code on bad input

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

Resource Anti-Patterns

Unclosed Resources

# BAD
def read_file(path):
    f = open(path)
    return f.read()  # Never closed if this raises

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

Blocking in Async

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

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

Type Safety Anti-Patterns

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

def get_users() -> list:
    ...

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

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

Testing Anti-Patterns

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

# GOOD: Tests error conditions too
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)

# BAD: Over-mocking
def test_user_service():
    mock_repo = Mock()
    mock_cache = Mock()
    mock_logger = 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 in decorators)
• No double retry (app + infrastructure layer)
• 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 at boundaries
• No unclosed resources (use context managers)
• No blocking calls in async code
• All public functions have type hints
• Collections have type parameters (list[str] not list)
• 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	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

---

Best Practices Summary

Style

1. Use ruff — Single tool for linting and formatting
2. 120 character lines — Modern standard
3. Descriptive names — Clarity over brevity, no abbreviations
4. Absolute imports — More maintainable than relative
5. Google-style docstrings — For all public APIs
6. Automate in CI — Run ruff and mypy on every commit

Type Safety 7. Annotate all public APIs — Functions, methods, class attributes 8. Use T | None — Modern union syntax over Optional[T] 9. Run strict mypy — mypy --strict in CI 10. Use generics — Preserve type info in reusable code 11. Define protocols — Structural typing without inheritance 12. Minimize Any — Use specific types or generics

Anti-Patterns 13. Run the checklist before merge — Catch recurring issues early 14. Test error paths — Verify exceptions are raised correctly 15. Use context managers — For all resource management