Python Style Guide

You are an expert in Python best practices and code quality. Use this guide when writing or reviewing Python code.

Core Principles

•Readability first: Python prioritizes human readability over machine efficiency
•PEP 8 compliance: Follow the official Python style guide
•Type hints: Use built-in generic types (PEP 585) for Python 3.9+
•Docstrings: Provide PEP 257-compliant documentation for all public functions/classes

Type Annotations (PEP 585)

Preferred - built-in generic types (Python 3.9+):

python

def process_items(items: list[str]) -> dict[str, int]:
    names: set[str] = set()
    pairs: tuple[str, int] = ("key", 42)

Avoid - legacy typing module equivalents:

python

from typing import List, Dict  # Don't use these
def process_items(items: List[str]) -> Dict[str, int]:  # Old style

When to use typing module:

•Types without built-in equivalents: Callable, Iterable, TypeGuard, TypedDict, Literal, Protocol
•Advanced constructs: Union, Optional, ParamSpec, TypeVar

python

from typing import Callable, Iterable, Optional

def transform(data: Iterable[str], mapper: Callable[[str], int]) -> Optional[int]:
    ...

Function Design

Signatures:

python

def fetch_data(url: str, timeout: int = 30, retries: int = 3) -> dict[str, Any]:
    """Fetch data from remote API.

    Args:
        url: Target endpoint URL
        timeout: Connection timeout in seconds
        retries: Number of retry attempts

    Returns:
        Parsed JSON response as dictionary

    Raises:
        TimeoutError: If request exceeds timeout
        ValueError: If response is invalid JSON
    """
    ...

Requirements:

•Descriptive names using snake_case
•Type hints on all parameters and return values
•Docstring immediately after def (PEP 257)
•Handle edge cases explicitly with clear error messages

Code Style (PEP 8)

Line length: Max 79 characters (hard limit 99 for CI)

Indentation: 4 spaces per level (no tabs)

Imports:

python

# 1. Standard library
import os
import sys
from pathlib import Path

# 2. Third-party
import requests
from pydantic import BaseModel

# 3. Local
from meto.agent import Agent
from meto.conf import Settings

Naming conventions:

•snake_case for functions, variables, modules
•PascalCase for classes
•UPPER_CASE for constants
•_leading_underscore for protected/internal

Spacing:

python

# Good
result = func(arg1, arg2)

# Bad
result=func(arg1,arg2)

Function Structure

Break down complexity:

python

# Too complex - refactor
def process_large_file(path: str) -> list[dict]:
    # 50 lines of parsing, validation, transformation...

# Better - separate concerns
def read_file(path: str) -> str: ...
def parse_content(content: str) -> list[dict]: ...
def validate_data(data: list[dict]) -> list[dict]: ...

def process_large_file(path: str) -> list[dict]:
    content = read_file(path)
    parsed = parse_content(content)
    return validate_data(parsed)

Docstrings (PEP 257)

One-line docstring:

python

def calculate_sum(numbers: list[int]) -> int:
    """Return the sum of a list of numbers."""
    return sum(numbers)

Multi-line docstring:

python

def connect_to_database(
    host: str,
    port: int,
    credentials: Credentials,
) -> Connection:
    """Establish a connection to the database server.

    Args:
        host: Database server hostname or IP address
        port: TCP port number for connection
        credentials: Authentication credentials

    Returns:
        Active database connection object

    Raises:
        ConnectionError: If server is unreachable
        AuthError: If credentials are invalid

    Example:
        >>> creds = Credentials(user="admin", key="secret")
        >>> conn = connect_to_database("localhost", 5432, creds)
    """
    ...

Error Handling

Be specific:

python

# Good
try:
    data = json.loads(raw)
except json.JSONDecodeError as e:
    raise ValueError(f"Invalid JSON: {e}") from e

# Bad
try:
    data = json.loads(raw)
except Exception:  # Too broad
    pass

Document edge cases:

python

def divide(numerator: float, denominator: float) -> float:
    """Divide two numbers.

    Raises:
        ZeroDivisionError: If denominator is zero
    """
    if denominator == 0:
        raise ZeroDivisionError("Cannot divide by zero")
    return numerator / denominator

Testing

Critical paths need tests:

python

def test_parse_invalid_json():
    """Test that invalid JSON raises ValueError."""
    with pytest.raises(ValueError):
        parse_content("{invalid json}")

Test edge cases:

•Empty inputs: "", [], None
•Invalid types: wrong types passed to typed functions
•Boundary values: 0, -1, MAX_INT
•Large datasets: performance considerations

Maintainability

Document design decisions:

python

# Using OrderedDict instead of dict for Python 3.6 compatibility
# despite dict being ordered since 3.7
from collections import OrderedDict

Comment external dependencies:

python

# pydantic used for runtime validation (not just type checking)
from pydantic import BaseModel, validator

Why over what:

python

# Pre-compile regex for performance (called in tight loop)
EMAIL_PATTERN = re.compile(r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$")

Review Checklist

When reviewing Python code, verify:

Essential:

• PEP 8 compliant (ruff check)
• Type hints on all public functions
• Docstrings for public functions/classes
• Built-in generic types used (list[str] not List[str])

Important:

• Functions under 20 lines (or refactored)
• Clear exception handling with specific error types
• Edge cases documented and tested
• External dependencies commented

Nice to have:

• Algorithm complexity noted for non-trivial implementations
• Example usage in docstrings
• Type coverage >80%