Python Clean Code

Python Clean Code

Line- and function-level readability: naming, classes-vs-functions, comments, data types, plus DRY/KISS/YAGNI.

Activation Contract

Apply when code is hard to read, a function does too much, names are cryptic, comments restate code, primitives model structured data, or the user asks to refactor/clean up Python. For class/module structure and SOLID, use python-design-principles instead.

Hard Rules

• Name by intent: verb_noun for functions, is_/has_ for booleans, named constants for magic numbers. A good name removes the need for a comment.
• Delete comments that restate code or hold dead code. Keep only why (rationale) and docstrings.
• Use a function, not a class, when there is no state to hold. Use @dataclass/Enum/NamedTuple instead of tuples or raw dicts for structured data.
• Flatten nesting with guard clauses (early return). Extract long functions into named steps.
• Replace repeated blocks with one parameterized abstraction (DRY): but only after the third repeat, never speculative (YAGNI). Prefer the simplest construct that works (KISS).

Decision Gates

Execution Steps

1. Read the smell; name which gate above it hits.
2. Show a tight # Bad → # Good diff: change one smell at a time.
3. Keep behavior identical; if tests exist, they must stay green.
4. Stop at KISS: do not introduce abstraction the current code does not need.

# Bad
d = 86400
def process(u):
    if u:
        if u.active:
            return charge(u)

# Good
SECONDS_IN_DAY = 86400
def charge_if_active(user: User) -> Receipt | None:
    if not user or not user.active:
        return None
    return charge(user)

Output Contract

Return the refactored Python, one smell per diff, with a one-line rationale per change. Never expand scope beyond the smell asked about.

References

• references/naming-and-comments.md: naming and comment rules with examples.
• references/data-and-smells.md: data-type modeling, code smells, DRY/KISS/YAGNI.