Clean Code in Python: 10 Rules to Follow in 2025

Messy code is easy to write — clean code makes you a professional. These 10 rules will future-proof your Python skills in 2025 and beyond.

Clean Code in Python: 10 Rules to Follow in 2025

Whether you’re writing a script or scaling an API, these clean code principles will help you write readable, maintainable, and bug-resistant Python.

If you’ve ever returned to your own code after a few months and thought, “Who wrote this mess?” — congrats, you’re officially a developer.

Writing code that works is one thing. Writing clean, maintainable Python code that other humans (including future you) can understand is a whole different game.

As Python continues to evolve, so do our standards. Here are 10 practical rules for writing clean Python code in 2025 — backed by experience, modern tools, and the Pythonic way of doing things.

1. Name Things Like You Mean It

One of the most powerful tools for writing clean code isn’t a fancy library or framework — it’s your choice of names.

When you write code, you’re not just instructing a computer — you’re also communicating with other developers (and with future-you).

If your function is named do_stuff, it tells us nothing. But if it’s named generate_monthly_report, we instantly understand its purpose without reading a single line of its implementation.

Bad Example

def do_stuff(a, b): pass

Good Example

def calculate_discounted_price(original_price, discount_rate): pass

“There are only two hard things in computer science: cache invalidation and naming things.” — Phil Karlton

In 2025, clean Python starts with clear, intentional names.
Your goal? Make your code so readable, your teammate (or future self) won’t need to ask, “What the heck does this do?”

2. Favor Dataclasses Over Dictionaries for Structured Data

In Python, it’s common to represent structured data using dictionaries:

user = { 
    "name": "Alice", 
    "age": 30, 
    "email": "alice@example.com" 
}

This works, but it has several downsides:

• No auto-complete in your editor
• No type safety (you can accidentally write user['nage'])
• Hard to know what fields are expected
• Easy to miss errors (e.g., typos, missing keys)
• No documentation about what this “user” actually is

As your codebase grows, these small issues compound into bugs and confusion.

Use dataclass Instead?

Starting with Python 3.7, we can use the @dataclass decorator to define structured data with less boilerplate and more clarity.

from dataclasses import dataclass 
 
@dataclass 
class User: 
    name: str 
    age: int 
    email: str

Now we can create user objects like this:

user = User(name="Alice", age=30, email="alice@example.com")

In 2025, using raw dictionaries for structured data is like using Notepad for coding. It technically works — but you can do so much better.

Switching to dataclass will make your codebase more maintainable, readable, and safe — and that’s what clean code is all about.

3. Keep Functions Short and Focused

Each function in your code should do one thing, and do it well.

If a function is doing too many things — validating inputs, making network calls, logging errors, transforming data — it becomes:

• Hard to understand
• Difficult to test
• Prone to bugs
• A nightmare to debug later

Core Idea:

One function = one responsibility

Bad Example: One Big Messy Function

def process_order(order): 
    # Validate order 
    if not order["items"]: 
        raise ValueError("Empty order") 
 
    # Apply discounts 
    if order["customer_type"] == "premium": 
        for item in order["items"]: 
            item["price"] *= 0.9 
 
    # Calculate total 
    total = sum(item["price"] for item in order["items"]) 
 
    # Log order 
    print(f"Processed order: {order['id']} - Total: {total}") 
 
    # Send confirmation 
    send_email(order["email"], f"Your total is {total}")

That’s five different responsibilities in one place.

Good Example: Break It Down

Split the logic into small, focused functions:

def validate_order(order): 
    if not order["items"]: 
        raise ValueError("Empty order") 
 
def apply_discounts(order): 
    if order["customer_type"] == "premium": 
        for item in order["items"]: 
            item["price"] *= 0.9 
 
def calculate_total(order): 
    return sum(item["price"] for item in order["items"]) 
 
def log_order(order, total): 
    print(f"Processed order: {order['id']} - Total: {total}") 
 
def send_confirmation_email(order, total): 
    send_email(order["email"], f"Your total is {total}") 
 
def process_order(order): 
    validate_order(order) 
    apply_discounts(order) 
    total = calculate_total(order) 
    log_order(order, total) 
    send_confirmation_email(order, total)

Small, focused functions are the building blocks of clean code.

In 2025, long and complex functions are a red flag.

Clean Python code should look like a series of clear, concise, single-purpose steps — not a jumbled list of everything the program does.

4. Use Type Hints (Seriously, Use Them)

Type hints (also called type annotations) are a feature in Python that let you explicitly state the types of variables, function arguments, and return values.

They don’t change how your code runs, but they:

• Help you and your team understand the code faster
• Let editors/IDEs catch bugs early
• Improve autocomplete and documentation
• Enable static type checking tools like mypy, pyright, or pylance

Without Type Hints

def send_email(to, subject, body): 
    # What types are these? Strings? Lists? Dicts? 
    ...

You have no idea what’s expected. Someone might pass a number to subject by mistake — and you won't know until the app crashes.

With Type Hints

def send_email(to: str, subject: str, body: str) -> bool: 
    ...

Now it’s crystal clear:

• to, subject, and body are strings
• The function returns a bool (True = success, False = failure)

Your IDE can:

• Warn you if you pass the wrong type
• Auto-suggest arguments
• Highlight missing return values

“Type hints are like seatbelts: they don’t slow you down — they keep you safe.”

In 2025, type hints are not optional if you want to write clean, modern Python. They make your code easier to read, easier to debug, and harder to break — and they play nicely with the whole modern Python ecosystem.

5. Say Goodbye to Magic Numbers and Strings

A magic number or magic string is a literal value (a number or string) that’s used directly in your code without explanation — making the code harder to read, understand, and maintain.

They’re called “magic” because there’s no context — the meaning is invisible unless you already know what it’s supposed to do.

Imagine this code:

if status == 3: 
    send_email()

It works. But:

• What does 3 mean? Approved? Rejected? Something else?
• Where are the other possible values?
• What happens if the logic changes in the future?

Similarly:

if user["role"] == "admin": 
    give_access()

Again, it works. But:

• What if you mistype "admin" as "Admin"? It might silently fail.
• What if other roles are added later?

These “magic” values are brittle, error-prone, and not self-documenting.

The Clean Way: Use Constants or Enums

Instead of sprinkling numbers or strings everywhere, give them names.

Example with Constants

ROLE_ADMIN = "admin" 
ROLE_EDITOR = "editor" 
 
if user["role"] == ROLE_ADMIN: 
    give_access()

Example with Enums (Preferred in 2025)

from enum import Enum 
 
class Role(Enum): 
    ADMIN = "admin" 
    EDITOR = "editor" 
    VIEWER = "viewer" 
 
if user["role"] == Role.ADMIN: 
    give_access()

Much better

“Magic belongs in fantasy novels, not your production code.”

Magic numbers and strings make your code look mysterious — but clean code should never rely on mystery.

In modern Python (especially in 2025), use constants or enums to make your intent explicit and your logic robust.

6. Write Tests That Make Sense

A clean codebase isn’t just about the code — it’s about the tests that prove your code works and explain how it should behave.

In 2025, tests are no longer just a safety net — they are a source of truth. They guide refactoring, prevent regressions, and serve as living documentation.

But bad tests? They do the opposite. They confuse, break randomly, and slow down development.

Bad Test Code: Vague and Useless

def test_thing(): 
    assert True

Or even this:

def test_price(): 
    assert calculate_total() >= 0

What’s wrong here?

• It’s unclear what the test is verifying
• There’s no context or setup
• If it fails, you won’t know why
• Doesn’t describe what a “correct” result looks like

Good Test Code: Descriptive and Precise

def test_calculate_total_with_multiple_items(): 
    items = [{"price": 100}, {"price": 200}, {"price": 50}] 
    total = calculate_total(items) 
    assert total == 350

This test:

• Describes exactly what it’s testing
• Has clear input and expected output
• Will fail loudly and clearly if something breaks
• Acts as documentation: “Oh, so calculate_total() sums item prices!"

“If it’s not tested, it’s broken.” — Every developer who’s ever pushed to production at 2AM

In 2025, writing meaningful, readable, reliable tests is a critical part of writing clean Python code. Don’t just write tests to increase coverage — write them to increase confidence and clarity.

7. Avoid Deep Nesting

Deep nesting happens when your code contains multiple levels of if, for, while, or try blocks — one inside another, sometimes 3, 4, or 5 levels deep.

It starts looking like this:

if condition1: 
    if condition2: 
        for item in data: 
            if condition3: 
                if not condition4: 
                    do_something(item)

This is hard to read, hard to follow, and a nightmare to debug.
You have to constantly mentally track which block you’re in.

The Clean Code Solution: Flatten the Structure

Strategy 1: Use Guard Clauses (Early Returns)

Instead of nesting deeply, bail out early when possible.

Bad (Deep Nesting)

def process_user(user): 
    if user: 
        if user.is_active: 
            if not user.is_banned: 
                send_email(user)

Good (Flattened)

def process_user(user): 
    if not user or not user.is_active or user.is_banned: 
        return 
    send_email(user)

Guard clauses eliminate unnecessary indentation. If the function shouldn’t proceed, just return early.

Strategy 2: Break Code Into Smaller Functions

If nesting is caused by too many operations in one place, split the logic.

Bad

def handle_request(request): 
    if request.user: 
        if request.user.is_authenticated: 
            if request.method == "POST": 
                if request.data.get("accept"): 
                    approve_request(request)

Good

def handle_request(request): 
    if not is_valid_request(request): 
        return 
    approve_request(request) 
 
def is_valid_request(request): 
    return ( 
        request.user 
        and request.user.is_authenticated 
        and request.method == "POST" 
        and request.data.get("accept") 
    )

Breaking logic into helper functions increases clarity and reusability.

Strategy 3: Use continue in Loops

Inside loops, deep nesting often comes from too many if checks. Instead, use continue to skip early.

Bad

for user in users: 
    if user.is_active: 
        if not user.is_banned: 
            if user.email_verified: 
                send_email(user)

Good

for user in users: 
    if not user.is_active or user.is_banned or not user.email_verified: 
        continue 
    send_email(user)

“Code should be flat, not nested.” — The Zen of Python

Deep nesting is like quicksand: the deeper you go, the harder it is to escape.
In 2025, clean Python means clear structure, minimal indentation, and maximum readability.

Keep your logic flat and your functions focused — your teammates (and future self) will thank you.

8. Use List Comprehensions Thoughtfully

A list comprehension is a concise way to create a new list by transforming or filtering elements from an existing iterable.

Instead of writing a full for loop:

squares = [] 
for x in range(10): 
    squares.append(x * x)

You can write:

squares = [x * x for x in range(10)]

“Simple is better than complex.” — The Zen of Python

List comprehensions are one of Python’s most elegant tools. But just because you can write something in one line doesn’t mean you should.

In 2025, thoughtful use of comprehensions separates clean, modern Python from messy, unreadable code.

9. Document the “Why”, Not the “What”

Too many developers write comments like this:

# Add 1 to count 
count += 1

This is stating the obvious — the code already tells us what is happening.

But that’s not where comments shine.

Instead, you should write comments that explain why the code exists or behaves a certain way — the context, the reasoning, the decision.

“What” vs “Why” — A Comparison

Commenting the “What” (Useless)

# Multiply price by quantity 
total = price * quantity

We can already see that.

Commenting the “Why” (Useful)

# Multiply by quantity to get total before tax — discount is applied later 
total = price * quantity

Now we understand: Where we are in the logic, what assumptions are being made and why this order matters

“Code tells you how. Comments should tell you why.” — Steve McConnell, Code Complete

In 2025, with intelligent tools and clean syntax, your code is the documentation for what happens.

But only thoughtful comments can explain why it happens that way — and that’s what makes code clean, understandable, and durable.

10. Lint It Like You Mean It

Linting is the process of automatically analyzing your code to find:

• Errors
• Bad practices
• Inconsistent formatting
• Style violations
• Dead or duplicate code

A linter is like a virtual code reviewer — it enforces rules so you don’t have to manually nitpick every line.

Key Tools You Should Use (and Why)

1. black — The Uncompromising Code Formatter

• Opinionated: One style to rule them all
• No configuration means fewer debates
• Formats Python code automatically

black .

Auto-formats long lines

Fixes indentation

Removes trailing commas, spacing issues, etc.

2. ruff — The All-in-One Linter & Fixer (Blazing Fast)

• Built in Rust → super fast
• Replaces: flake8, pylint, pycodestyle, pyflakes, etc.
• Checks code for:
• Unused imports
• Redundant variables
• Bad naming
• Logical errors
• Performance improvements

ruff check . 
ruff format .

In 2025, ruff has basically become the default linter for most Python teams.

3. mypy or pyright — Static Type Checkers

If you’re using type hints (see Rule #4), you’ll want to check them:

mypy . 
# or 
pyright

These tools catch:

• Missing return types
• Incompatible assignments
• Forgotten optional checks (None handling)
• Mismatched function signatures

4. isort — Import Sorter

Keeps your import statements clean and grouped logically:

isort .

Automatically sorts:

• Standard libraries
• Third-party packages
• Local modules

This prevents messy diffs and improves readability at the top of every file.

“Let robots handle code style. Let humans focus on logic.” — Every clean coder in 2025

Linting isn’t about perfection — it’s about consistency and saving time.
You shouldn’t have to think about spacing, commas, or imports ever again.

With black, ruff, isort, and type checkers, you’re not just writing clean code — you’re enforcing it automatically.

Final Thoughts

Clean code isn’t just about aesthetics. It’s about communication. It’s about building systems that are easy to reason about, debug, and extend.

In 2025, Python gives us everything we need — type hints, dataclasses, static analysis, powerful tools. There’s no excuse to write spaghetti anymore.

Write code for humans, not just machines. Your future self (and your team) will thank you.