The One Refactor That Cleaned Up 80% of My Python Code

One simple shift that made my Python code easier to read, maintain, and scale.

The One Refactor That Cleaned Up 80% of My Python Code

We spend so much time learning what to code in Python — data structures, libraries, frameworks — but very little time on how to structure that code cleanly.

A year ago, I found myself staring at Python files that were functional… but painful. Functions sprawled across hundreds of lines, repeated logic everywhere, and a creeping feeling that this code shouldn’t be this hard to manage.

I didn’t need a new framework. I needed a new mindset.

That’s when I discovered the one refactor that transformed how I write Python:

Decoupling business logic from side effects using pure functions.

This isn’t just about writing “functional-style” code. It’s about writing clean, testable, composable code that actually scales.

Let’s break down exactly what this means — and how it cleaned up 80% of my Python codebase.

What I Used to Do (and Why It Hurt)

Before this refactor, most of my functions looked like this:

def send_welcome_email(user_id): 
    user = db.get_user(user_id) 
    if user and not user.has_signed_in: 
        email = generate_email(user) 
        smtp.send(email)

At first glance, this looks fine. It gets the job done.

But here’s what’s wrong:

• Logic and effects are mixed together.
  It’s fetching from the DB, checking conditions, generating content, and triggering an external service — all in one go.
• Hard to test.
  Want to test the logic of generate_email? Good luck mocking everything else.
• Zero reusability.
  If you need the welcome email content elsewhere (say, in a Slack notification), you’ll probably reimplement or duplicate it.

The Refactor That Changed Everything

Here’s what the refactored version looks like:

def get_user_status(user): 
    return not user.has_signed_in 
 
def create_welcome_email(user): 
    return { 
        "to": user.email, 
        "subject": "Welcome!", 
        "body": f"Hello {user.name}, thanks for joining us!" 
    } 
 
def send_email(email): 
    smtp.send(email) 
 
# Main orchestration 
def process_new_user(user_id): 
    user = db.get_user(user_id) 
    if get_user_status(user): 
        email = create_welcome_email(user) 
        send_email(email)

What’s Changed?

• Each function does one thing.
  get_user_status checks logic. create_welcome_email returns data. send_email triggers the effect. They’re independent.
• Testing is trivial.
  Want to test the email content? Just call create_welcome_email() with a mock user—no mocks or side effects needed.
• Reusable & composable.
  Now I can reuse create_welcome_email anywhere—API response, UI preview, logging—you name it.

This is the power of separating pure logic from side effects.

What Are Pure Functions (And Why Do They Matter)?

A pure function is a function that:

• Depends only on its input.
• Produces no side effects (like writing to a file, printing, or hitting a network).
• Always returns the same output for the same input.

Why they’re awesome:

• Testable. No mocks. No flaky tests. Just input and output.
• Composable. Small building blocks are easier to combine into bigger systems.
• Predictable. Less debugging, fewer hidden surprises.

In contrast, side-effect-heavy code tends to be harder to reason about. You tweak one thing, and something breaks three functions away.

The 80/20 Principle Applied to Python Code

You don’t need to go full-on functional programming. This isn’t about writing Haskell in Python.

Instead, it’s about applying the 80/20 rule:

If you extract pure functions for 80% of your core logic, your code becomes 10x easier to work with.

Here’s where I now always separate logic from effects:

• Data validation
• Business rules
• Email generation
• Report formatting
• Calculations and scoring logic
• Transforming API responses

Each of these lives in pure, testable functions. Then orchestration layers (handlers, views, scripts) call them and handle the side effects.

Real-World Example: From Messy to Maintainable

Let’s take a messier real-world scenario.

Before: The All-in-One Function

def handle_order_submission(order_data): 
    if not validate_order(order_data): 
        raise ValueError("Invalid order") 
 
    db.save_order(order_data) 
    invoice = generate_invoice(order_data) 
    pdf = create_pdf(invoice) 
    send_email_with_attachment(order_data["user_email"], pdf)

Looks fine, but it’s tightly coupled.

After: Modular and Clean

def is_order_valid(order_data): 
    # pure logic 
    return ...  
 
def build_invoice(order_data): 
    # pure logic 
    return ... 
 
def generate_invoice_pdf(invoice): 
    # pure logic 
    return ... 
 
def send_invoice_email(email, pdf): 
    smtp.send(...) 
 
def process_order(order_data): 
    if not is_order_valid(order_data): 
        raise ValueError("Invalid order") 
 
    db.save_order(order_data) 
    invoice = build_invoice(order_data) 
    pdf = generate_invoice_pdf(invoice) 
    send_invoice_email(order_data["user_email"], pdf)

Now:

• I can reuse build_invoice for downloading, previewing, printing, or exporting.
• I can write unit tests for the core logic without touching the DB or SMTP.
• I can swap out smtp.send() later with no impact on the logic.

This structure has saved me countless debugging hours and made onboarding new devs a breeze.

The Takeaway: Think in Layers

The secret to writing clean Python code isn’t some obscure design pattern.

It’s this mindset:

Pure functions are your business logic. Side effects are your shell. Keep them apart.

Think in three layers:

1. Core logic: Pure, reusable, and easily testable.
2. Orchestration: Combines logic and decides when to trigger effects.
3. Side effects: The actual I/O — DB calls, API hits, email sends.

By refactoring code into these layers, your Python codebase becomes:

• Easier to test
• Easier to scale
• Easier to refactor later

One Final Thought

This one refactor — extracting pure functions — has cleaned up at least 80% of my code. Not just in lines of code, but in clarity and sanity.

It’s simple, but powerful. And once you see your code this way, it’s hard to go back.

If you’ve been fighting messy functions and unpredictable bugs, try this one change. You might never look at Python the same way again.

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