The Real Reason 80% of Python Code Is Hard to Maintain

Most Python devs are solving the wrong problem — and it shows.

The Real Reason 80% of Python Code Is Hard to Maintain

It’s not about indentation, design patterns, or even testing. The core issue is deeper — and it’s killing productivity in Python projects everywhere.

Python is famous for its readability.
Clean syntax, no semicolons, indentation-as-structure — it practically begs you to write elegant code.

And yet, walk into any real-world Python codebase and it won’t take long to find:

• functions doing way too much
• vague variable names like data, temp, or obj
• side effects hidden in utility files
• inconsistent use of classes and types
• duplicated logic sprinkled across modules

If Python is designed to be readable, why is so much Python code such a pain to maintain?

The real reason isn’t what you think.

The Problem Isn’t the Language — It’s the Way We Use It

Here’s the uncomfortable truth:

Most Python codebases are hard to maintain because they lack clear boundaries of responsibility.

Not just at the module or class level — but at the level of intent.

Let me explain.

In Python, it’s easy to start coding. You don’t need to declare types. You don’t need to set up interfaces. You don’t even need to define a class. You just write:

def do_thing(x): 
    # magic 
    return something

And it works. Until it doesn’t.

Python’s Flexibility Is a Double-Edged Sword

Python’s dynamic nature gives you a lot of freedom. But that freedom makes it too easy to:

• Mix I/O logic with business rules
• Write monolithic scripts that grow uncontrollably
• Pass around dictionaries instead of clear data structures
• Skip boundaries between layers of code (e.g. domain vs. infrastructure)
• Delay decisions about architecture… forever

The result? Code that’s hard to test, debug, or change safely.

Symptoms of Poorly Structured Python Code

You’ve likely encountered these signs:

1. Utility Hell

A giant utils.py or helpers.py file where miscellaneous logic goes to die.

# utils.py 
def clean(data): 
    # ???? 
    pass 
 
def transform(x): 
    # ???? 
    pass

There’s no domain context — just functions that take things and do things. Future-you (or worse, someone else) has to guess what they were for.

2. Overuse of Dictionaries Instead of Real Models

def process(order: dict): 
    if order.get("status") == "shipped": 
        # ...

This works… until you misspell a key or forget what structure was expected. No IDE help. No type hints. Just duct tape.

3. Spaghetti Imports

Files import each other cyclically, violating separation of concerns. Refactoring anything breaks everything.

4. Side Effects Everywhere

Functions that quietly modify global state, read from disk, or make HTTP calls — all without warning.

The Real Solution: Think in Layers, Not Scripts

Here’s the shift that changes everything:

Don’t think in terms of files and functions. Think in terms of responsibilities and boundaries.

Your code should reflect how the system works conceptually, not just what you need it to do today.

A simple pattern to follow is:

Layer 1: Domain Layer (Business Logic)

Pure, testable functions and classes that model the core of your application. No external dependencies.

Example:

@dataclass 
class Order: 
    id: str 
    status: str 
 
    def mark_shipped(self): 
        if self.status != "paid": 
            raise ValueError("Can't ship unpaid order") 
        self.status = "shipped"

Layer 2: Application Layer (Orchestration)

Functions that coordinate domain logic, handle workflows, and call services.

def ship_order(order_id): 
    order = order_repo.get(order_id) 
    order.mark_shipped() 
    order_repo.save(order) 
    send_email(order)

Layer 3: Infrastructure Layer (External Systems)

Handles I/O — databases, APIs, filesystems, etc.

class OrderRepository: 
    def get(self, id): 
        # Fetch from DB 
        pass 
 
    def save(self, order): 
        # Save to DB 
        pass

This simple layering keeps things clean and swappable. Want to switch to a new database? Just change the infrastructure layer. Business logic stays untouched.

Python Doesn’t Force You to Do This — But You Should

Unlike statically typed languages like Java or Go, Python doesn’t push you to define interfaces or strict boundaries.

That’s a feature — but it comes with a cost.

To write maintainable Python, you have to bring the discipline yourself:

• Separate pure functions from side-effecting ones
• Use dataclass or Pydantic models for structure
• Prefer composition over inheritance
• Introduce types gradually (with mypy or pyright)
• Write for clarity, not cleverness

Your future team will thank you.

Real-World Example: Refactoring a Messy Script

Before:

def handle_request(): 
    data = json.loads(request.body) 
    if data["type"] == "order": 
        db.insert("orders", data) 
        send_email(data["email"], "Order received")

After:

@dataclass 
class OrderRequest: 
    email: str 
    # ... 
 
def handle_order(req: OrderRequest): 
    order = Order.from_request(req) 
    order_repo.save(order) 
    email_service.send_confirmation(order.email)

Notice what changed?

• We introduced structure (OrderRequest)
• We separated concerns (parsing, domain logic, side effects)
• It’s now testable, extensible, and easier to read

Final Thoughts

Most Python code isn’t hard to maintain because of poor syntax or lack of unit tests.

It’s hard to maintain because it lacks clarity of purpose and boundaries of responsibility.

Once you start thinking in terms of layers, intent, and structure — your Python code stops being a script and starts becoming a system.

Write Python like you’re building a system — not solving a puzzle.