5 Python Patterns I Wish I Knew Before Building Real Projects

You’ll Wish You Knew These Sooner

5 Python Patterns I Wish I Knew Before Building Real Projects

When I first started writing Python, I thought I was doing pretty well.

My scripts ran. I followed tutorials. I copied code from Stack Overflow that mostly worked.

But once I moved from small scripts to full-blown applications — APIs, automation tools, data pipelines — I started hitting walls. The code got harder to maintain. Bugs became harder to trace. Adding features felt like tiptoeing through a minefield.

What changed everything? Learning the right design patterns.

Here are five Python coding patterns I wish someone had drilled into my head before I built my first real-world app. These aren’t theoretical CS-class patterns — these are battle-tested, pragmatic, and Pythonic.

1. The Strategy Pattern — For Cleaner, Configurable Logic

Ever written a giant if-elif-else chain that made you hate your life?

if payment_type == "credit_card": 
    process_credit_card(data) 
elif payment_type == "paypal": 
    process_paypal(data) 
elif payment_type == "crypto": 
    process_crypto(data)

It works… until you need to add one more condition. Or refactor. Or test.

The fix? Use the Strategy pattern.

strategies = { 
    "credit_card": process_credit_card, 
    "paypal": process_paypal, 
    "crypto": process_crypto, 
} 
 
handler = strategies.get(payment_type) 
if handler: 
    handler(data) 
else: 
    raise ValueError("Unsupported payment method")

Clean separation of logic

Easy to extend (just add to the dictionary)

Testable, maintainable, readable

2. The Dependency Injection Pattern — For Decoupling and Testing

One of my early APIs had this:

def create_user(data): 
    db = connect_to_database() 
    db.insert("users", data)

Then I had to test it. And guess what? I needed a real database — or a monkey patch nightmare.

Here’s the better way:

def create_user(data, db_client): 
    db_client.insert("users", data)

Now in your real app:

db = connect_to_database() 
create_user(data, db)

In tests:

mock_db = InMemoryDB() 
create_user(data, mock_db)

Loose coupling between components

Easy mocking for unit tests

Reusable across environments (test, dev, prod)

3. The Command Pattern — When You Need Reversible or Queued Actions

Let’s say you’re building an app with undo functionality (or job queues). You have actions like:

def send_email(): 
    ... 
 
def notify_user(): 
    ...

Instead of calling them directly, wrap them as commands:

class SendEmailCommand: 
    def execute(self): 
        # send the email 
        pass 
 
class NotifyUserCommand: 
    def execute(self): 
        # notify logic 
        pass

Then you can do:

commands = [SendEmailCommand(), NotifyUserCommand()] 
for cmd in commands: 
    cmd.execute()

In a queuing system where jobs needed retries

For audit-logging every operation

For features like undo/redo

4. The Adapter Pattern — For Handling Incompatible Interfaces

Imagine integrating two APIs:

• Your app expects user.get_name()
• The third-party API returns user["fullName"]

Instead of hacking every usage, wrap it:

class ThirdPartyUserAdapter: 
    def __init__(self, data): 
        self.data = data 
 
    def get_name(self): 
        return self.data["fullName"]

Now the rest of your code doesn’t care.

user = ThirdPartyUserAdapter(raw_data) 
print(user.get_name())

Keeps your core logic clean

Avoids scattering compatibility hacks

Helps when refactoring or replacing APIs

5. The Registry Pattern — For Scalable Plugin-Like Architectures

Ever wanted to build a system where you can “plug in” features or handlers without touching the core logic?

The Registry pattern is your friend.

registry = {} 
 
def register(name): 
    def wrapper(func): 
        registry[name] = func 
        return func 
    return wrapper 
 
@register("sum") 
def handle_sum(data): 
    return sum(data) 
 
@register("max") 
def handle_max(data): 
    return max(data) 
 
# Dynamically run handler 
handler = registry.get("sum") 
if handler: 
    result = handler([1, 2, 3])

In a CLI tool with pluggable commands

In a Django app where admins could define workflows

In a data pipeline with dynamic processing steps

Final Thoughts — Patterns Make You a Pro

Python is beautiful because it lets you “just write code.”

But as your projects grow, that’s not enough.

Design patterns aren’t just for Java architects — they’re for you, the Python developer who wants maintainable, testable, and extendable software.

And the best part? Python makes these patterns feel natural once you start using them.

So the next time you write a giant if-else, tightly couple your functions, or duplicate logic — stop and ask: Is there a better pattern for this?

There probably is.

Want More Python Patterns?

If this article helped you level up your Python game, consider bookmarking it, sharing it with your team, or commenting with your own favorite pattern. Want a follow-up post with advanced patterns? Let me know!

Your future self will thank you for learning these now — before your codebase becomes a jungle.