The Architecture Mistakes I Made on My First Python Project (And What I Do Differently Now)

My first Python app worked… until it didn’t.

The Architecture Mistakes I Made on My First Python Project (And What I Do Differently Now)

The Nightmare That Started It All

I still remember the day my “working” Python project fell apart.

It was a side project — a simple web app that grew too fast. At first, everything felt smooth. I was writing Python scripts, adding features, shipping fast. But within a few weeks, things got… messy. Every new feature broke something else. The code was impossible to test. Bugs appeared out of nowhere. And I dreaded opening the codebase.

What went wrong?

In short: bad architecture.

In this post, I’ll break down the biggest architecture mistakes I made — and what I’ve learned (the hard way) about structuring Python projects that scale.

1. Treating Python Like a Scripting Language

My entire app started as a bunch of loosely connected .py files with procedural logic. I avoided modules. I avoided packages. It was one big monolith of spaghetti.

# example.py 
def main(): 
    user = input("Enter your name: ") 
    print(f"Hello, {user}!")

This is fine for quick scripts — but I was building something bigger.

As the app grew, the lack of structure made it hard to understand what the code was doing — or where anything lived.

What I Do Now:
I treat every Python project like a mini-package, even if it’s small at first.

my_app/ 
├── my_app/ 
│   ├── __init__.py 
│   ├── models/ 
│   ├── services/ 
│   ├── utils/ 
│   └── main.py 
├── tests/ 
├── requirements.txt 
└── README.md

Organizing your code into logical modules forces you to think ahead — and makes the project far easier to maintain and scale.

2. Skipping Dependency Injection

I hardcoded everything. Database connections, API keys, file paths — right there in the functions.

def connect_to_db(): 
    return psycopg2.connect( 
        dbname="mydb", 
        user="admin", 
        password="secret",  # yikes 
        host="localhost" 
    )

This made it nearly impossible to test anything without hitting real services. My “unit tests” weren’t really unit tests at all.

What I Do Now:
I inject dependencies. I pass them in from the outside. I separate config from logic.

class DatabaseService: 
    def __init__(self, connection): 
        self.conn = connection 
 
# In main.py 
db_conn = psycopg2.connect(**config.DATABASE) 
db_service = DatabaseService(db_conn)

This makes your code more testable, flexible, and portable.

3. Ignoring Separation of Concerns

Functions that queried the database also printed logs, transformed data, handled exceptions, and returned results — all in one place.

def get_user(id): 
    try: 
        print("Connecting to DB...") 
        conn = sqlite3.connect("db.sqlite") 
        cursor = conn.cursor() 
        cursor.execute("SELECT * FROM users WHERE id = ?", (id,)) 
        return cursor.fetchone() 
    except Exception as e: 
        print("Error:", e)

When responsibilities blur together, your code becomes tightly coupled and hard to reuse or test.

What I Do Now:
I isolate responsibilities into clear layers:

Data access layer for queries

Service layer for logic

Presentation layer for output (CLI, web, etc.)

This makes each part easier to reason about and change.

4. Not Writing Tests (Until It Was Too Late)

I told myself, “I’ll add tests later.” Spoiler: I never did.

Once the project was big, writing tests felt overwhelming. And without tests, refactoring was scary.

What I Do Now:
I write tests from the start — not full coverage, but critical paths. I also structure my code to make testing easier:

Avoid globals

Use pure functions where possible

Return data instead of printing directly

Example:

def format_user_greeting(user): 
    return f"Hello, {user}!" 
 
# test_formatting.py 
def test_greeting(): 
    assert format_user_greeting("Alex") == "Hello, Alex!"

Tests give you confidence to iterate.

5. Overengineering Too Early

Ironically, I also made the opposite mistake: I tried to implement DDD, event-driven architecture, and complex patterns… before they were needed.

Premature architecture adds unnecessary complexity. It slows you down and makes onboarding harder.

What I Do Now:
I follow a simple rule: Start simple. Evolve deliberately.

I now ask:

Is this complexity solving a real problem?

Can I defer this decision until later?

Will this make things easier to test or harder?

Start with clear code. Optimize when it hurts.

6. Not Using a Config System

API keys, secrets, and environment-specific values were hardcoded.

It made deploying to staging or production painful. I’d forget to change values, or worse — leak secrets into version control.

What I Do Now:
I use dotenv or a config module to load environment variables and separate config from logic.

# .env 
API_KEY=your-api-key 
DB_HOST=localhost 
 
# config.py 
from dotenv import load_dotenv 
import os 
 
load_dotenv() 
 
API_KEY = os.getenv("API_KEY")

It’s clean, secure, and deployment-friendly.

7. Avoiding Tools Like linters and formatters

I manually formatted code. I ignored flake8 warnings. My files were full of inconsistent styles.

Inconsistent code adds cognitive load. Reviewers spend more time on style than substance.

What I Do Now:
I use:

Black for formatting

Flake8 or Ruff for linting

mypy for type checking

I also add a pre-commit hook so I never forget.

pre-commit install

These tools enforce discipline and improve readability.

8. Not Thinking in Layers

All logic was crammed into a single file. There was no distinction between business logic, user interface, and infrastructure.

You can’t reuse anything. You can’t replace one layer without affecting others. It’s fragile.

What I Do Now:
I use a layered architecture:

Domain layer → pure logic

Infrastructure layer → DB, API, file I/O

Interface layer → CLI, HTTP, etc.

Each layer has a clear purpose. They talk to each other via interfaces, not direct dependencies.

Final Thoughts: The Code Worked — Until It Didn’t

Bad architecture rarely breaks your code today. It breaks your velocity tomorrow.

That early Python project taught me more than any tutorial. It taught me that architecture isn’t just for large teams or big apps. It’s the foundation for being able to keep going.

If your codebase feels like a mess, you’re not alone. But you can start fixing it — one layer, one refactor, one good habit at a time.

Don’t build a house of code on a foundation of duct tape.

Start small, start clean — and your future self (and teammates) will thank you.