The Cleanest Way to Structure a Python Project in 2025

You’ve mastered Python’s syntax — now it’s time to master its structure.

The Cleanest Way to Structure a Python Project in 2025

Python projects often grow messy and unmaintainable. Here’s a modern, scalable project structure you can follow to write cleaner code, speed up onboarding, and future-proof your applications.

When I first started writing Python apps, I didn’t think too much about structure. I’d write a few modules, drop them into a folder, and call it a day. It worked — until it didn’t.

Eventually, I found myself trapped in “spaghetti project hell” — files everywhere, confusing imports, duplicated configs, and no clear separation between code, logic, and tooling. Sound familiar?

In 2025, clean architecture and maintainability matter more than ever. Whether you’re building a REST API, CLI tool, machine learning pipeline, or a data processing service, having a clear and scalable project structure saves you time, reduces bugs, and makes collaboration a breeze.

Let’s break down what a modern Python project should look like in 2025 — and how to build one that’s clean, scalable, and production-ready.

Read this article : Structure Large-Scale Python Projects Like a Senior Engineer

How to Structure Large-Scale Python Projects Like a Senior Engineer

If your project has more than 3 files, you need more than just good intentions — you need structure.

Why Project Structure Matters More Than Ever

In 2025, Python isn’t just for scripts or quick prototypes — it powers production systems, backend services, machine learning pipelines, and even enterprise-level applications. With this evolution comes complexity, and with complexity comes the need for clarity.

Here’s what a good project structure gives you:

Predictability: New devs can navigate your codebase without asking questions.

Scalability: You can add features without breaking things or creating chaos.

Testability: You can plug in testing tools with minimal friction.

Reusability: Modules can be reused across projects with little effort.

CI/CD friendliness: Deployment and automation become much easier.

The Gold Standard: Recommended Structure for Python Projects in 2025

Here’s a directory structure you can confidently use across most production-grade Python projects:

my_project/ 
├── .github/             # GitHub actions/workflows 
├── .vscode/             # Editor-specific settings (optional) 
├── docs/                # Documentation files 
├── src/                 # Source code lives here (recommended!) 
│   └── my_project/      # Actual Python package 
│       ├── __init__.py 
│       ├── config.py 
│       ├── main.py      # Entry point (CLI, API, etc.) 
│       ├── core/        # Core domain logic 
│       ├── services/    # Business logic, services 
│       ├── models/      # Pydantic/ORM models 
│       ├── api/         # REST or GraphQL routes 
│       └── utils/       # Helper functions 
├── tests/               # Unit and integration tests 
│   ├── __init__.py 
│   └── test_main.py 
├── .env                 # Environment variables 
├── pyproject.toml       # Build system & dependencies (Poetry is king in 2025) 
├── README.md            # Project overview 
├── .gitignore 
└── requirements.txt     # Optional (for Docker or deployment)

Let’s dive into some of the most important components.

1. Use the src/ Layout

Putting your code inside a src/ folder may seem like overkill, but it prevents accidental imports and makes your testing cleaner.

Why it matters:

Prevents your test runner from importing the local modules instead of installed ones.

Promotes better packaging and dependency isolation.

With the src/ layout, your pyproject.toml should include:

[tool.setuptools] 
package-dir = {"" = "src"}

2. Use pyproject.toml — Not setup.py

By 2025, pyproject.toml is the standard. It centralizes configuration for building, dependency management, and linting tools.

Works seamlessly with Poetry, Hatch, PDM, and other modern Python tooling.

Cleaner than mixing setup.py, setup.cfg, MANIFEST.in, etc.

Easier CI/CD integration.

Example snippet for Poetry:

[tool.poetry] 
name = "my_project" 
version = "0.1.0" 
description = "Clean Python structure example" 
authors = ["Your Name <you@example.com>"] 
 
[tool.poetry.dependencies] 
python = "^3.11" 
fastapi = "^0.110.0" 
 
[build-system] 
requires = ["poetry-core"] 
build-backend = "poetry.core.masonry.api"

3. Split Business Logic from Application Code

This is where most Python devs mess up — they bundle everything inside the main app or route handler. Instead, separate your:

core/ — domain logic, core rules, entity models

services/ — interface logic like API clients, mailers, job schedulers

api/ — HTTP/REST routes or CLI entry points

This makes your logic:

Easier to test

Easier to reuse

Cleaner to debug

4. Keep Configs Centralized and Versioned

Use a central config.py (or better yet, dynaconf, pydantic-settings, or dotenv) to manage settings.

Example using pydantic-settings:

from pydantic_settings import BaseSettings 
 
class Settings(BaseSettings): 
    db_url: str 
    redis_url: str 
    environment: str = "development" 
 
    class Config: 
        env_file = ".env" 
 
settings = Settings()

Keeps secrets out of code
Works in local, staging, and production
Easily mockable in tests

5. Structure Tests Like Your App

Mimic the src/ structure inside tests/. If you have src/my_project/services/email.py, create tests/services/test_email.py.

Instant mapping between app and test files

Encourages focused, modular testing

Easier for new devs to navigate

Tip: Use pytest with plugins like pytest-cov and pytest-mock for full coverage and mocking power.

6. Use .env Files for Environment Variables

Don’t hardcode secrets. Use .env files with libraries like python-dotenv or pydantic-settings. Commit a .env.example file with sample structure for onboarding.

DB_URL=postgres://user:password@localhost:5432/mydb 
REDIS_URL=redis://localhost:6379

7. Version Control Everything (Except Secrets)

Include:

.pre-commit-config.yaml

.editorconfig

.flake8

.github/workflows/ci.yml for GitHub Actions

These small things add up. They enforce standards, formatting, and reliability for every commit.

8. Bonus: Add Makefile or CLI Tooling

Add a Makefile or noxfile.py for tasks like:

lint: 
    black src/ tests/ 
    flake8 src/ tests/ 
 
test: 
    pytest 
 
run: 
    python -m src.my_project.main

Or use Typer to build your own CLI for dev tooling:

$ python tools/manage.py lint

It’s the kind of polish that makes your project stand out.

Final Thoughts: Your Future Self Will Thank You

Bad project structure won’t break your app — but it will break your team’s spirit. Good structure, on the other hand, is an investment that pays off in speed, confidence, and joy of development.

Start clean. Stay clean.
Structure like it’s 2025.

Write code for humans, not just for machines — and your future teammates will silently thank you.