How I Build Python Projects That Don’t Collapse Under Their Own Weight

Big projects don’t fail overnight — they slowly rot from the inside.

How I Build Python Projects That Don’t Collapse Under Their Own Weight

Practical strategies, lessons learned, and real-world patterns for writing Python code that survives growth, complexity, and changing requirements.

I’ve lost count of how many Python projects I’ve started with big dreams — only to see them grind to a halt months later. Not because of lack of ideas or motivation, but because the codebase had quietly turned into a swamp.

Every feature took longer to implement. Every bug fix introduced new bugs. Dependencies fought each other. Tests were brittle. And every time I touched an old file, I found a cryptic comment from my past self asking “Why is this like this?”

If this sounds familiar, you’re not alone. Most projects don’t implode suddenly — they collapse under their own weight as complexity grows unchecked.

Over the years, I’ve learned (often painfully) how to design and maintain Python projects so they stay healthy, even as they scale. This isn’t theory — these are battle-tested principles I follow on every serious project.

Let’s break them down.

1. Start With the Right Folder Structure (and Stick to It)

Most Python projects don’t fail because of a bad algorithm. They fail because no one knows where anything lives.

A good folder structure:

• Reduces cognitive load
• Makes onboarding easier for new contributors
• Prevents “god files” that contain everything

Here’s my go-to starting point for medium-to-large projects:

my_project/ 
│ 
├── src/                # Main source code 
│   ├── my_project/      # Package code 
│   ├── __init__.py 
│ 
├── tests/              # All tests 
│ 
├── docs/               # Documentation 
│ 
├── requirements.txt    # Production dependencies 
├── requirements-dev.txt # Dev/test dependencies 
├── pyproject.toml       # Build & metadata 
└── README.md

Use a src/ layout to avoid the “works on my machine” import issues that happen with flat structures.

2. Keep Functions and Classes Small

The fastest way to kill a project is to let functions turn into spaghetti monsters.

My personal rules:

• Functions: Aim for 20–30 lines max
• Classes: Should do one thing well — if you need more, break them up
• Modules: If a file hits ~300–400 lines, split it

When code is small, you get:

• Easier testing
• Faster debugging
• Less fear when making changes

I think of it like Lego bricks: small, well-shaped pieces make it easier to build complex structures.

3. Lock Down Your Dependencies

Nothing ages a project faster than dependency chaos.

I’ve been burned by upgrading one library and suddenly breaking half the app because another package wasn’t compatible.

My approach:

• Use pip-tools or Poetry to lock versions:

pip-compile requirements.in

• Regularly run pip list --outdated and upgrade in controlled batches
• Avoid unnecessary dependencies — every package is a future liability

If a feature can be implemented in 10 lines without pulling in a library, I’ll do that every time.

4. Write Tests You’ll Actually Maintain

Here’s the dirty truth: unmaintained tests are worse than no tests.

Tests should:

• Focus on behavior, not implementation details
• Be easy to read and update
• Run fast (under a minute for the whole suite)

I structure tests to mirror the code:

src/my_project/ 
tests/test_my_project/

And I prioritize:

• Unit tests for core logic
• Integration tests for critical workflows
• Smoke tests to catch catastrophic breakages

Use pytest fixtures to avoid repeating setup code.

5. Automate the Boring Stuff Early

Every time I’ve skipped automation “until later,” I’ve paid for it.

At minimum, I set up:

• Pre-commit hooks for linting & formatting (black, ruff)
• Continuous Integration (CI) for running tests on every push
• Type checks with mypy (or pyright)

Example pre-commit-config.yaml:

repos: 
  - repo: https://github.com/psf/black 
    rev: 24.3.0 
    hooks: 
      - id: black 
  - repo: https://github.com/astral-sh/ruff-pre-commit 
    rev: v0.4.3 
    hooks: 
      - id: ruff

Automation isn’t just about speed — it’s about consistency and removing human error.

6. Document As You Go

Future you will not remember why you did that weird thing with asyncio.

I document at three levels:

1. Docstrings for public functions/classes
2. README for project setup and key usage
3. ADR (Architecture Decision Records) for major design choices

An ADR can be as simple as:

# ADR 0001: Database Choice 
Date: 2025-08-08 
We chose PostgreSQL because...

Small bits of documentation now save massive time later.

7. Refactor Ruthlessly, But Safely

Codebases rot because small hacks become permanent.

I’ve learned to:

• Refactor in small, tested steps
• Never mix refactoring with feature changes
• Use feature flags to roll out risky changes gradually

Refactoring isn’t about perfection — it’s about keeping the code changeable.

8. Design for Change, Not Perfection

Early in my career, I tried to design the perfect architecture up front. It never worked — requirements always changed.

Now, I focus on:

• Decoupling modules with clear interfaces
• Avoiding deep inheritance trees (favor composition)
• Making it easy to swap components (e.g., a database layer)

Good code isn’t code that never changes — it’s code that changes without breaking.

Conclusion

Python makes it easy to start projects. It’s just as easy to let them rot.

The difference between a weekend script and a maintainable product isn’t language choice — it’s discipline:

• Keep code small and modular
• Lock dependencies
• Write maintainable tests
• Automate early
• Document decisions
• Refactor continuously

If you treat your codebase like a garden — pruning, watering, and weeding regularly — it will thrive for years instead of collapsing under its own weight.

The best time to keep your project healthy was on day one. The second-best time is today.