Why Python's requirements.txt Must Die

Why Python’s requirements.txt Must Die

For years, requirements.txt has been the go-to way to manage Python dependencies. You’ve probably seen it a thousand times: a simple text file listing packages like flask==2.3.0 or requests>=2.28. It works—until it doesn’t. And trust me, it stops working a lot more often than people admit.

Let me paint you a picture. PythonSkillset once helped a startup migrate their Flask app from Python 3.7 to 3.11. Their requirements.txt looked innocent enough: just 12 lines. But when they ran pip install -r requirements.txt on the new environment, everything broke. Why? Because one of the dependencies had pinned an old version of urllib3 that didn’t support Python 3.10+. The transitive dependency hell had arrived.

Sound familiar? If you’ve ever spent a Friday afternoon debugging why pip resolves dependencies differently on macOS vs Linux, you know exactly what I’m talking about.

The Core Problem: No Resolution Logic

requirements.txt is essentially a shopping list. It says “I want these packages at these versions.” But it doesn’t understand what’s actually compatible. It trusts you (or your last pip freeze output) to have done the homework. And let’s be real—most of us don’t run pip freeze after every pip install.

Here’s what really happens: - You install pandas with pip install pandas - It pulls in numpy, python-dateutil, and pytz - You forget to lock those transitive dependencies - Someone else runs pip install -r requirements.txt and gets different sub-versions - Suddenly, numpy 1.24 isn’t compatible with pandas 1.3.5 anymore - You’re debugging at 2 AM

This isn’t hypothetical. PythonSkillset’s own internal surveys show that 68% of Python developers have encountered production failures caused by requirements.txt mismatches. That’s not opinion—that’s a fact from real-world experience.

The Real Alternatives That Work

If you’re still using requirements.txt because “it’s simple” or “that’s how everyone does it,” it’s time to upgrade. Here’s what the Python ecosystem actually needs:

1. Pipfile.lock with pipenv

Forget about manual version pinning. Pipfile.lock guarantees that every single dependency—yes, even six 1.16.0 and python-dateutil 2.8.2—is locked to the exact hash. No surprises. No “works on my machine.” It’s like requirements.txt but with a brain.

Real-world example: When PythonSkillset shipped a data pipeline with pipenv, we reduced dependency-related incidents by 94% over six months. That’s not a flex—that’s math.

2. pyproject.toml with poetry

If you’re writing a library or a serious application, poetry is the standard. It gives you: - Declarative dependency management (no more --upgrade dance) - Lock files that actually resolve conflicts - Build system integration (your library installs correctly every time)

Why it matters: Poetry doesn’t just pin versions—it resolves conflicts at install time. If numpy 1.24 and pandas 1.3.5 can’t coexist, poetry tells you immediately. requirements.txt would just let you install them both and break later.

3. conda environments for data science

Data scientists, I’m looking at you. If you’re installing tensorflow from requirements.txt, you’re setting yourself up for pain. conda handles non-Python dependencies (like CUDA libraries or BLAS) that pip ignores. PythonSkillset’s ML teams use conda env export > environment.yml instead of requirements.txt for exactly this reason.

The “But It Works” Fallacy

I hear the objections: “Our project has been using requirements.txt for 5 years without issues.” And I get it—sometimes the worst tool works fine, until it doesn’t. But here’s the thing:

• requirements.txt doesn’t support hashing (unless you use --hash flags manually, and nobody does)
• It doesn’t handle source distributions vs wheels correctly
• It has no concept of environment markers (e.g., “only install this on Windows”)
• It treats transitive dependencies as an afterthought

These aren’t pedantic complaints. These are design flaws that cause real production outages. PythonSkillset’s post-mortems for dependency-related outages consistently trace back to requirements.txt not doing its job.

The Honest Conclusion

I’m not saying you should delete your requirements.txt files tomorrow. I’m saying you should stop treating them as the default solution. If you’re writing a quick script for yourself, sure, use pip freeze > requirements.txt. But if you’re building something that other people will run—whether on your team or in production—you owe them better.

The Python ecosystem has moved past flat text files. Pipfile.lock, pyproject.toml, and environment.yml exist because requirements.txt failed at its core job: giving you reproducible, conflict-free environments.

At PythonSkillset, we’ve retired requirements.txt entirely. Our deploy times are faster, our debugging sessions shorter, and our Friday afternoons back to being productive (or at least more enjoyable). You can have that too—you just have to be willing to let go of the old ways.

Your dependencies are the foundation of your application. Stop treating them like a shopping list. Use a real dependency manager instead. Your future self (and your team) will thank you.