Python Poetry vs Pip: Managing Dependencies the Right Way (Stop Using requirements.txt)

Python Poetry vs Pip: Managing Dependencies the Right Way

If you are still generating `requirements.txt` files by hand, you are living in the past. In 2026, professional Python developers use Poetry to stop "Dependency Hell" before it starts.

We have all been there. You clone a project, run pip install -r requirements.txt, and the screen explodes in red error text. Version conflict. Package not found. Environment mismatch.

The problem isn't Python; the problem is Pip. While Pip is great for installing things, it is terrible at managing the relationships between them. It installs packages blindly, often breaking other packages that rely on specific versions.

Enter Poetry. It is a dependency manager and packaging tool that replaces setup.py, requirements.txt, and pip with a single, robust workflow.

The "Flat List" Problem: A requirements.txt file is just a flat list. It doesn't distinguish between the packages you installed (like Pandas) and the 15 other packages Pandas installed to make itself work. This makes upgrading impossible.

How Poetry Fixes the Mess

Poetry uses a configuration file called pyproject.toml. This is the modern standard for Python projects.

When you want to add a library, you don't run pip install. You run:

poetry add pandas

Poetry does three magical things instantly:

1. It checks if Pandas conflicts with any existing packages.
2. It creates/updates a Virtual Environment automatically (no more manually creating `venv` folders).
3. It generates a poetry.lock file. This file records the exact version of every sub-dependency, ensuring that everyone on your team has the identical setup.

Pip vs. Poetry: The Showdown

Feature	Pip (+ venv)	Poetry
Dependency Resolution	Weak (Can install conflicting versions)	Strong (Fails before breaking environment)
Lock File	No (requirements.txt is not a lock file)	Yes (poetry.lock)
Virtual Env	Manual setup required	Automatic management
Publishing	Complex (setup.py, twine)	Simple (`poetry publish`)

Separating Dev Dependencies

One of the biggest headaches with Pip is managing tools you only need for testing, like `pytest` or `black`. You usually end up with `requirements.txt` and `requirements-dev.txt`.

With Poetry, it is built in:

poetry add --group dev pytest

This adds Pytest to your project, but marks it as "Dev only." When you deploy your app to production, you simply run poetry install --without dev, keeping your production environment lean and fast.

Is It Hard to Switch?

Not at all. If you have an existing project with a requirements.txt, you can initialize Poetry in seconds:

# 1. Install Poetry pip install poetry # 2. Initialize in your folder poetry init # 3. Import existing requirements cat requirements.txt | xargs poetry add

Pro Tip: Poetry also handles packaging. If you want to upload your library to PyPI, you don't need `setuptools` anymore. You just type poetry build and poetry publish.

Conclusion

Pip was good enough for 2015. But in 2026, applications are complex. You need the stability of a Lock File and the intelligence of a Resolver.

Stop fighting with your environment. Let Poetry write the code.

Download January Skills: Python Poetry Cheat Sheet