Python Dependency Management: Pip, Poetry, or Conda?

Python’s packaging ecosystem can be overwhelming. Unlike other languages with a single dominant tool, Python has several options.

1. Pip + venv

The classic standard. pip installs packages, and venv creates isolated environments.

• Pros: Built-in, standard, simple.
• Cons: No dependency resolution (can lead to conflicts), requirements.txt is manual.

2. Poetry

The modern favorite. Poetry handles dependency management, packaging, and publishing.

• Pros: Deterministic poetry.lock file, intuitive CLI, handles transitive dependencies well.
• Cons: A bit slower than pip, another tool to install.

3. Conda

The data science heavyweight. Conda manages not just Python packages but binary dependencies (like C libraries).

• Pros: Essential for Data Science/ML (NumPy, Pandas), manages Python versions.
• Cons: Large, slow solver (though mamba is a faster alternative).

Conclusion

• For web development: Use Poetry.
• For data science: Use Conda.
• For simple scripts: Pip is fine.