TL;DR: Automating Python packaging and distribution saves time, reduces errors, and ensures consistency. Tools like setuptools and Poetry simplify dependency management, building, and publishing to PyPI. This guide covers both traditional (setuptools) and modern (Poetry) workflows, helping you choose the right approach for your projects.
As a Python developer, I’ve spent countless hours wrestling with dependency conflicts, manual setup scripts, and messy deployment processes. Over time, I’ve learned that automating packaging and distribution isn’t just a convenience—it’s essential for maintainable, scalable projects. In this article, I’ll walk you through automating Python packaging using setuptools and Poetry, and show you how to efficiently manage PyPI repository uploads.
Why Automate Python Packaging?
Manual packaging is error-prone and time-consuming. Every time you update dependencies or tweak metadata, you risk introducing inconsistencies. Automation ensures repeatability, simplifies collaboration, and integrates seamlessly with CI/CD pipelines[^2][^5]. Whether you’re building internal tools or open-source libraries, streamlining this process pays dividends in reliability and developer happiness.
Traditional Packaging with setuptools
setuptools has long been the standard for Python packaging. It’s robust, widely supported, and integrates with PyPI via twine. Here’s how I automate with setuptools:
Setting Up pyproject.toml for setuptools
Since PEP 518, pyproject.toml is the recommended place to define build requirements[^7]. I start by creating this file:
[build-system]
requires = ["setuptools>=45", "wheel"]
build-backend = "setuptools.build_meta"
This tells tools like pip to use setuptools for building distributions.
Configuring Package Metadata
I prefer storing metadata in pyproject.toml for consistency, though setup.py is still supported[^1][^7]:
[project]
name = "my-package"
version = "0.1.0"
description = "A sample Python package"
authors = [{name = "Your Name", email = "[email protected]"}]
license = {file = "LICENSE"}
readme = "README.md"
requires-python = ">=3.8"
dependencies = [
"requests>=2.25.0",
"numpy>=1.20.0"
]
[project.optional-dependencies]
dev = ["pytest>=6.0", "black"]
Building and Distributing with setuptools
To build source and wheel distributions, I run:
python -m build
This generates dist/ with .tar.gz and .whl files. For PyPI uploads, I use twine:
twine upload dist/*
I automate this in CI scripts to run on tag pushes.
Modern Packaging with Poetry
Poetry simplifies dependency management and packaging by combining multiple tools into one[^2][^5]. I’ve switched most of my projects to Poetry for its intuitive workflow and robust dependency resolution.
Initializing a Project with Poetry
Starting a new project is straightforward:
poetry new my-project
cd my-project
For existing projects, I run poetry init to generate pyproject.toml interactively.
Managing Dependencies
Poetry excels at dependency management. I add packages with:
poetry add requests@^2.25.0
poetry add --group dev pytest
Poetry resolves version constraints and updates pyproject.toml and poetry.lock automatically[^9]. The lockfile ensures reproducible installs across environments.
Building and Publishing with Poetry
Building packages is a single command:
poetry build
This creates distributions in dist/, similar to setuptools. For publishing, I configure PyPI credentials:
poetry config pypi-token.pypi my-token
Then publish with:
poetry publish
Poetry handles uploading to PyPI without needing twine[^3][^10].
Comparing setuptools and Poetry
Both tools get the job done, but they cater to different workflows:
- setuptools is battle-tested and flexible, ideal for complex packages or legacy systems.
- Poetry offers a unified experience for dependency management, virtual environments, and publishing[^5][^9].
I recommend Poetry for new projects and setuptools for maintaining existing ones or when you need fine-grained control.
Automating with CI/CD Pipelines
Automation shines in CI environments. Here’s how I set it up:
GitHub Actions Example for Poetry
name: Publish to PyPI
on:
release:
types: [published]
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install Poetry
run: pip install poetry
- name: Build and publish
run: |
poetry build
poetry publish --username __token__ --password ${{ secrets.PYPI_TOKEN }}
Similar workflows can be built for setuptools using twine.
Best Practices for PyPI Management
- Use API tokens instead of passwords for better security[^3][^8].
- Test distributions in TestPyPI before production releases.
- Keep dependencies pinned in lockfiles (Poetry) or constraints files (setuptools).
- Automate version bumps with tools like
bumpversionor Poetry’s version command.
Conclusion: Streamline Your Packaging Workflow
Automating Python packaging with setuptools or Poetry reduces friction and lets you focus on writing code. For most new projects, I prefer Poetry for its integrated approach. For existing setuptools projects, incrementally adopting pyproject.toml modernizes your configuration without a full rewrite.
Start by picking one tool, automating your build process, and integrating it into your CI/CD pipeline. The time you save will compound with every release.
Ready to automate? Choose a tool, set up pyproject.toml, and try publishing a test package to PyPI today!
Frequently Asked Questions
What’s the difference between setuptools and Poetry?
setuptools is a traditional build tool focused on packaging, while Poetry handles dependencies, virtual environments, and publishing in one tool[^2][^5].
Can I use Poetry with existing setuptools projects?
Yes, but it may require migration. You can gradually adopt Poetry by initializing in an existing directory and adjusting configurations.
How do I manage private dependencies with these tools?
Both support private repositories. Poetry allows adding alternative sources via poetry source add, while setuptools works with pip’s --extra-index-url.
Is pyproject.toml replacing setup.py?
Yes, pyproject.toml is the modern standard for project configuration[^7]. setup.py is still supported but considered legacy for new projects.
Can I use Poetry in CI/CD without installing it globally?
Absolutely. Install Poetry via pip in your CI environment or use the official installer script with --version to pin a specific release.
How do I handle conditional dependencies?
In setuptools, use extras_require. In Poetry, use dependency groups like --group dev for development-only packages[^1][^5].
References
[^1]: Packaging and distributing projects — https://packaging.python.org/guides/distributing-packages-using-setuptools/
[^2]: Poetry: Dependency Management and Packaging Simplified … — https://thedatafreak.medium.com/poetry-dependency-management-and-packaging-simplified-in-python-e407e8f7a04e
[^3]: Publishing a Package to PyPI with Poetry — https://www.ianwootten.co.uk/2020/10/20/publishing-a-package-to-pypi-with-poetry/
[^4]: Project Summaries — https://packaging.python.org/key_projects/
[^5]: How to use Poetry for dependency management and … — https://medium.com/@jimwang3589/how-to-use-poetry-for-dependency-management-and-packaging-f9f5d2aff13c
[^6]: Packaging in Python: Tools and Best Practices — https://www.turing.com/kb/python-packaging
[^7]: How to Manage Python Projects With pyproject.toml — https://realpython.com/python-pyproject-toml/
[^8]: A Guide to Building and Uploading a Pip Installable Python … — https://learnopencv.com/building-pip-installable-package-pypi/
[^9]: Python Poetry: Modern And Efficient Python Environment … — https://www.datacamp.com/tutorial/python-poetry
[^10]: Simplify Your Python Development with Poetry’s Packaging … — https://levelup.gitconnected.com/simplify-your-python-development-with-poetrys-packaging-and-dependency-management-9d8b181f926f
