Poetry vs Pip: Comparing Python Dependency Management and Packaging Tools

Poetry and Pip are two popular tools for managing Python dependencies and packaging Python projects. Both have their own sets of advantages and disadvantages. This guide will provide a technical comparison of the two tools and demonstrate why Poetry has more features and is generally preferable to Pip for most use cases.

Key Differences

Some of the key differences between Poetry and Pip include:

• Dependency resolution: Poetry uses a resolver to find the best combination of package versions to meet your requirements. Pip does not do this, it just installs the latest version that matches each requirement.

• Virtual environments: Poetry creates and manages a virtualenv for each project to isolate dependencies. Pip uses global packages by default.

• Lock files: Poetry uses a pyproject.toml file to lock all dependencies for reproducible builds. Pip has a requirements.txt but it does not lock transitive dependencies.

• Build system: Poetry includes a build system that can compile extensions and package your project. Pip focuses only on dependencies.

• Publishing: Poetry provides commands to publish your package to PyPI. Pip can only install packages.

Overall, Poetry takes a more holistic approach to dependency management, virtual environments, and packaging compared to Pip’s more narrow focus on just installing dependencies.

Dependency Resolution

One major advantage of Poetry over Pip is its use of a dependency resolver.

When you install packages with Pip, it will just grab the latest version of each package on PyPI that matches your requirements.txt:

# requirements.txt

numpy 
pandas==1.1.0
matplotlib>=3.2.0

With this requirements.txt, Pip will install the latest numpy, pandas 1.1.0, and the latest matplotlib >=3.2.0.

The problem is that these may not all be compatible versions. Pandas 1.1.0 might require an older numpy, while the latest matplotlib needs a newer numpy. This can lead to broken environments.

Poetry uses a SAT solver to figure out the best compatible versions of all packages and dependencies to meet your criteria.

For example, with this pyproject.toml:

# pyproject.toml

[tool.poetry.dependencies]
python = "^3.7"
numpy = "^1.17.0"
pandas = "1.1.0"
matplotlib = ">=3.2.0"

Poetry will resolve all packages and install a set of versions that work correctly together, even if they aren’t the very latest releases. This prevents difficult-to-diagnose dependency issues.

Virtual Environments

Pip installs packages globally by default. This can cause compatibility issues if you work on multiple projects with different dependencies.

Poetry creates a virtual environment for each project to isolate dependencies:

$ poetry install
Creating virtualenv poetry-vYfqZ98k-py3.7 in /Users/name/project1/.venv

The virtualenv will be named based on the path to the project. All packages are installed into this virtualenv.

You can then activate the virtualenv to work on the project:

$ poetry shell
Spawning shell within /Users/name/project1/.venv
(poetry-vYfqZ98k-py3.7) $ 

This keeps different project environments completely isolated. Pip can also create virtualenvs, but it is an extra manual step for the developer to do this.

Lock Files

Pip has a requirements.txt file that lists the project’s dependencies and their versions:

# requirements.txt

numpy==1.16.0
pandas==1.1.0

However, this only locks the direct dependencies. If pandas requires numpy>=1.17, pip will still install the latest compatible numpy version, even if it no longer matches the version in requirements.txt.

Poetry uses a pyproject.toml file that locks all dependencies for reproducible builds:

# pyproject.toml

[tool.poetry.dependencies]
python = "^3.7"
numpy = "1.16.0"
pandas = "1.1.0"

[tool.poetry.dev-dependencies]
pytest = "^5.2"

[build-system]
#...

Running poetry install will now always install numpy 1.16.0, even if pandas needs a newer version. This ensures you always get the exact same versions to prevent bugs from creeping in.

Pip’s requirements.txt does not provide the same guarantee.

Build System

Poetry includes a build system for packaging and distributing your Python project:

# pyproject.toml

[tool.poetry.name]
name = "mypackage"

#...

[build-system]
requires = ["poetry>=0.12"]
build-backend = "poetry.masonry.api"

You can then build a wheel or sdist:

$ poetry build
Building mypackage (0.1.0)
  - Building sdist
  - Built mypackage-0.1.0.tar.gz

  - Building wheel
  - Built mypackage-0.1.0-py3-none-any.whl

And publish to PyPI:

$ poetry publish
Publishing mypackage (0.1.0) to PyPI
 - Uploading mypackage-0.1.0.tar.gz 100%
 - Uploading mypackage-0.1.0-py3-none-any.whl 100% 

Pip is focused solely on managing dependencies and has no build system capabilities. For packaging and distribution you need external tools like setuptools and twine.

Poetry provides a single integrated tool for dependency management, virtual environments, building, and publishing.

Publishing to PyPI

As shown above, Poetry has built-in support for publishing your package to PyPI:

$ poetry publish

With Pip, you need to use other tools like Twine to upload to PyPI:

$ python setup.py sdist bdist_wheel
$ twine upload dist/*  

The PyPI publishing workflow is much more seamless with Poetry since it handles packaging and uploading.

Documentation

Poetry provides excellent documentation of its features and usage:

https://python-poetry.org/docs/

The documentation covers installation, dependency management, virtual environments, publishing, and more. There are also thorough references for configuration options.

Pip’s documentation is decent but not quite as comprehensive:

https://pip.pypa.io/en/stable/

In general, Poetry’s documentation is more user friendly and complete compared to Pip’s.

Configuration

Poetry keeps all configuration in the pyproject.toml file. This is easy to edit by hand if needed:

# pyproject.toml

[tool.poetry]
name = "myproject"
version = "0.1.0"
description = ""
authors = ["Your Name <[email protected]>"]

[tool.poetry.dependencies]
python = "^3.7"
# ...

Pip uses a number of different files for configuration:

• requirements.txt - dependency requirements
• setup.py - packaging metadata
• setup.cfg - packaging config
• .venv folders - virtual environments

It can be more difficult to understand and manage all these different config files in Pip compared to Poetry’s single pyproject.toml.

Conclusion

In summary, Poetry provides a much more complete and integrated toolset compared to Pip for Python dependency management, virtual environments, packaging, and publishing.

The key advantages of Poetry include:

• Robust dependency resolution
• Automatic virtual environments
• Reproducible lock files
• Integrated build system
• Simple PyPI publishing
• Great documentation
• Consolidated configuration

For these reasons, Poetry is generally preferable over Pip for most Python projects today. The consolidated feature set and simple workflows make Poetry a very compelling tool for modern Python dependency and packaging needs.