Comparison of Python Project Templates and Build Tools: CookieCutter, PyScaffold, PyBuilder, and Poetry

Python has never settled on a single, de‑facto standard for project layout and build automation, which leads to many different structures and tools. Unlike Java, which converged on Maven’s conventions, Python’s ecosystem offers several independent solutions.

The article introduces four widely used tools for creating and managing Python projects: CookieCutter, PyScaffold, PyBuilder, and Poetry. For each tool it shows how to install it, generate a sample project, and the typical directory tree produced.

CookieCutter

Install and generate a project from the popular audreyr/cookiecutter-pypackage template:

$ pip install cookiecutter
$ cookiecutter gh:audreyr/cookiecutter-pypackage

After answering the prompts, the generated layout looks like:

$ tree sample
sample
├── AUTHORS.rst
├── CONTRIBUTING.rst
├── HISTORY.rst
├── LICENSE
├── MANIFEST.in
├── Makefile
├── README.rst
├── docs
│   ├── Makefile
│   ├── authors.rst
│   ├── conf.py
│   ├── ...
├── requirements_dev.txt
├── sample
│   ├── __init__.py
│   ├── cli.py
│   └── sample.py
├── setup.cfg
├── setup.py
├── tests
│   ├── __init__.py
│   └── test_sample.py
└── tox.ini

Typical build commands are executed via make (e.g., make test , make coverage , make docs , make dist ) after installing supporting packages such as tox , wheel , coverage , sphinx , and flake8 with pip .

PyScaffold

Install and create a project with:

$ pip install pyscaffold
$ putup sample

PyScaffold places source files under a src directory:

$ tree sample
sample
├── AUTHORS.rst
├── CHANGELOG.rst
├── ...
├── pyproject.toml
├── setup.cfg
├── setup.py
├── src
│   └── sample
│       ├── __init__.py
│       └── skeleton.py
├── tests
│   ├── conftest.py
│   └── test_skeleton.py
└── tox.ini

All build and test actions are driven by tox , which creates isolated virtual environments for each task.

PyBuilder

Install and start a project:

$ pip install pybuilder
$ mkdir sample && cd sample
$ pyb --start-project

The resulting layout is Maven‑like:

$ tree sample
.
├── build.py
├── docs
├── pyproject.toml
├── setup.py
└── src
    ├── main
    │   ├── python
    │   └── scripts
    └── unittest
        └── python

Tasks are listed with pyb -t sample and can be run individually (e.g., pyb run_unit_tests ). Dependencies are declared inside build.py using project.depends_on(...) .

Poetry

Install and create a new project:

$ pip install poetry
$ poetry new sample

The minimal structure is:

$ tree sample
sample
├── README.rst
├── pyproject.toml
├── sample
│   └── __init__.py
└── tests
    ├── __init__.py
    └── test_sample.py

Poetry manages dependencies and packaging via the pyproject.toml file (similar to Node’s package.json ). Common commands include poetry add , poetry install , poetry build , poetry run pytest , and poetry run my-script for executing scripts defined under [tool.poetry.scripts] .

Overall, the complexity of the generated project structure decreases from CookieCutter to PyScaffold, PyBuilder, and finally Poetry, while the learning curve follows the same order.