Features¶

PyScaffold comes with a lot of elaborated features and configuration defaults to make the most common tasks in developing, maintaining and distributing your own Python package as easy as possible.

Configuration, Packaging & Distribution¶

All configuration can be done in setup.cfg like changing the description, url, classifiers, installation requirements and so on as defined by setuptools. That means in most cases it is not necessary to tamper with setup.py. The syntax of setup.cfg is pretty much self-explanatory and well commented, check out this example or setuptools’ documentation.

In order to build a source, binary or wheel distribution, just run python setup.py sdist, python setup.py bdist or python setup.py bdist_wheel (recommended).

Uploading to PyPI

Of course uploading your package to the official Python package index PyPI for distribution also works out of the box. Just create a distribution as mentioned above and use twine to upload it to PyPI, e.g.:

pip install twine
twine upload dist/*

For this to work, you have to first register a PyPI account. If you just want to test, please be kind and use TestPyPI before uploading to PyPI.

Please also note that PyPI does not allow uploading local versions for practical reasons. Thus, you have to create a git tag before uploading a version of your distribution. Read more about it in the versioning section below.

Warning

Be aware that the usage of python setup.py upload for PyPI uploads also works but is nowadays strongly discouraged and even some of the new PyPI features won’t work correctly if you don’t use twine.

Namespace Packages

Optionally, namespace packages can be used, if you are planning to distribute a larger package as a collection of smaller ones. For example, use:

putup my_project --package my_package --namespace com.my_domain

to define my_package inside the namespace com.my_domain in java-style.

Package and Files Data

Additional data, e.g. images and text files, that must reside within your package, e.g. under my_project/src/my_package, and are tracked by Git will automatically be included (include_package_data = True in setup.cfg). It is not necessary to have a MANIFEST.in file for this to work. Just make sure that all files are added to your repository. To read this data in your code, use:

from pkgutil import get_data
data = get_data('my_package', 'path/to/my/data.txt')

Starting from Python 3.7 an even better approach is using importlib.resources:

from importlib.resources import read_text, read_binary
data = read_text('my_package.sub_package', 'data.txt')

Note that we need a proper package structure in this case, i.e. directories need to contain __init__.py and we only specify the file data.txt, no path is allowed. The library importlib_resources provides a backport of this feature. Even another way, provided by setuptools’s pkg_resources is:

from pkg_resources import resource_string
data = resource_string(__name__, 'path/to/my/data/relative/to/module.txt')

Yes, actually “there should be one– and preferably only one –obvious way to do it.” ;-)

Please have in mind that the include_package_data option in setup.cfg is only guaranteed to be read when creating wheels. Other distribution methods might behave unexpectedly (e.g. always including data files even when include_package_data=False). Therefore, the best option if you want to have data files in your repository but not as part of the pip installable package is to add them somewhere outside the src directory (e.g. a files directory in the root of the project, or inside tests if you use them for checks). Additionally you can exclude them explicitly via the [options.packages.find] exclude option in setup.cfg.

Warning

Using package files to store runtime configuration or mutable data is not considered good practice. Package files should be read-only. If you need configuration files, or files that should be written at runtime, please consider doing so inside standard locations in the user’s home folder (appdirs is a good library for that). If needed you can even create them at the first usage from a read-only template, which in turn can be a package file.

Versioning and Git Integration¶

Your project is already an initialised Git repository and setup.py uses the information of tags to infer the version of your project with the help of setuptools_scm. To use this feature you need to tag with the format MAJOR.MINOR[.PATCH] , e.g. 0.0.1 or 0.1. Run python setup.py --version to retrieve the current PEP440-compliant version. This version will be used when building a package and is also accessible through my_project.__version__. If you want to upload to PyPI you have to tag the current commit before uploading since PyPI does not allow local versions, e.g. 0.0.post0.dev5+gc5da6ad, for practical reasons.

Best Practices and Common Errors with Version Numbers

How do I get a clean version like 3.2.4 when I have 3.2.3.post0.dev9+g6817bd7? Just commit all your changes and create a new tag using git tag v3.2.4. In order to build an old version checkout an old tag, e.g. git checkout -b v3.2.3 v3.2.3 and run python setup.py bdist_wheel.
Why do I see `unknown` as version? In most cases this happens if your source code is no longer a proper Git repository, maybe because you moved or copied it or Git is not even installed. In general using python setup.py install (or develop) to install your package is only recommended for developers of your Python project, which have Git installed and use a proper Git repository anyway. Users of your project should always install it using the distribution you built for them e.g. pip install my_project-3.2.3-py3-none-any.whl. You build such a distribution by running python setup.py bdist_wheel and then find it under ./dist.
Is there a good versioning scheme I should follow? The most common practice is to use Semantic Versioning. Following this practice avoids the so called dependency hell for the users of your package. Also be sure to set attributes like python_requires and install_requires appropriately in setup.cfg.
Is there a best practise for distributing my package? First of all, cloning your repository or just coping your code around is a really bad practice which comes with tons of pitfalls. The clean way is to first build a distribution and then give this distribution to your users. This can be done by just copying the distribution file or uploading it to some artifact store like PyPI for public packages or devpi, Nexus, etc. for private packages. Also check out this article about packaging, versioning and continuous integration.
Using some CI service, why is the version `unknown` or `my_project-0.0.post0.dev50`? Some CI services use shallow git clones, i.e. --depth N, or don’t download git tags to save bandwidth. To verify that your repo works as expected, run:
```
git describe --dirty --tags --long --first-parent
```
which is basically what setuptools_scm does to retrieve the correct version number. If this command fails, tweak how your repo is cloned depending on your CI service and make sure to also download the tags, i.e. git fetch origin --tags.

Pre-commit Hooks

Unleash the power of Git by using its pre-commit hooks. This feature is available through the --pre-commit flag. After your project’s scaffold was generated, make sure pre-commit is installed, e.g. pip install pre-commit, then just run pre-commit install.

It goes unsaid that also a default .gitignore file is provided that is well adjusted for Python projects and the most common tools.

Sphinx Documentation¶

PyScaffold will prepare a docs directory with all you need to start writing your documentation. Start editing the file docs/index.rst to extend the documentation. The documentation also works with Read the Docs.

The Numpy and Google style docstrings are activated by default. Just make sure Sphinx 1.3 or above is installed.

If you have make and Sphinx installed in your computer, build the documentation with make -C docs html and run doctests with make -C docs doctest. Alternatively, if your project was created with the --tox option, simply run tox -e docs ot tox -e doctests.

Dependency Management in a Breeze¶

PyScaffold out of the box allows developers to express abstract dependencies and take advantage of pip to manage installation. It also can be used together with a virtual environment to avoid dependency hell during both development and production stages.

In particular, PyPA’s Pipenv can be integrated in any PyScaffold-generated project by following standard setuptools conventions. Keeping abstract requirements in setup.cfg and running pipenv install -e . is basically what you have to do (details in Dependency Management).

Warning

Experimental Feature - Pipenv support is experimental and might change in the future

Unittest & Coverage¶

PyScaffold relies on py.test to run all unittests defined in the subfolder tests. Some sane default flags for py.test are already defined in the [pytest] section of setup.cfg. The py.test plugin pytest-cov is used to automatically generate a coverage report. It is also possible to provide additional parameters and flags on the commandline, e.g., type:

py.test -h

to show the help of py.test (requires py.test to be installed in your system or virtualenv).

JUnit and Coverage HTML/XML

For usage with a continuous integration software JUnit and Coverage XML output can be activated in setup.cfg. Use the flag --travis to generate templates of the Travis configuration files .travis.yml and tests/travis_install.sh which even features the coverage and stats system Coveralls. In order to use the virtualenv management and test tool tox the flag --tox can be specified. If you are using GitLab you can get a default .gitlab-ci.yml also running pytest-cov with the flag --gitlab.

Managing test environments with tox

Run tox to generate test virtual environments for various python environments defined in the generated tox.ini. Testing and building sdists for python 2.7 and python 3.4 is just as simple with tox as:

tox -e py27,py34

Environments for tests with the the static code analyzers pyflakes and pep8 which are bundled in flake8 are included as well. Run it explicitly with:

tox -e flake8

With tox, you can use the --recreate flag to force tox to create new environments. By default, PyScaffold’s tox configuration will execute tests for a variety of python versions. If an environment is not available on the system the tests are skipped gracefully. You can rely on the tox documentation for detailed configuration options.

Management of Requirements & Licenses¶

Installation requirements of your project can be defined inside setup.cfg, e.g. install_requires = numpy; scipy. To avoid package dependency problems it is common to not pin installation requirements to any specific version, although minimum versions, e.g. sphinx>=1.3, or maximum versions, e.g. pandas<0.12, are used sometimes.

More specific installation requirements should go into requirements.txt. This file can also be managed with the help of pip compile from pip-tools that basically pins packages to the current version, e.g. numpy==1.13.1. The packages defined in requirements.txt can be easily installed with:

pip install -r requirements.txt

All licenses from choosealicense.com can be easily selected with the help of the --license flag.

Extensions¶

PyScaffold comes with several extensions:

If you want a project setup for a Data Science task, just use --dsproject after having installed pyscaffoldext-dsproject.
Create a Django project with the flag --django which is equivalent to django-admin startproject my_project enhanced by PyScaffold’s features.
Create a template for your own PyScaffold extension with --custom-extension after having installed pyscaffoldext-custom-extension with pip.
Have a README.md based on MarkDown instead of README.rst by using --markdown after having installed pyscaffoldext-markdown with pip.
Add a pyproject.toml file according to PEP 518 to your template by using --pyproject after having installed pyscaffoldext-pyproject with pip.
With the help of Cookiecutter it is possible to further customize your project setup with a template tailored for PyScaffold. Just use the flag --cookiecutter TEMPLATE to use a cookiecutter template which will be refined by PyScaffold afterwards.
… and many more like --gitlab to create the necessary files for GitLab.

There is also documentation about writing extensions. Find more extensions within the PyScaffold organisation and consider contributing your own. All extensions can easily be installed with pip install pyscaffoldext-NAME.

Warning

Deprecation Notice - In the next major release both Cookiecutter and Django extensions will be extracted into independent packages. After PyScaffold v4.0, you will need to explicitly install pyscaffoldext-cookiecutter and pyscaffoldext-django in your system/virtualenv in order to be able to use them.

Easy Updating¶

Keep your project’s scaffold up-to-date by applying putup --update my_project when a new version of PyScaffold was released. An update will only overwrite files that are not often altered by users like setup.py. To update all files use --update --force. An existing project that was not setup with PyScaffold can be converted with putup --force existing_project. The force option is completely safe to use since the git repository of the existing project is not touched! Also check out if configuration options in setup.cfg have changed.

Updates from PyScaffold 2¶

Since the overall structure of a project set up with PyScaffold 2 differs quite much from a project generated with PyScaffold 3 it is not possible to just use the --update parameter. Still with some manual efforts an update from a scaffold generated with PyScaffold 2 to PyScaffold 3’s scaffold is quite easy. Assume the name of our project is old_project with a package called old_package and no namespaces then just:

make sure your worktree is not dirty, i.e. commit all your changes,
run putup old_project --force --no-skeleton -p old_package to generate the new structure inplace and cd into your project,
move with git mv old_package/* src/old_package/ --force your old package over to the new src directory,
check git status and add untracked files from the new structure,
use git difftool to check all overwritten files, especially setup.cfg, and transfer custom configurations from the old structure to the new,
check if python setup.py test sdist works and commit your changes.

Adding features¶

With the help of an experimental updating functionality it is also possible to add additional features to your existing project scaffold. If a scaffold lacking .travis.yml was created with putup my_project it can later be added by issuing putup --update my_project --travis. For this to work, PyScaffold stores all options that were initially used to put up the scaffold under the [pyscaffold] section in setup.cfg. Be aware that right now PyScaffold provides no way to remove a feature which was once added.