structuring.md

Structuring a Python project

If you are writing code that you are sure will never be seen or used by anybody else, then you can structure your project however you want, and you can stop reading now.

However, if you are intending to make your code available for others to use, either as end users, or as a dependency of their own code, you will make their lives much easier if you spend a little time organising your project directory.

• Recommended project structure
• The mypackage/ directory
• README
• LICENSE
• requirements.txt
• setup.py
• Appendix: Tests
• Appendix: Versioning
• Include the version in your code
• Deprecate, don't remove!
• Appendix: Cookiecutter

Official documentation: https://packaging.python.org/tutorials/distributing-packages/

Recommended project structure

A Python project directory should, at the very least, have a structure that resembles the following:

  myproject/
      mypackage/
          __init__.py
          mymodule.py
      README
      LICENSE
      requirements.txt
      setup.py

This example structure is in the example_project/ sub-directory - have a look through it if you like.

The mypackage/ directory

The first thing you should do is make sure that all of your python code is organised into a sensibly-named package. This is important, because it greatly reduces the possibility of naming collisions when people install your library alongside other libraries. Hands up those of you who have ever written a file called utils.[py|m|c|cpp]!

Check out the advanced_topics/02_modules_and_packages.ipynb practical for more details on packages in Python.

README

Every project should have a README file. This is simply a plain text file which describes your project and how to use it. It is common and acceptable for a README file to be written in plain text, reStructuredText (README.rst), or markdown (README.md).

LICENSE

Having a LICENSE file makes it easy for people to understand the constraints under which your code can be used.

requirements.txt

This file is not strictly necessary, but is very common in Python projects. It contains a list of the Python-based dependencies of your project, in a standardised syntax. You can specify the exact version, or range of versions, that your project requires. For example:

six==1.*
numpy==1.*
scipy>=0.18,<2
nibabel==2.*

If your project has optional dependencies, i.e. libraries which are not critical but, if present, will allow your project to offer some extra features, you can list them in a separate requirements file called, for example, requirements-extra.txt.

Having all your dependencies listed in a file in this way makes it easy for others to install the dependencies needed by your project, simply by running:

pip install -r requirements.txt

setup.py

This is the most important file (apart from your code, of course). Python projects are installed using setuptools, which is used internally during both the creation of, and installation of Python libraries.

The setup.py file in a Python project is akin to a Makefile in a C/C++ project. But setup.py is also the location where you can define project metadata (e.g. name, author, URL, etc) in a standardised format and, if necessary, customise aspects of the build process for your library.

You generally don't need to worry about, or interact with setuptools at all. With one exception - setup.py is a Python script, and its main job is to call the setuptools.setup function, passing it information about your project.

The setup.py for our example project might look like this:

#!/usr/bin/env python

from setuptools import setup
from setuptools import find_packages

# Import version number from
# the project package (see
# the section on versioning).
from mypackage import __version__

# Read in requirements from
# the requirements.txt file.
with open('requirements.txt', 'rt') as f:
    requirements = [l.strip() for l in f.readlines()]

# Generate a list of all of the
# packages that are in your project.
packages = find_packages()

setup(

    name='Example project',
    description='Example Python project for PyTreat',
    url='https://git.fmrib.ox.ac.uk/fsl/pytreat-2018-practicals/',
    author='Paul McCarthy',
    author_email='pauldmccarthy@gmail.com',
    license='Apache License Version 2.0',

    packages=packages,

    version=__version__,

    install_requires=requirements,

    classifiers=[
        'Development Status :: 3 - Alpha',
        'Intended Audience :: Developers',
        'License :: OSI Approved :: Apache Software License',
        'Programming Language :: Python :: 2.7',
        'Programming Language :: Python :: 3.4',
        'Programming Language :: Python :: 3.5',
        'Programming Language :: Python :: 3.6',
        'Topic :: Software Development :: Libraries :: Python Modules'],
)

The setup function gets passed all of your project's metadata, including its version number, depedencies, and licensing information. The classifiers argument should contain a list of classifiers which are applicable to your project. Classifiers are purely for descriptive purposes - they can be used to aid people in finding your project on PyPI, if you release it there.

See here for more details on setup.py and the setup function.

Appendix: Tests

There are no strict rules for where to put your tests (you have tests, right?). There are two main conventions:

You can store your test files inside your package directory:

myproject/
    mypackage/
        __init__.py
        mymodule.py
        tests/
            __init__.py
            test_mymodule.py

Or, you can store your test files alongside your package directory:

myproject/
    mypackage/
        __init__.py
        mymodule.py
    tests/
        test_mymodule.py

If you want your test code to be completely independent of your project's code, then go with the second option. However, if you would like your test code to be distributed as part of your project (e.g. so that end users can run them), then the first option is probably the best.

But in the end, the standard Python unit testing frameworks (pytest and nose) are pretty good at finding your test functions no matter where you've hidden them, so the choice is really up to you.

Appendix: Versioning

If you are intending to make your project available for public use (e.g. on PyPI and/or conda), it is very important to manage the version number of your project. If somebody decides to build their software on top of your project, they are not going to be very happy with you if you make substantial, API-breaking changes without changing your version number in an appropriate manner.

Python has official standards on what constitutes a valid version number. These standards can be quite complicated but, in the vast majority of cases, a simple three-number versioning scheme comprising major, minor, and patch release numbers should suffice. Such a version number has the form:

major.minor.patch

For example, a version number of 1.3.2 has a major release of 1, minor release of 3, and a patch release of 2.

If you follow some simple and rational guidelines for versioning your_project, then people who use your project can, for instance, specify that they depend on your_project==1.*, and be sure that their code will work for any version of your_project with a major release of 1. Following these simple guidelines greatly improves software interoperability, and makes everybody (i.e. developers of other projects, and end users) much happier!

Many modern Python projects use some form of semantic versioning. Semantic versioning is simply a set of guidelines on how to manage your version number:

• The major release number should be incremented whenever you introduce any backwards-incompatible changes. In other words, if you change your code such that some other code which uses your code would break, you should increment the major release number.

• The minor release number should be incremented whenever you add any new (backwards-compatible) features to your project.

• The patch release number should be incremented for backwards-compatible bug-fixes and other minor changes.

If you like to automate things, bumpversion is a simple tool that you can use to help manage your version number.

Include the version in your code

While the version of a library is ultimately defined in setup.py, it is standard practice for a Python library to contain a version string called __version__ in the __init__.py file of the top-level package. For example, our example_project/mypackage/__init__.py file contains this line:

__version__ = '0.1.0'

This makes a library's version number programmatically accessible and queryable.

Deprecate, don't remove!

If you really want to change your API, but can't bring yourself to increment your major release number, consider deprecating the old API, and postponing its removal until you are ready for a major release. This will allow you to change your API, but retain backwards-compatilbiity with the old API until it can safely be removed at the next major release.

You can use the built-in warnings module to warn about uses of deprecated items. There are also some third-party libraries which make it easy to mark a function, method or class as being deprecated.

Appendix: Cookiecutter

It is worth mentioning Cookiecutter, a little utility program which you can use to generate a skeleton file/directory structure for a new Python project.

You need to give it a template (there are many available templates, including for projects in languages other than Python) - a couple of useful templates are the minimal Python package template, and the full Python package template (although the latter is probably overkill for most).

Here is how to create a skeleton project directory based off the minimal Python packagetemplate:

pip install cookiecutter

# tell cookiecutter to create a directory
# from the pypackage-minimal template
cookiecutter https://github.com/kragniz/cookiecutter-pypackage-minimal.git

# cookiecutter will then prompt you for
# basic information (e.g. projectname,
# author name/email), and then create a
# new directory containing the project
# skeleton.