Python style guide
This manual is designed to aid developers in writing Python code that is clear and consistent, within, and across, projects at GDS.
We follow PEP 8; where PEP 8 does not express a view (for example, on the usage of language features such as metaclasses) we defer to the Google Python style guide. Use these as references unless something is explicitly mentioned here. As always these rules should be followed only in conjunction with the advice on consistency on the main programming languages manual page.
If you want to add a new rule or exception please create a pull request against this repo.
Maximum line length of 120 characters
PEP 8 dictates a preferred maximum line length of <= 79. This is is a hangover from developing in a Unix terminal window. The vast majority of developers are now using an IDE which can handle a greater line length comfortably.
Couple this with the fact that much of the time GDS developers are coding web apps and
have to deal with nested
JSON objects, ORM model definitions/ queries, and error/ url
strings and this convention begins to show its age.
Do not do if not models.Address.query( models.Address.street_address_line_1 == user['address']['street_address_line_1'] ): pass Do if not models.Address.query(models.Address.street_address_line_1 == user['address']['street_address_line_1']): pass
This manual advises the use of the Flake8 command line checker as an all in one lint, codestyle and complexity checker.
How to use Flake8
Implementation of Flake8 will depend on whether the repository you want to run the checks on is a module or an application, and how your dependencies, automated testing, and continiuous integration are set up.
First you should add the Flake8 module (available from PyPI) to your ‘dev’ or ‘test’ requirements/dependencies.
You’ll then likely want to run it alongside your unit tests.
Flake8 has been designed to be extensible and has numerous plugins. They’re worth a look to see if any would be particularly beneficial to your code base.
Examples include checks for requiring copyright/licensing strings, requiring docstrings or warnings for upcoming deprecations.
A list can be found by performing a PyPI search.
Flake8 per file ignores
A particularly useful feature of Flake8 is the ability to specify rule exemptions on a per directory, per file, or per regex match basis.
Commonly it’s used for ignoring unused imports in module level
files or imports not being at the top of a file in settings files or scripts.
The feature is documented in the Flake8 options documentation, under per-file-ignores. You can also see an example in the Digital Marketplace API repo.
Digital Marketplace is already running the latest version of Flake8 on all
of its repos. You can find an example of their configuration in the root of
any repo in the
Commonly a configuration file will live in the root of the package. By default Flake8 will look for a
.flake8 file in each directory.
[flake8] # Rule definitions: https://flake8.pycqa.org/en/latest/user/error-codes.html # D203: 1 blank line required before class docstring # W503: line break before binary operator exclude = venv*,__pycache__,node_modules,bower_components,migrations ignore = D203,W503 max-complexity = 9 max-line-length = 120
In the above file we exclude directories we want the checker to ignore completely, ignore specific rules we disagree with, set the maximum line length and set the maximum complexity. We’ve also included comments detailing what the specific exclusions are.
Note: you can also ignore rules on particular lines of code or files by adding a
# noqa comment - see flake8’s noqa syntax.
Additional flake8 resources
- Digital Marketplace Config: A production config to base off
- Flake8 error codes list
- Flake8 plugins list
isort is a command line tool that sorts and formats imports. It groups the imports according to pep8 recommendations, and automatically sorts imports and lines alphabetically to keep git diffs sensible.
You can run
isort locally to fix imports before commiting.
You should add a check-only command to your test script to ensure imports are correct on PRs.
Here’s an example configuration, as taken from the
setup.cfg file in notify’s API repo.
[isort] line_length=80 indent=' ' multi_line_output=3 known_third_party=notifications_utils,notifications_python_client known_first_party=app,tests include_trailing_comma=True use_parentheses=True
Use Pyenv to manage different versions of Python you have installed. For more information see Intro to pyenv
Use the pyev plugin pyenv-virtualenv to manage virtual python environment. For more information see Python virtual environment primer
We recommend using direnv to manage environment variables.
This ensures project specific variables do not clutter your main environment or the environment of other projects.
.envrc for general project specific variables and you can use a non version controlled
.secrets to store sensitive information.
A Python application project typically brings together Python packages from PyPI, with others written in-house (or otherwise not distributed through PyPI).
These packages are the applications immediate dependencies. Additionally, any package can have its own immediate dependencies, where they draw on other packages. From an application’s point of view, the dependencies of the packages it requires are its sub-dependencies.
The below diagram shows a simplified view of the resulting pyramid of dependencies; however it is important to note that the hierarchy can repeat itself infinitely.
Application | +-----+-----+ | | | Immediate dependencies | | | +--+--+--+--+--+--+--+--+ | | | | | | | | | ... Sub-dependencies ...
There are two ways of specifying dependencies in Python world: as a specific version or as an allowable range.
Different considerations apply to dependency management depending whether you are packaging a library, or creating an end-product such as an application or a bundle of scripts. In general, you should only specify specific versions when creating a Python system that sits at the top of the dependency pyramid; otherwise there is a danger of creating version conflicts.
These recommendations apply wherever you need a reproducible set of dependencies, such as a complete web application, perhaps with many dependencies and sub-dependencies. It also applies to a collection of scripts that are deployed into the cloud and run automatically (for example, batch jobs).
A good strategy for specifying your application’s Python dependencies has two desirable characteristics - they should be:
Pin your application’s full dependencies – specific versions, rather than ranges – or you’ll get unpredictability between your dev environment and other environments. You want a new starter to avoid small hard-to-spot problems. And you want parity between what you test locally, what is tested by CI, and what you deploy, or you risk new issues appearing on a live server. Additionally these things can be hard to diagnose.
Security issues are found in libraries, so it is important to choose libraries that are maintained and to ensure your team has a strategy to ensure security updates are installed without significant delay. The how to manage third party software dependencies section gives further context and discusses tools that can help, such as snyk.io.
Your README should document an easy-to-follow process by which all your Python dependencies can be upgraded to get bug fixes and security fixes, without introducing breaking changes to your build.
Your pinned dependencies should be fully specified in a file called
requirements.txt, and checked
into your version control system (VCS). For projects with only a small number of dependencies, maintaining
this manually (for example, installing with
pip install, then using
pip freeze) may be adequate.
For larger projects, GDS recommends using [pip-tools][pip-tools] for managing dependencies.
Put your top-level requirements in a
requirements.in file, and then use
pip-compile to generate a
requirements.txt file. Both the .in and .txt files should be commited to your repository.
List dependencies only needed for development or testing into a separate
This recommendation applies to any Python repository that intended to be installable (into a virtual environment, a container, or onto bare metal) as a dependency of some larger system or application. It may be applicable to repositories that provide scripts to be run by developers or other end-users, but is not recommended for code that’s intended to be deployed on its own into the cloud.
to specify the dependencies of your library, and the version ranges with which it
can be reasonably expected to work.
The range you choose will depend on the guarantees each dependency makes about backward-compatibility. For example, if you’re currently using version 1.3.1 of a semantically-versioned library, it would be reasonable to specify a range such as
<2.0,>=1.3.1. However, for a library that does not make that guarantee, you might specify a more restricted range, such as
Update this file whenever you are ready to test and validate a new version that falls outside the existing range.
If you have dependencies that are not available on PyPI (for example, because you’ve fixed a bug by forking the code), then you can use a PEP 440 git reference in your
Specify dependencies needed only for testing your library in
tox.ini if you are using Tox
or in a