Best practices for setting up a project to maintain the highest quality code
Jul 31, 2019
When starting a new Python project, it is tempting to just dive in and start coding. Spending a tiny amount of time to setup a project with the best tools will save immense time and lead to a happier coding experience.
In an ideal world, dependencies would be identical for all developers, code would be perfectly formatted, common errors forbidden and everything would be covered by tests. Additionally, all of these would ensured at each commit.
In this article I'll go through how to set up a project that does exactly that. You can either follow along with the steps or jump straight to generating a new project automatically by installing pipx and pipenv then generating a new project.
Let's create a new project directory:
Pipx is a handy utility that allows quick installation of python command line tools. We'll be using it to install pipenv and cookiecutter.
python3 -m pip install --user pipx
python3 -m pipx ensurepath
Pipenv automatically creates and manages a virtualenv for your projects, as well as adding/removing packages from your Pipfile as you install/uninstall packages. It also generates the ever-important Pipfile.lock, which is used to produce deterministic builds.
Knowing that you and your teammates are using the same library versions is a huge confidence boost. Pipenv takes care of this and has gained massive traction in the last year or so.
pipx install pipenv
Black formats our code:
Black is the uncompromising Python code formatter. By using it, you agree to cede control over minutiae of hand-formatting. In return, Black gives you speed, determinism, and freedom from pycodestyle nagging about formatting. You will save time and mental energy for more important matters.
Blackened code looks the same regardless of the project you're reading. Formatting becomes transparent after a while and you can focus on the content instead.
Black makes code review faster by producing the smallest diffs possible.
while isort sorts our imports:
isort your python imports for you so you don't have to. isort is a Python utility / library to sort imports alphabetically, and automatically separated into sections.
Let's install them using pipenv as development dependencies so they don't clutter a deployment:
pipenv install black isort --dev
Black and isort have incompatible default options so we'll override isort to
follow black's lead. Create a
setup.cfg file and add this config:
We can run these tools with:
pipenv run black
pipenv run isort
Flake8 ensures our code follows the standard python conventions as defined in PEP8. Install using pipenv:
pipenv install flake8 --dev
Just like isort it needs a little configuration to work well with black. Add
this config to
ignore = E203, E266, E501, W503
max-line-length = 88
max-complexity = 18
select = B,C,E,F,W,T4
Now we can run flake8 with
pipenv run flake8.
Mypy is an optional static type checker for Python that aims to combine the benefits of dynamic (or "duck") typing and static typing. Mypy combines the expressive power and convenience of Python with a powerful type system and compile-time type checking. Mypy type checks standard Python programs; run them using any Python VM with basically no runtime overhead.
Having types in Python takes a little getting used to but the benefits are substantial. From the website:
pipenv install mypy --dev
Mypy by default will recursively check all imports for type annotations which
leads to errors when libraries do not include these annotations. We need to
configure mypy to run only on our code and to ignore any errors for imports
without type annotations. We are assuming that our code lives in the
best_practices package for the following config. Add this to
Now we can run mypy with:
pipenv run mypy
Here's a useful cheat sheet for using it.
Writing tests with pytest is incredibly easy and removing any friction to writing tests means we will write more of them!
pipenv install pytest pytest-cov --dev
Here's a simple example from the pytest website:
# content of test_sample.py
return x + 1
assert inc(3) == 5
To execute it:
$ pipenv run pytest
=========================== test session starts ============================
platform linux -- Python 3.x.y, pytest-5.x.y, py-1.x.y, pluggy-0.x.y
collected 1 item
test_sample.py F [100%]
================================= FAILURES =================================
_______________________________ test_answer ________________________________
> assert inc(3) == 5
E assert 4 == 5
E + where 4 = inc(3)
========================= 1 failed in 0.12 seconds =========================
All our tests should go in the
test directory so add this config to
We also want to check how much of our code is covered by tests. Create a new
.coveragerc to only return coverage statistics for our application code,
again we are assuming our application code lives in the
source = best_practices
# Have to re-enable the standard pragma
pragma: no cover
# Don't complain about missing debug-only code:
# Don't complain if tests don't hit defensive assertion code:
# Don't complain if non-runnable code isn't run:
if __name__ == .__main__.:
We can now run our tests and report coverage with
pipenv run pytest --cov --cov-fail-under=100
This will fail if our test coverage of the application code is less than 100%.
Git hooks allow you to run scripts any time you want to commit or push. This lets us run all of our linting and tests automatically every time we commit/push. pre‑commit allows easy configuration of these hooks:
Git hook scripts are useful for identifying simple issues before submission to code review. We run our hooks on every commit to automatically point out issues in code such as missing semicolons, trailing whitespace, and debug statements. By pointing these issues out before code review, this allows a code reviewer to focus on the architecture of a change while not wasting time with trivial style nitpicks.
Here we configure all of the above tools to run on any changed python files on
committing, and also to run pytest coverage only when pushing as it can be slow.
Create a new file
- repo: local
- id: isort
entry: pipenv run isort
- id: black
entry: pipenv run black
- id: flake8
entry: pipenv run flake8
- id: mypy
entry: pipenv run mypy
- id: pytest
entry: pipenv run pytest
- id: pytest-cov
entry: pipenv run pytest --cov --cov-fail-under=100
If you ever need to skip these hooks you can run
git commit --no-verify or
git push --no-verify
Now we've seen what an ideal project contains, we can turn this into a template to generate a new project with a single command:
pipx run cookiecutter gh:sourcery-ai/python-best-practices-cookiecutter
Fill in the project name and repo name and your project will be generated for you.
To finish setting up, follow these steps:
# Enter project directory
# Initialise git repo
# Install dependencies
pipenv install --dev
# Setup pre-commit and pre-push hooks
pipenv run pre-commit install -t pre-commit
pipenv run pre-commit install -t pre-push
The template project contains a very simple Python file and test to try the
tools out on. Once you're happy with the code you can then do your first
git commit and all the hooks will be run.
While it is great to know that the standard of code on our project will always be maintained at the highest level when committing code, it is somewhat frustrating to find out any issues after we think the code changes have all been done. It is much better to get the issues displayed in real-time.
Spend some time to make sure these commands are run by your code editor when saving a file. Having instant feedback means you can quickly fixup any minor issues introduced while the code is fresh in the mind.
Personally I use some excellent Vim plugins to accomplish this: