This lesson is being piloted (Beta version)

Checks and tests


Teaching: 10 min
Exercises: 10 min
  • How do you ensure your code will work well?

  • Learn about the basics of setting up tests.

  • Learn about the basics of setting up static checks

This is intended to be a very light introduction to testing and static checks (also known as linters, formatters, and type checkers), focused on setting them up, not on how you write good tests, etc.


The most popular testing framework is pytest, so we will focus on that. Python has a built-in framework too, but it’s really intended for Python’s own tests, and adding a dependency for testing only is fine even for the most strict no-dependencies allowed packages, since users don’t need tests. Tests should be as easy to write as possible, and pytest handles that beautifully.

Test directory

There are several options for test directory. The recommendation is /tests (with an s), at the root of your repository. Combined with /src/<package> layout, you will have the best experience avoiding weird edge cases with package importing.

User runnable tests

Tests should distributed with your SDist, but not your wheel. Sometimes, you might want some simple Tests a user can run in order to verify that their system works. Adding a /src/<package>/tests module using Python’s unittest that does some very quick checks to validate the package works is fine (though it should not be your entire test suite!).

Pytest configuration

A recommended pytest configuration in your pyproject.toml is:

minversion = "7.0"
addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"]
xfail_strict = true
filterwarnings = ["error"]
log_cli_level = "info"
testpaths = [

The minversion will print a nicer error if your pytest is too old. The addopts setting will add whatever you put there to the command line when you run; -ra will print a summary “r”eport of “a”ll results, which gives you a quick way to review what tests failed and were skipped, and why. --showlocals will print locals in tracebacks. --strict-markers will make sure you don’t try to use an unspecified fixture. And --strict-config will error if you make a mistake in your config. xfail_strict will change the default for xfail to fail the tests if it doesn’t fail - you can still override locally in a specific xfail for a flaky failure. filter_warnings will cause all warnings to be errors (you can add allowed warnings here too, see below). log_cli_level will report INFO and above log messages on a failure. Finally, testpaths will limit pytest to just looking in the folders given - useful if it tries to pick up things that are not tests from other directories. See the docs for more options.

pytest also checks the current and parent directories for a file. If it finds them, they will get run outer-most to inner-most. These files let you add fixtures and other pytest configurations (like hooks for test discovery, etc) for each directory. For example, you could have a “mock” folder, and in that folder, you could have a that has a mock fixture with autouse=True, then every test in that folder will get this mock applied.

In general, do not place a file in your tests; there’s not often a reason to make the test directory importable, and it can confuse package discovery algorithms. You can use pythonpath=["tests/utils"] to allow you to import things inside a tests/utils folder - though many things can be added to as fixtures.

Python hides important warnings by default, mostly because it’s trying to be nice to users. If you are a developer, you don’t want it to be “nice”. You want to find and fix warnings before they cause user errors! Locally, you should run with -Wd, or set export PYTHONWARNINGS=d in your environment. The pytest warning filter “error” will ensure that pytest will fail if it finds any warnings. You can list warnings that should be hidden or just shown without becoming errors using the syntax "<action>:Regex for warning message:Warning:package", where <action> can tends to be default (show the first time) or ignore (never show). The regex matches at the beginning of the error unless you prefix it with .*.

Static checks

In addition to tests, which run your code, there are also static checkers that look for problems or format your code without running it. While tests only check the parts of the code you write tests for, and only the things you specifically think to check, static checkers can verify your entire codebase is free of certain classes of bugs. Unlike a compiled language, like C, C++, or Rust, there is no required “compile” step, so think of this like that - an optional step you can add that can find things that don’t make sense, invalid syntax, etc.

The pre-commit framework

There’s a tool called pre-commit that is used to run static checks. (Technically it can run just about anything, but it’s designed around speed and works best with checks that take under a couple of seconds - perfect for static checks.)

You can install pre-commit with pipx, pip, your favorite package manager, or even run it inside nox.

You run pre-commit like this:

pre-commit run -a

This runs pre-commit on all files; the default is to just check staged changes for speed. As you might have guessed from the name, you can also make pre-commit run as a git pre-commit hook, with pre-commit install. You can also keep your pre-commit config up to date with pre-commit autoupdate.

You can add pre-commit checks inside a .pre-commit-config.yaml file. There are some “standard” checks most projects include:

  - repo:
    rev: "v4.4.0"
      - id: check-added-large-files
      - id: check-case-conflict
      - id: check-merge-conflict
      - id: check-symlinks
      - id: check-yaml
      - id: debug-statements
      - id: end-of-file-fixer
      - id: mixed-line-ending
      - id: requirements-txt-fixer
      - id: trailing-whitespace

There are a few things to dissect here. First, we have a repos table. This holds a list of git repositories pre-commit will use. They each have repo (pointing at the repo URL), a rev, which holds a non-moving tag (pre-commit caches environments based on this tag), and a hooks table that holds the hooks you want to use from that repo.

You can look at the docs (or the pre-commit-hooks.yaml file in the repo you are using!) to see what id’s you can select. There are more options as well - in fact, every pre-defined field can be overridden by providing the field when you use the hook.

The checks above, from the first-part pre-commit/pre-commit-hooks repo, are especially useful in the “installed” mode (where only staged changes are checked).


The defacto standard today for code formatting is Black. Unlike other auto-formatters, Black has very, very few configuration points - it’s generally easy to recognized blacked code. And once you are use to it, it is faster to read blacked code, as you can start relying on indentation patterns around brackets and such. And while no standard will please everyone, Black’s style was designed around minimizing conflicts when merging (which is already a benefit with any formatter), which was a great practical choice.

To use Black in pre-commit:

  - repo:
    rev: "23.3.0"
      - id: black

While you can turn formatting on and off inline, generally try to write your code so that it formats well. If something looks bad, maybe break it into two operations, using a variable. You’ll likely find yourself writing better code with Black.

Also, an auto-formatter helps highlight errors in your code. If you forget a comma in a list, causing strings to auto-concatenate, the blacked form often makes it much easier to spot the error; this is just one example.


Ruff has recently exploded as the most popular linting tool for Python, and it’s easy to see why. It’s tens to hundreds of times faster than similar tools like flake8, and has dozens of popular flake8 plugins and other tools (like isort and pyupgrade) all well maintained and shipped in a single Rust binary. It is highly configurable in a modern configuration format (in pyproject.toml!). And it supports auto-fixes, something common outside of Python, but rare in the Python space before now.

To use Ruff from pre-commit:

- repo:
  rev: "v0.0.271"
    - id: ruff
      args: ["--fix", "--show-fixes"]

And you’ll need a bit of configuration in your pyproject.toml:

select = [
  "E", "F", "W", # flake8
  "B",           # flake8-bugbear
  "I",           # isort
  "PGH",         # pygrep-hooks
  "RUF",         # Ruff-specific
  "UP",          # pyupgrade
extend-ignore = [
  "E501",   # Line too long
target-version = "py38"
src = ["src"]
unfixable = [
  "F841", # Removes unused variables

You can a more complete suggested config at the Scientific-Python Development Guide.


The biggest advancement since the development of Python 3 has been the addition of optional static typing. Static checks in Python have a huge disadvantage vs. a more “production” focused language like C++: you can’t tell what types things are most of the time! For example, is this function well defined?

def bit_count(x):
    return x.bit_count()

A static checker can’t tell you, since it depends on how it is called. bit_count("hello") is an error, but you won’t know that until it runs, hopefully in a test somewhere. However, now contrast that with this version:

def bit_count(x: int) -> int:
    return x.bit_count()

Now this is well defined; a type checker will tell you that this function is valid (and it will even be able to tell you it is invalid if you target any Python before 3.10, regardless of the version you are using to run the check!), and it will tell you if you try to call it with anything that’s not an int, anywhere - regardless if the function is part of a test or not!

You do have to add static types to function signatures and a few variable definitions (usually variables can be inferred automatically), but the payoff is well worth it - a static type checker can catch many things, and doesn’t require writing tests!

Here’s how you run mypy, a first party and popular type checker, from pre-commit:

- repo:
  rev: "v1.4.0"
    - id: mypy
      files: src
      args: []

You can learn about configuring mypy in the Scientific-Python Development Guide. You also need to add any packages that have static types to additional_dependencies: [...].

Going further

See the Style guide at Scientific-Python Development Guide for a lot more suggestions on static checking.

Key Points

  • Run tests and static checks on your codebase.