How to Build a Testing Library Like Pytest?

As Pythonistas, we all love Pytest. No questions there. But what if we embark on a journey of building something similar on our own?


Let's talk about a skill that every developer dreams of mastering - writing unit tests. We all know that testing is the secret sauce that makes our code rock-solid and dependable. And when it comes to the Python world, pytest is practically our code-testing deity! 🙌

But wait, have you ever wondered how pytest works under the hood? Sure, you can find a gazillion tutorials on how to use it, but let's take a different route. In this blog, we'll embark on an exciting journey to build our testing library from scratch.

Pytest 101

If you have never seen pytest before, I don't want you to feel left out. So let's start there with an introduction to the Pytest. For this sample the folder structure will look something like this

|__ tests

With contents

def add(a, b):
    return a + b
from app.calculator import add

def test_add():
    assert add(2, 3) == 5

Now you install pytest

pip install pytest

Run pytest


Now all tests in the tests folder runs.

Other Pytest Features I love

Parametrized Test Cases

@pytest.mark.parametrize("test_input,expected", [
    ([2, 3], -1),
    ([3, 2], 1),
    ([5, 5], 0),
def test_subtract(test_input, expected):
    assert subtract(*test_input) == expected


Fixtures come in handy when you want to set up and teardown some resources before and after a test case. For example, you want to set up a database connection before a test case and teardown after the test case.

def setup():

def test_fixture(setup):

Check for exceptions

def test_divide_by_zero():
    with pytest.raises(ZeroDivisionError):
        divide(1, 0)

Why this? Why Now?

Wonderful, now let's take all these features as our inspiration to try and build our own.

  1. Why do this? For fun, because we can :P

  2. What I cannot build, I do not understand - Richard Feynman

  3. It's fun. I told you that already.

Can't Copilot do this for me?

Trust me, I've tried it.

It didn't perform well since there were no code examples for the copilot to learn from. But when I tried specific cases like Fixtures, the results were good for me to draw inspiration from.

Disclaimer: This is an experiment never meant to see production or usage. The code doesn't resemble pytest. Infact, all my attempts to read the pytest code went down the drain. This is purely my version

Features of a Testing Library

Test Runner

  • Find all test files and test functions. If the folders or files don't start with test, we skip it

for dr in os.listdir(sys.path[0]):
    if dr != "tests":
    for files in os.listdir(dr):
        if not (files.startswith("test") and files.endswith(".py")):
        module = importlib.import_module(dr + "." + files[:-3])
            for members in getmembers(module, isfunction):
                if members[0].startswith("test"):
  • calling the test functions.

    • Once we have the test function object, let's call it and capture errors if any

except AssertionError as e:
    errors[members[1]] = e
  • Capture all test results (success, failure, error)

for member, ex in errors.items():
        tb = ex.__traceback__
        while tb is not None:
            if tb.tb_next is None:
            tb = tb.tb_next

        trace = []
                "filename": tb.tb_frame.f_code.co_filename,
                "name": tb.tb_frame.f_code.co_name,
                "lineno": tb.tb_lineno,
                "traceback": traceback.format_tb(tb),
        print(type(ex).__name__, trace)

Parametrized test cases

If you look at parametrized test case of Pytest, you'll notice that it's driven by decorator. Let's start there

  • create owntest.parametrized function. Since the parametrize function takes values on setup, the decorator will be 3 layered.

 def parametrize(keys, values):
    def decorator(func):
        def wrapper():
        return wrapper
    return decorator
  • Let's preserve the parameters. On decorating, let's format the values and make it easy for consumption in test cases.

# keys = "test_input,expected"
# values = [("3+5", 8), ("2+4", 6), ("6*9", 54)]

# params = [{"test_input": "3+5", "expected": 8}, {"test_input": "2+4", "expected": 6}, {"test_input": "6*9", "expected": 54}]
def parametrize(keys, values):
    def decorator(func):
        params = []
        for value in values:
            param = {}
            for i, key in enumerate(keys.split(",")):
                param[key] = value[i]
  • Run parametrized test cases, with each parameter.

    • During test run we loop through each parameter and call the test on each parameter individually

def wrapper(*args, **kwargs):
    """Runs the original test function with multiple params"""
    for i, param in enumerate(params):
        print("subtest running", i + 1)


Let's create owntest.fixture function. This decorator will store the fixture function in a dictionary. Later when we run the test, we can map the function attribute names to a fixture.

Register Function as Fixture

class FixtureDecorator:
    def __init__(self, func):
        self.func = func

    def __call__(self):
        return self.func()

def fixture(function):
    return FixtureDecorator(function)

Create a map of Fixtures

  • Update the test runner to maintain a mapping of fixture when it encounters one.

for members in getmembers(module, isfunction):
    if "fixture_wrapper" in members[1].__name__:
        fixtures_mapping[members[0]] = members[1]

Handling Generator Fixture

If the recognized function is a fixture, as stored in fixture mapping, check if the function is a generator. If yes, generate the value with __next__ else use the return value directly on the test function

for arg in members[1].__code__.co_varnames:
    if arg in fixtures_mapping:
        fixture_return_value = fixtures_mapping[arg]()
        # check if the generator
        if hasattr(fixture_return_value, "__next__"):

The Wrap-up

And that wraps up our adventure into the world of building a testing library in Python! We've covered all the essential components - test discovery, functional test cases, fixtures, and parametrized test cases - that come together to create a fantastic testing framework.

Now armed with this newfound knowledge, you have the tools to craft your very own testing library customized to suit your project's needs. It's an exciting journey that lets you showcase your coding prowess while ensuring your code is thoroughly validated.

So, go ahead and take on the challenge and rebuild ownpytest in your own way. Maybe think about testing your testing library.

Until my next freaky experiment, see ya.

I will be presenting this post as a talk at Pycon AU 2023. If you are around, say hi.

Last updated