Why Your Library Needs Pytest (And How to Get Started)

So, you’ve built a Python library. Maybe it solves a niche problem, maybe it’s the next big thing. You’ve poured hours into crafting the API, writing documentation, and maybe even setting up linting (go you!). But… have you written tests?

I get it. Writing tests can sometimes feel like eating your vegetables – you know it’s good for you, but it’s not always the most exciting part of the meal. For library developers, however, testing isn’t just good practice; it’s fundamental to building trust and ensuring your code doesn’t unexpectedly break for the people relying on it.

Why Testing is Critical for Library Authors

Unlike application code where you control the environment, library code runs in countless unpredictable contexts. Users might install it alongside different package versions, on various operating systems, and use it in ways you never anticipated. This makes testing paramount:

• API Stability: Tests act as a contract. They verify that your public functions behave as expected, preventing accidental breaking changes between releases.
• User Trust: Users are more likely to adopt and rely on a library that is demonstrably well-tested.
• Refactoring Confidence: Good tests give you the confidence to improve and refactor your code without worrying about silently breaking things.
• Catching Regressions: As you add features or fix bugs, tests ensure you don’t reintroduce old problems.
• Cross-Version/Platform Compatibility: Tests help verify that your library works across the different Python versions and platforms you intend to support (we’ll talk more about tox for this later!).

Enter Pytest: Making Testing Less of a Chore

Python’s built-in unittest module works, but many developers find pytest offers a more intuitive and powerful testing experience. It aims to reduce boilerplate and make writing simple tests simple, while still providing advanced features when needed.

What does Pytest do?

At its core, pytest is a framework for writing, organizing, and running tests. It discovers test files and functions automatically, runs them, and provides detailed reports on successes and failures.

Key Advantages:

• Less Boilerplate: No need for mandatory classes; simple functions are often enough.
• Powerful Assertions: Uses plain assert statements, which provide excellent introspection on failure (e.g., showing the actual values in comparisons).
• Fixtures: A clean, powerful way to manage test setup, teardown, and dependencies.
• Extensibility: A rich ecosystem of plugins (like pytest-cov for coverage, pytest-mock for mocking, pytest-benchmark for performance).
• Good Integration: Works well with other tools like tox and CI/CD systems.

Getting Started: Your First Pytest Test

Let’s say your library has a simple function in my_library/utils.py:

# my_library/utils.py
def add_numbers(a, b):
    """Adds two numbers together."""
    if not isinstance(a, (int, float)) or not isinstance(b, (int, float)):
        raise TypeError("Both inputs must be numeric")
    return a + b

To test this with pytest, you’d create a test file (e.g., tests/test_utils.py):

# tests/test_utils.py
import pytest
from my_library.utils import add_numbers

def test_add_numbers_positive():
    """Test adding two positive numbers."""
    assert add_numbers(2, 3) == 5

def test_add_numbers_negative():
    """Test adding negative numbers."""
    assert add_numbers(-1, -5) == -6

def test_add_numbers_mixed():
    """Test adding positive and negative numbers."""
    assert add_numbers(10, -2) == 8

def test_add_numbers_floats():
    """Test adding floating-point numbers."""
    assert add_numbers(1.5, 2.5) == 4.0

def test_add_numbers_type_error():
    """Test that non-numeric input raises TypeError."""
    with pytest.raises(TypeError):
        add_numbers("a", 3) # One input is a string

    with pytest.raises(TypeError):
        add_numbers(1, None) # One input is None

Key things to note:

• Test files are typically named test_*.py or *_test.py.
• Test functions are named test_*.
• Plain assert statements are used for checking results.
• pytest.raises is a context manager used to assert that specific exceptions are raised.

To run these tests, you’d typically install pytest (pip install pytest) and run the pytest command in your project’s root directory. Pytest will automatically discover and run the tests.

This is just scratching the surface of pytest. We haven’t even touched fixtures or plugins yet! But hopefully, this gives you a sense of why testing is crucial for libraries and how pytest provides a pleasant way to approach it.

Next up in the series, we’ll look at measuring how much of your code your tests are actually running using coverage.py.