Testing with pytest

Testing with pytest

While unittest is built into the std‑lib, pytest has become the de‑facto standard for Python testing because of its simple syntax, powerful fixtures, and expressive assertions.

1️⃣ Install pytest

pip install pytest

2️⃣ A basic test file (test_math.py)

# test_math.py

def add(a, b):
    return a + b

def mul(a, b):
    return a * b

def test_add():
    assert add(2, 3) == 5
    assert add(-1, 1) == 0

def test_mul():
    assert mul(3, 4) == 12
    assert mul(0, 5) == 0

Run it with:

pytest -q

You’ll see a concise output showing which tests passed.

3️⃣ Fixtures – reusable setup/teardown

import pytest

@pytest.fixture
def sample_list():
    return [1, 2, 3]

def test_append(sample_list):
    sample_list.append(4)
    assert sample_list == [1, 2, 3, 4]

Fixtures can also be autouse, scope‑session, scope‑module, etc.

4️⃣ Parametrized Tests – data‑driven testing

@pytest.mark.parametrize("a,b,expected", [
    (1, 2, 3),
    (5, 5, 10),
    (-1, 1, 0)
])
def test_add_param(a, b, expected):
    assert add(a, b) == expected

This replaces multiple near‑identical test functions with a single compact one.

5️⃣ Expected Exceptions

import pytest

def div(a, b):
    return a / b

def test_div_zero():
    with pytest.raises(ZeroDivisionError):
        div(1, 0)

6️⃣ Running a subset & coverage

# Run only tests in a specific file
pytest tests/test_api.py

# Run with coverage (requires pytest‑cov)
pip install pytest-cov
pytest --cov=myapp

7️⃣ Good Practices Checklist

• Keep tests in a separate tests/ folder.
• Name test files test_*.py and test functions test_*.
• Use fixtures for heavy‑weight setup (DB connections, API clients).
• Aim for fast tests – they should run in milliseconds.
• Combine pytest with mypy for type‑checked tests (--check-type).

With pytest you’ll spend less time writing boilerplate and more time verifying that your code works.