Master Data‑Driven API Testing with Pytest’s @parametrize

Why Data‑Driven Testing Matters

Separating test logic from test data reduces code duplication, improves coverage by exercising many input combinations, and makes tests more flexible because data can be added or changed without touching the test code.

Pytest’s @pytest.mark.parametrize Decorator

The decorator lets you supply multiple sets of input data to a test function, supporting single‑parameter tests, multi‑parameter combination tests, and dynamic data generation from functions.

1. Single‑Parameter Test Example

import pytest

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

Pytest runs test_addition once for each tuple.

2. Multi‑Parameter Combination Test Example

import pytest

@pytest.mark.parametrize("username, password, expected_status", [
    ("user1", "pass1", 200),
    ("user2", "wrong_pass", 401),
    ("admin", "admin123", 200),
    ("guest", "guest", 200),
    ("", "", 400),
    ("user1", "", 400),
    ("", "pass1", 400)
])
def test_login(username, password, expected_status):
    url = "https://api.example.com/login"
    data = {"username": username, "password": password}
    response = requests.post(url, json=data)
    assert response.status_code == expected_status

This checks various credential combinations against the login endpoint.

3. Dynamic Data Generation

import pytest

def generate_test_data():
    return [
        (1, 1, 2),
        (2, 3, 5),
        (5, 5, 10),
        (0, 0, 0),
        (-1, 1, 0)
    ]

@pytest.mark.parametrize("a, b, expected", generate_test_data())
def test_addition(a, b, expected):
    assert a + b == expected

The function supplies data at runtime, keeping the test suite adaptable.

Combining Fixtures with parametrize

import pytest

@pytest.fixture
def base_url():
    return "https://api.example.com"

@pytest.mark.parametrize("endpoint, status_code", [
    ("/users", 200),
    ("/posts", 200),
    ("/comments", 200),
    ("/invalid", 404)
])
def test_api_endpoints(base_url, endpoint, status_code):
    url = base_url + endpoint
    response = requests.get(url)
    assert response.status_code == status_code

The fixture provides the common base URL while the decorator supplies different endpoints and expected codes.

Using External Data Sources

import pytest, json

def load_test_data():
    with open("test_data.json", "r") as f:
        return json.load(f)

@pytest.mark.parametrize("data", load_test_data())
def test_api(data):
    url = data["url"]
    expected_status = data["expected_status"]
    response = requests.get(url)
    assert response.status_code == expected_status

Test data stored in a JSON file is loaded at runtime, allowing non‑technical team members to edit test cases without touching code.

Conclusion

Pytest’s @pytest.mark.parametrize makes data‑driven API testing straightforward, boosting efficiency and coverage. By pairing it with fixtures and external data sources, you gain even greater flexibility and maintainability in your automated test suite.