Python pytest examples for validating JSON API responses

1. Validate JSON data structure: send a GET request to an API endpoint and assert that the response JSON contains specific top‑level fields.

import requests
import pytest

def test_json_structure():
    url = "https://api.example.com/data"
    response = requests.get(url)
    data = response.json()
    assert "id" in data and "name" in data, "Expected fields not found in JSON response."

2. Validate nested JSON structure: ensure that a nested object and its sub‑fields exist in the response.

def test_nested_json():
    url = "https://api.example.com/data"
    response = requests.get(url)
    data = response.json()
    assert "user" in data and "details" in data["user"], "Nested structure not found."

3. Validate JSON size: check that the number of top‑level items falls within an expected range.

def test_json_size():
    url = "https://api.example.com/data"
    response = requests.get(url)
    data = response.json()
    assert 10 <= len(data) <= 20, "JSON size is out of the expected range."

4. Validate list length inside JSON: verify that a specific list field contains the expected number of elements.

def test_list_length():
    url = "https://api.example.com/data"
    response = requests.get(url)
    data = response.json()
    assert len(data["items"]) == 5, "List length does not match the expected value."

5. Validate uniqueness of a field across items: ensure that all "id" values are unique.

def test_unique_values():
    url = "https://api.example.com/data"
    response = requests.get(url)
    data = response.json()
    ids = [item["id"] for item in data]
    assert len(ids) == len(set(ids)), "IDs are not unique."

6. Validate pattern matching: confirm that a date field matches the YYYY‑MM‑DD format using a regular expression.

import re

def test_pattern_matching():
    url = "https://api.example.com/data"
    response = requests.get(url)
    data = response.json()
    pattern = r"^\d{4}-\d{2}-\d{2}$"
    assert all(re.match(pattern, item["date"]) for item in data), "Date format does not match the expected pattern."

7. Validate date range: check that each date in the response is not in the future.

from datetime import datetime

def test_date_range():
    url = "https://api.example.com/data"
    response = requests.get(url)
    data = response.json()
    today = datetime.now().date()
    for item in data:
        date_str = item["date"]
        date_obj = datetime.strptime(date_str, "%Y-%m-%d").date()
        assert date_obj <= today, "Date is in the future."

8. Validate numeric range: ensure that a numeric field stays within 0‑100.

def test_numeric_range():
    url = "https://api.example.com/data"
    response = requests.get(url)
    data = response.json()
    for item in data:
        value = item["value"]
        assert 0 <= value <= 100, "Value is out of the expected range."

9. Validate element order: confirm that the list of IDs is sorted in ascending order.

def test_element_order():
    url = "https://api.example.com/data"
    response = requests.get(url)
    data = response.json()
    ids = [item["id"] for item in data]
    assert ids == sorted(ids), "Elements are not in ascending order."

10. Validate field types: check that each "id" is an integer and each "name" is a string.

def test_field_types():
    url = "https://api.example.com/data"
    response = requests.get(url)
    data = response.json()
    for item in data:
        assert isinstance(item["id"], int), "ID should be an integer."
        assert isinstance(item["name"], str), "Name should be a string."

All tests use the pytest framework; when run against a reachable API they output "PASSED" if the assertions hold, otherwise pytest provides detailed failure information.