Unit Testing - RamNayakTech/knowledge-hub GitHub Wiki

Unit testing in Python is the practice of writing tests for individual units of code (typically functions or methods) to ensure they work as expected. Python provides a built-in framework called unittest, but you can also use other libraries like pytest for a more flexible approach.

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1. Using unittest

unittest is Python's built-in testing framework.

Example

import unittest

def add(x, y):
    return x + y

class TestMathOperations(unittest.TestCase):

    def test_add(self):
        self.assertEqual(add(2, 3), 5)
        self.assertEqual(add(-1, 1), 0)

    def test_add_fail(self):
        self.assertNotEqual(add(2, 2), 5)

if __name__ == '__main__':
    unittest.main()

Running the test

Save the script as test_math.py and run:

python -m unittest test_math.py

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2. Using pytest (Recommended)

pytest is a more powerful and concise testing framework.

Installation

pip install pytest

Example

import pytest

def add(x, y):
    return x + y

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

def test_add_fail():
    assert add(2, 2) != 5

Running the test

Save the script as test_math.py and run:

pytest test_math.py

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3. Mocking with unittest.mock

Mocking is useful when testing functions that depend on external services.

Example

from unittest.mock import MagicMock

class ExternalService:
    def fetch_data(self):
        return "real data"

def get_processed_data(service):
    data = service.fetch_data()
    return f"Processed {data}"

def test_mocking():
    mock_service = ExternalService()
    mock_service.fetch_data = MagicMock(return_value="mocked data")

    assert get_processed_data(mock_service) == "Processed mocked data"

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4. Best Practices for Unit Testing

✅ Write tests for all critical functionalities.
✅ Keep tests independent from each other.
✅ Use meaningful test names (test_add_valid_inputs).
✅ Cover edge cases (empty values, incorrect data types).
✅ Automate test execution in CI/CD pipelines.