LEARN COMPLETE PYTHON IN 24 HOURS

13. Testing in Python

13.1 unittest vs pytest

Python has two dominant testing frameworks:

Featureunittest (built-in)pytest (third-party)Winner (2026)Included in PythonYesNo (pip install pytest)unittestSyntax styleClass-based, verboseSimple functions, minimal boilerplatepytestAssertionsself.assertEqual(), self.assertTrue(), etc.assert x == y (natural Python)pytestFixtures / setupsetUp(), tearDown(), setUpClass()@pytest.fixture (very powerful)pytestParameterized testsManual or unittest.subTest()@pytest.mark.parametrize (clean & readable)pytestPlugins & ecosystemLimitedHuge (pytest-django, pytest-asyncio, etc.)pytestMocking supportBuilt-in unittest.mockSame + pytest-mock pluginTieCommunity adoptionUsed in standard library & some enterprisesDominant in open-source & modern projectspytestLearning curveSlightly steeperVery easypytest

Recommendation (2026):

• Use pytest for almost everything — it's faster to write, more readable, and has a massive ecosystem.

• Use unittest only if:

  • You cannot install external packages (e.g., some corporate environments)

  • You are contributing to Python standard library or legacy code

13.2 Writing Unit Tests

With pytest (recommended)

Python

# tests/test_calculator.py def add(a, b): return a + b def test_add_positive(): assert add(2, 3) == 5 def test_add_negative(): assert add(-1, 1) == 0 def test_add_zero(): assert add(0, 0) == 0

Run tests:

Bash

pytest tests/ # discovers all test_*.py files pytest tests/test_calculator.py pytest -v # verbose output pytest --tb=short # shorter tracebacks

With unittest (built-in)

Python

# tests/test_calculator_unittest.py import unittest def add(a, b): return a + b class TestAddFunction(unittest.TestCase): def test_add_positive(self): self.assertEqual(add(2, 3), 5) def test_add_negative(self): self.assertEqual(add(-1, 1), 0) def test_add_zero(self): self.assertEqual(add(0, 0), 0) if name == '__main__': unittest.main()

Run:

Bash

python -m unittest tests/test_calculator_unittest.py python -m unittest discover # auto-discover

Best practices for unit tests:

• Name tests clearly: test_function_scenario_expectation

• Test one thing per test function

• Use descriptive assertions

• Keep tests fast & independent

• Aim for high coverage (80%+ for important code)

13.3 Mocking (unittest.mock)

Mocking replaces real objects (external APIs, databases, files) with fake versions during tests.

Using unittest.mock (works with both pytest & unittest)

Python

# calculator.py import requests def get_weather(city): response = requests.get(f"https://api.weather.com/{city}") return response.json()["temp"] # tests/test_calculator.py (pytest) from unittest.mock import patch import pytest @patch("requests.get") def test_get_weather_success(mock_get): # Mock response mock_response = mock_get.return_value mock_response.json.return_value = {"temp": 28} mock_response.status_code = 200 result = get_weather("Muzaffarpur") assert result == 28 mock_get.assert_called_once_with("https://api.weather.com/Muzaffarpur")

Mocking file reading

Python

from unittest.mock import mock_open, patch @patch("builtins.open", new_callable=mock_open, read_data="42") def test_read_number(mock_file): with open("number.txt") as f: num = int(f.read()) assert num == 42

pytest fixtures + mocking (cleaner)

Python

import pytest from unittest.mock import patch @pytest.fixture def mock_requests(): with patch("requests.get") as mock: yield mock def test_get_weather(mock_requests): mock_requests.return_value.json.return_value = {"temp": 28} assert get_weather("Delhi") == 28

13.4 Test-Driven Development (TDD) Basics

TDD workflow:

1. Write a failing test (red)

2. Write minimal code to make test pass (green)

3. Refactor code (keep tests green)

Example: TDD for a simple calculator

Step 1: Write failing test

Python

# tests/test_math.py def test_subtract(): assert subtract(10, 4) == 6

Step 2: Make it pass (minimal code)

Python

def subtract(a, b): return a - b

Step 3: Add more tests & refactor

Python

def test_subtract_negative(): assert subtract(5, 10) == -5 def test_subtract_zero(): assert subtract(7, 0) == 7

Benefits of TDD:

• Forces clear requirements

• Produces testable, clean code

• Reduces bugs

• Gives confidence during refactoring

Pythonic TDD tools:

• pytest + pytest-watch (pip install pytest-watch) → auto-run tests on file change

• coverage.py → measure test coverage

• tox → test across multiple Python versions

Mini Project – TDD for a User Validator

Python

# tests/test_validator.py def test_valid_email(): assert is_valid_email("anshuman@example.com") is True def test_invalid_email(): assert is_valid_email("anshuman@") is False def test_email_with_subdomain(): assert is_valid_email("ans@sub.example.co.in") is True

Start with failing tests, then implement is_valid_email() step-by-step using regex or string checks.

This completes the full Testing in Python section — now you can write reliable, well-tested code and adopt professional testing habits!

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