Quick Tutorial: Introduction to Unit Testing in Python
Quick Tutorial: Introduction to Unit Testing in Python. Get practical lessons and hands-on examples at AIComputerClasses in Indore to master programming & IT development skills quickly. This article from AIComputerClasses Indore breaks down quick tutorial: introduction to unit testing in Python into actionable steps. Follow practical exercises and tool-based examples to learn rapidly. Ideal for beginners and working professionals seeking fast skill gains.
🧪 Quick Tutorial: Introduction to Unit Testing in PythonTesting is one of the most essential parts of professional programming. It ensures that your code runs correctly — not just once, but every time it’s changed or updated.
At AI Computer Classes – Indore, we focus on teaching practical coding workflows that make you a reliable and efficient developer. One such workflow is unit testing — an essential skill for any Python programmer in 2025 and beyond.
Let’s dive into a beginner-friendly, hands-on guide to unit testing in Python and see how you can use it to make your code cleaner, more reliable, and easier to maintain.
Unit testing is a process where you test small parts (or units) of your code — usually individual functions or classes — to make sure they work as expected.
Imagine you wrote a function that calculates discounts. Before integrating it into your full project, you can test that one function independently.
💡 Example:
def add(a, b):
return a + b
# Expected Output: 5
print(add(2, 3))
Here, testing means checking if add(2, 3) really returns 5.
In real-world projects, you don’t do this manually — you use automated tests using Python’s built-in unittest module.
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⚙️ Why Unit Testing MattersUnit testing isn’t just for big companies — it’s for every serious coder who wants to:
- ✅ Write error-free code
- ⚡ Catch bugs early
- 🔄 Simplify debugging
- 🧠 Improve logical thinking
- 💼 Build confidence before deployment
Think of it as a safety net for your code. Once tests pass, you know your logic is solid.
Python comes with a built-in module called unittest that lets you write and run automated tests easily.
Here’s how a basic test file looks:
import unittest
def multiply(a, b):
return a * b
class TestMultiply(unittest.TestCase):
def test_positive_numbers(self):
self.assertEqual(multiply(2, 3), 6)
def test_negative_numbers(self):
self.assertEqual(multiply(-2, 3), -6)
if __name__ == '__main__':
unittest.main()
When you run this file, it will check both cases — if any test fails, you’ll immediately know which one and why.
Assertions are the core of testing — they check if something is true.
Here are some commonly used ones:
AssertionPurposeExampleassertEqual(a, b)Checks if a == bassertEqual(2+2, 4)assertNotEqual(a, b)Checks if a != bassertNotEqual(3*3, 8)assertTrue(x)Checks if x is TrueassertTrue(5 > 1)assertFalse(x)Checks if x is FalseassertFalse(1 > 3)assertRaises(ErrorType)Checks if a function raises an errorassertRaises(ValueError)
These make your tests clear, readable, and powerful.
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🧰 How to Run Unit Tests in PythonRunning tests is simple — you don’t need any special tool. Just type this command in your terminal:
python -m unittest test_file_name.py
If all your tests pass, you’ll see something like this:
.. ---------------------------------------------------------------------- Ran 2 tests in 0.001s OK
If a test fails, Python shows which one and why — helping you fix the exact issue quickly.
Let’s say you built a function to calculate discounts:
def calculate_discount(price, percent):
if percent < 0 or percent > 100:
raise ValueError("Invalid discount percentage")
return price - (price * percent / 100)
Now test it like this:
import unittest
from discount import calculate_discount
class TestDiscount(unittest.TestCase):
def test_valid_discount(self):
self.assertEqual(calculate_discount(100, 10), 90)
def test_invalid_discount(self):
with self.assertRaises(ValueError):
calculate_discount(100, 120)
if __name__ == '__main__':
unittest.main()
That’s a real-world, professional way to test logic safely before deploying it to production.
- Name Tests Clearly: Use descriptive names like test_user_login_valid instead of test1.
- Test Edge Cases: Check for invalid inputs, empty lists, or zero values.
- Keep Tests Independent: Each test should test only one thing.
- Use Setup and Teardown: For reusable test setups, use setUp() and tearDown() methods.
- Integrate with CI/CD: Tools like GitHub Actions can run tests automatically when you push code.
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🧭 ConclusionUnit testing is not just a developer’s tool — it’s a confidence booster.
When you write tests, you don’t just check your code — you prove that it works.
At AI Computer Classes – Indore, our Programming & IT Development course includes:
- Real-world Python projects
- Automated testing workflows
- Integration with tools like Tally Prime, Power BI, and Flask
If you want to become a complete developer who writes clean, tested, and reliable code — start learning today!
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