⚡ 1. Testing in Python
Python has multiple ways to test your code: unit tests, integration tests, and end-to-end tests.
1.1 Unit Testing with unittest (Built-in)
# my_module.py
def add(a, b):
return a + b
# test_my_module.py
import unittest
from my_module import add
class TestMathFunctions(unittest.TestCase):
def test_add_positive_numbers(self):
self.assertEqual(add(2, 3), 5)
def test_add_negative_numbers(self):
self.assertEqual(add(-2, -3), -5)
if __name__ == "__main__":
unittest.main()
Run: python test_my_module.py
Key methods: assertEqual, assertTrue, assertFalse, assertRaises
1.2 Using pytest (Simpler & More Powerful)
# test_my_module.py
from my_module import add
def test_add_positive():
assert add(2, 3) == 5
def test_add_negative():
assert add(-2, -3) == -5
Run: pytest (automatically discovers test_*.py files)
Supports fixtures, parameterized tests, and plugins.
1.3 Mocking External Dependencies
Sometimes functions depend on APIs, databases, or files. Use unittest.mock:
from unittest.mock import patch
import requests
def get_status(url):
response = requests.get(url)
return response.status_code
@patch('requests.get')
def test_get_status(mock_get):
mock_get.return_value.status_code = 200
assert get_status("http://example.com") == 200
Prevents real network calls and isolates the function being tested.
1.4 Test Coverage
Measure how much of your code is tested using coverage:
pip install coverage
coverage run -m pytest
coverage report -m
🔍 2. Debugging in Python
Debugging is the process of finding and fixing errors in your code. Python provides multiple tools:
2.1 Print Statements (Quick & Dirty)
def divide(a, b):
print(f"a={a}, b={b}")
return a / b
divide(5, 0) # Will help track variables
Simple but can clutter code.
2.2 Using pdb (Python Debugger)
import pdb
def divide(a, b):
pdb.set_trace() # Pauses execution here
return a / b
divide(5, 0)
Commands inside pdb:
n → next line
s → step into function
c → continue execution
p variable → print variable
2.3 Using IDE Debuggers
Most IDEs (PyCharm, VS Code) provide graphical debugging:
Set breakpoints
Inspect variables
Step over / into / out
Watch expressions and call stack
2.4 Exception Handling & Logging
Instead of letting errors crash your program:
import logging
logging.basicConfig(level=logging.INFO)
def divide(a, b):
try:
return a / b
except ZeroDivisionError as e:
logging.error(f"Error: {e}")
return None
Logging is preferred over print for production code.
You can configure different log levels: DEBUG, INFO, WARNING, ERROR.
2.5 Profiling & Performance Debugging
cProfile: Find slow parts of your code:
python -m cProfile my_script.py
Helps optimize performance bottlenecks.
✅ 3. Best Practices
Write tests before or alongside code (Test-Driven Development).
Keep tests small and independent.
Use mocking to isolate external dependencies.
Debug systematically:
Reproduce the bug
Inspect variables & stack trace
Apply logging
Automate tests using CI/CD pipelines.
Combine testing and debugging: tests often reveal bugs, debugging fixes them.
Learn Fullstack Python Training in Hyderabad
Read More
How to Test Your API Endpoints in Python
Building Versioned APIs in Python
How to Handle Errors and Responses in Full Stack Python APIs
Integrating Third-Party APIs with Full Stack Python
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