As your Python projects grow in size and complexity, debugging and following coding best practices become essential. Clean, readable code not only prevents bugs but also makes future maintenance easier. Debugging helps you identify and fix issues quickly when they arise.

This guide covers effective debugging techniques and key best practices every Python developer should follow for writing high-quality, maintainable code.

1. Common Debugging Techniques

1.1 Using print() Statements

The simplest way to understand what's going wrong in your code is to print values at different points in the program.

def calculate_total(price, quantity):
    print(f"Price: {price}, Quantity: {quantity}")
    return price * quantity

print(calculate_total(10, 2))

1.2 Using the built-in pdb Module

The pdb module is Python’s built-in debugger. You can set breakpoints and inspect variable values line-by-line.

import pdb

def divide(a, b):
    pdb.set_trace()  # Pauses execution here
    return a / b

print(divide(10, 2))

1.3 Using try-except Blocks

Use try-except to handle exceptions gracefully and find out what went wrong without crashing your program.

try:
    result = 10 / 0
except ZeroDivisionError as e:
    print("Error:", e)

1.4 Logging Instead of Printing

For larger applications, prefer using the logging module over print statements for more control.

import logging

logging.basicConfig(level=logging.INFO)
logging.info("This is an info message.")

2. Python Coding Best Practices

2.1 Follow PEP 8 Guidelines

PEP 8 is the official style guide for Python code. It includes recommendations for naming conventions, indentation, line length, and more.

• Use 4 spaces per indentation level.
• Use lowercase_with_underscores for function and variable names.
• Use CamelCase for class names.
• Limit lines to 79 characters.

2.2 Write Readable Code

Prioritize clarity. Use meaningful variable names and avoid unnecessary complexity.

# Bad
def calc(x, y): return x*y+x

# Good
def calculate_total_price(price, tax): 
    return price * tax + price

2.3 Keep Functions Small and Focused

Each function should do one thing and do it well. Avoid overly long or complex functions.

2.4 Use Docstrings and Comments

Document your functions using triple-quoted docstrings, and use comments to explain tricky logic.

def add(a, b):
    """Returns the sum of a and b."""
    return a + b

2.5 Avoid Using Global Variables

Global variables make code harder to debug and maintain. Prefer passing variables as arguments to functions.

2.6 Test Your Code

Write unit tests using Python's built-in unittest module or external libraries like pytest to ensure your code works as expected.

2.7 Use List Comprehensions Thoughtfully

List comprehensions are concise but should be used where they improve readability.

# Clear and concise
squares = [x*x for x in range(10)]

3. Tools That Help

• Linters: Use tools like flake8 or pylint to catch formatting and logical issues.
• Formatters: black automatically formats your code based on PEP 8.
• Debuggers: Use built-in pdb or IDE-integrated debuggers like those in VS Code or PyCharm.

4. Real-World Debugging Tips

• Start with small, testable code blocks.
• Reproduce the bug with clear inputs.
• Trace the flow of the code manually if needed.
• Use assertions to validate assumptions.
• Keep backups before making big changes.

5. Summary

• Debugging helps identify and fix problems quickly. Use tools like pdb, try-except, and logging.
• Best practices like following PEP 8, writing small functions, and documenting your code lead to more maintainable software.
• Modern tools and good habits save time and make you a more effective Python programmer.

6. Final Thoughts

Even experienced developers spend a lot of time debugging and refactoring code. The goal isn't to avoid bugs entirely (which is nearly impossible), but to catch and fix them efficiently. With good coding practices and the right tools, you can write robust, clean, and scalable Python applications.