Tips for Writing Clean Python Code as a Beginner

Python is an excellent language for beginners—it’s simple and easy to understand. However, as you become more comfortable with it, it’s easy to fall into the habit of writing code that’s complex and hard to read—something that can be a developer’s worst nightmare to debug. Clean code isn’t just about making it easier for someone else to read; it can save you a lot of headaches when you have to revisit it. Trust me, taking the time to write clean code will make your own life much simpler in the long run.

In this article, we’ll explore some tips and best practices that will help you write clean, readable Python code, even if you’re just starting out.

Key Practices for Writing Clean, Beginner-Friendly Python Code

Follow the PEP 8 Style Guide
PEP 8 is the official style guide for Python, offering guidelines to write clean, readable code. Here are some important recommendations from PEP 8:

Use 4 spaces per indentation level:

def greet():
    print("Hello, World!")  # Indented with 4 spaces

Limit line length to 79 characters: Keep lines manageable and readable. If a line becomes too long, break it into smaller parts using parentheses or backslashes.

Use blank lines to separate code blocks: Adding blank lines between functions or classes improves readability:

def add(a, b):
    return a + b

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

Use snake_case for variable and function names: Write variables and functions in lowercase with words separated by underscores:

my_variable = 10

def calculate_average(numbers):
    return sum(numbers) / len(numbers)

Use Meaningful Names for Variables and Functions
Avoid vague or single-letter names like x or y unless they’re used in short, simple loops. Descriptive names enhance clarity.

Bad Example:

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

Good Example:

def add_numbers(first_number, second_number):
    return first_number + second_number

Use Clear Comments (But Don't Overdo It)
Comments should explain why something is done, not what is done. If your code is clean and well-named, excessive commenting isn't needed. However, when necessary, use comments to clarify your intentions.

Bad Example: Avoid stating the obvious.

x = x + 1  # increment x by 1

Good Example:

def retry_connection(attempts=3):
    # Retry 3 times to handle temporary network issues
    pass

4. Keep Functions Short and Focused
A function should perform one task and perform it well. If a function becomes too long or handles multiple responsibilities, break it into smaller, more focused functions.

Bad Example (too much responsibility):

def process_user_data(user):
    # 50 lines of code doing multiple tasks
    pass

Good Example (separated tasks):

def validate_user(user):
    pass

def save_user(user):
    pass

def notify_user(user):
    pass

5. Handle Errors Gracefully
As a beginner, it can be tempting to skip error handling, but it’s an essential part of writing robust code. Use try and except blocks to manage potential errors.

try:
    number = int(input("Enter a number: "))
    print(f"The number is {number}")
except ValueError:
    print("That’s not a valid number!")

This ensures your program handles errors without crashing unexpectedly.

6. Avoid Hardcoding Values
Hardcoding values (like numbers or strings) directly into your code can make it difficult to maintain or reuse. Instead, use variables or constants.

Bad Example:

print("The total price with tax is: $105")  # Hardcoded tax and total price

Good Example:

PRICE = 100  # Base price of the product
TAX_RATE = 0.05  # 5% tax rate

# Calculate the total price
total_price = PRICE + (PRICE * TAX_RATE)

print(f"The total price with tax is: ${total_price:.2f}")

This approach makes your code more flexible and easier to modify.

7. Avoid Global Variables
Relying on global variables can make your code harder to understand and debug. Instead, store state within functions or classes.

Bad Example (using a global variable):

total = 0

def add_to_total(value):
    global total
    total += value

Good Example (using a class):

class Calculator:
    def __init__(self):
        self.total = 0
    
    def add_value(self, value):
        self.total += value

Encapsulating data within objects or functions makes your code modular, easier to test, and less prone to errors.

8. Use f-Strings for String Formatting
f-Strings (introduced in Python 3.6) provide a cleaner and more readable way to format strings.

Bad Example (concatenating strings):

name = "Alice"
age = 25
print("My name is " + name + " and I am " + str(age) + " years old")

Good Example (using f-strings):

name = "Alice"
age = 25
print(f"My name is {name} and I am {age} years old")

f-Strings are not only more readable but also more efficient than other string formatting methods.

9. Use Built-in Functions and Libraries
Python offers a wealth of powerful built-in functions. Leverage these to write efficient code instead of reinventing the wheel.

Bad Example (manually finding the maximum):

def find_max(numbers):
    max_number = numbers[0]
    for num in numbers:
        if num > max_number:
            max_number = num
    return max_number

Good Example (using max):

def find_max(numbers):
    return max(numbers)

10. Write Pythonic Code
"Pythonic" code refers to taking full advantage of Python’s simplicity and readability. Avoid complex or verbose solutions when a simpler approach exists.

Bad Example:

numbers = [1, 2, 3, 4, 5]
doubled = []
for num in numbers:
    doubled.append(num * 2)

Good Example:

numbers = [1, 2, 3, 4, 5]
doubled = [num * 2 for num in numbers]

Using list comprehensions, built-in functions, and Python’s idiomatic expressions makes your code more concise and elegant.

11. Use Version Control
Even as a beginner, it’s a great idea to start using version control tools like Git. It helps you track changes, collaborate with others, and recover your work if something goes wrong.

Learn the basics of Git:
- Save your progress with git add and git commit
- Experiment freely, knowing you can revert to previous versions

12. Structure Your Project Well
As your project grows, organizing files and directories becomes crucial. A well-structured project makes it easier to navigate, debug, and scale.

Here’s an example of a typical project structure:

my_project/
├── README.md              # Project documentation
├── requirements.txt       # Project dependencies
├── setup.py               # Package configuration for distribution
├── .gitignore             # Git ignore file
├── src/                   # Main source code directory
│   └── my_project/        # Your package directory
│       ├── __init__.py    # Makes the folder a package
│       ├── main.py        # Main application file
│       ├── config.py      # Configuration settings
│       └── constants.py   # Project constants
├── tests/                 # Test files
│   ├── __init__.py
│   ├── test_main.py
│   └── test_utils.py
├── docs/                  # Documentation files
│   ├── api.md
│   └── user_guide.md
└── scripts/               # Utility scripts
    └── setup_db.py

A structured approach keeps your project organized as it grows.

13. Test Your Code
Always test your code to ensure it works as expected. Even for simple scripts, testing can be highly beneficial. Beginners can start using Python’s built-in unittest module for automated testing.

import unittest

def add_numbers(a, b):
    return a + b

class TestAddNumbers(unittest.TestCase):
    def test_add(self):
        self.assertEqual(add_numbers(2, 3), 5)
        self.assertEqual(add_numbers(-1, 1), 0)

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

Testing helps you catch errors early and ensures your code functions correctly.