Unlocking Excellence: Mastering Clean Code & Best Practices

7 min readMay 28, 2024


At GitRoll, our mission transcends connecting companies with top-tier software engineering talents; we’re here to redefine the tech talent landscape through a meticulous, data-driven approach. By harnessing the power of GitHub, we offer AI-driven assessments that scrutinize both the hard and soft skills of developers, ensuring that businesses can discover and engage with exceptional talent in the most cost-effective manner possible.

In our journey, we’ve identified a crucial element that stands at the core of technological innovation and excellence: the mastery of clean code and best practices. These aren’t just mere recommendations; they are the bedrock upon which efficient, maintainable, and scalable software is built, catering not only to the diverse spectrum of the software engineering field but also to anyone who interacts with code.

Join us as we delve into the essence of clean code and best practices, and discover how GitRoll is leading the charge in elevating coding standards across the globe. Let’s take a look into them via a very linguistically readable language — Python!

  1. Clean Code Gusto
  • What is Clean Code?

Clean code is a term that encapsulates the art of writing code that is easy to read, understand, and maintain. It adheres to a set of principles and best practices that enhance the overall quality of software. The essence of clean code lies in its simplicity and readability, ensuring that it can be easily managed and extended over time.

  • Why Clean Code?

The significance of clean code cannot be overstated. It leads to more reliable and robust software, reduces the complexity of codebases, and facilitates easier maintenance and updates. For developers, working with clean code means spending less time deciphering the codebase and more time focusing on innovation and problem-solving. For companies, it translates to faster development cycles, reduced costs, and higher-quality products.

  • How to Code Clean?

To illustrate the principles of clean code, let’s consider a Python example. Python is renowned for its readability, making it an excellent choice for demonstrating clean code practices.


def calculate_factorial(number):
"""Calculate the factorial of a given number."""
if not isinstance(number, int) or number < 0:
raise ValueError("Input must be a non-negative integer.")
if number == 0:
return 1
return number * calculate_factorial(number - 1)

This example adheres to clean code principles by using descriptive variable and function names, handling errors gracefully, and providing clear documentation. It demonstrates simplicity, readability, and efficiency.

  • Clean Code in Python: A Deeper Dive

Writing clean code is essential for maintaining a healthy, scalable, and efficient codebase. Clean code is more than just working code — it’s about ensuring that your code is easily understandable, modifiable, and extendable by others, including your future self. Let’s explore the principles of clean code in Python from various perspectives, with practical examples to guide you.

  • Readability: Use Descriptive Names

Choosing meaningful and descriptive names for variables, functions, and classes makes your code much easier to understand. A good function name usually follows a verb_nouns(variable) format.

Bad Example:
def calc(d):
# What does d represent? What does this function calculate?
return d * 3.14 * 2

Better Example:
def calculate_circumference(diameter):
return diameter * 3.14 * 2
  • Follow the “DRY Principle”

DRY stands for “Don’t Repeat Yourself.” This principle is about reducing repetition within your code. If you find yourself writing the same code more than once, consider refactoring it into a function, or class.

Bad Example:
area1 = length1 * width1
area2 = length2 * width2
area3 = length3 * width3

Better Example:
def calculate_area(length, width):
return length * width

area1 = calculate_area(length1, width1)
area2 = calculate_area(length2, width2)
area3 = calculate_area(length3, width3)
  • Simplicity: Avoid Premature Optimization

While it’s tempting to optimize your code as much as possible, premature optimization can lead to complex, unreadable code. Focus on simplicity and clarity first, then optimize as needed based on performance reviews.

Scenario: Imagine you’re writing a function to check if a number is prime. A straightforward approach is to check if the number is divisible by any number from 2 to the square root of the number itself.

def is_prime_over_optimized(number):
if number <= 1:
return False
elif number <= 3:
return True
elif number % 2 == 0 or number % 3 == 0:
return False
i = 5
while i * i <= number:
if number % i == 0 or number % (i + 2) == 0:
return False
i += 6
return True

This optimized version introduces additional checks and a while loop that might not be immediately clear to someone reading the code for the first time. It’s optimized for performance but at the cost of readability.

Better example:
def is_prime(number):
if number < 2:
return False
for i in range(2, int(number**0.5) + 1):
if number % i == 0:
return False
return True

This implementation is clear and easy to understand. It’s a good starting point because it’s straightforward and readable.

  • Modularity: Use Functions and Classes Appropriately

Breaking your code into functions and classes can help encapsulate functionality, making your code more modular, reusable, and maintainable.

class Greeter:
def __init__(self, name):
self.name = name

def greet(self):
print(f"Hello, {self.name}!")

def main():
greeter = Greeter("Ray")

if __name__ == "__main__":
  • Error Handling: Use Exceptions Rather Than Return Codes

Using exceptions for error handling makes your code cleaner and separates the normal flow from error handling.

Bad Example:
def divide(a, b):
if b == 0:
return "Error"
return a / b

Good Example:
def divide(a, b):
if b == 0:
raise ValueError("Cannot divide by zero.")
return a / b
  • For more formal definitions and information that initiated and created by Python PEP 8’s creator, please refer to PEP 8 — Style Guide for Python Code, by Guido van Rossum, Barry Warsaw, Alyssa Coghlan on July 5th 2001 (https://peps.python.org/pep-0008/).

2. Best Practices Talk

  • What is Best Practice?

Best practices are a set of guidelines or methodologies that are widely accepted as being the most effective way to achieve a specific goal or standard in software development. These practices evolve over time, reflecting the collective experience and wisdom of the developer community.

  • Why Best Practices?

Adhering to best practices in software development ensures that the code is not only functional but also secure, efficient, and scalable. It helps developers avoid common pitfalls and errors, leading to more robust and reliable software. For businesses, following best practices means delivering high-quality products to their customers faster and more cost-effectively.

  • How to Carry out Best Practices?

Let’s explore how to implement best practices through a Python example, focusing on error handling and code readability.


def divide_numbers(numerator, denominator):
"""Safely divide 2 numbers and return the result."""
return numerator / denominator
except ZeroDivisionError:
return "Error: Cannot divide by zero."

In this example, we’ve implemented error handling with a simple clause to catch and respond to a ZeroDivisionError. This practice ensures that our program can gracefully handle unexpected inputs, enhancing its reliability and user experience.

  • Best Practices in Python: A Comprehensive Guide

Again Python, as a high-level programming language, offers a lot of flexibility and freedom in implementation. However, this flexibility also means that developers are often faced with multiple viable approaches to solve a problem. To ensure code quality, maintainability, and readability, it’s crucial to adhere to established best practices. Let’s delve into it!

  • Formatting and Syntax
  1. Indentation and Line Length

Python uses indentation to define blocks of code. The recommended practice is to use 4 spaces per indentation level, ensuring consistency throughout the codebase. Additionally, PEP 8 suggests keeping line lengths to a maximum of 79 characters to enhance readability and facilitate side-by-side file comparison.


def greet(name):
1234message = "Hello, " + name

2. Blank Lines and Spacing

Use 2 blank lines to separate top-level functions and class definitions, and 1 blank line to separate method definitions inside a class. Spaces should be used around operators and after commas to improve readability.


class Greeter:

def __init__(self, name):
self.name = name

def greet(self):
print(f"Hello, {self.name}")
  • Comments and Documentation: Use Docstrings and Comments Wisely

While comments are essential, too many can clutter your code. Use docstrings to explain the purpose of a function, method, or class, and comments to clarify complex parts of your code.


def calculate_area(length, width):
Calculate the area of a rectangle.

length (float): The length of the rectangle.
width (float): The width of the rectangle.

float: The area of the rectangle.
return length * width
  • Testing

Testing is an essential part of ensuring code quality. Python offers several frameworks for automated testing, such as unittest and pytest. Writing tests early and frequently, focusing on edge cases, and aiming for high test coverage are recommended practices.


import unittest

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

class TestAddFunction(unittest.TestCase):
def test_add_positive_numbers(self):
self.assertEqual(add(2, 3), 5)

def test_add_negative_numbers(self):
self.assertEqual(add(-1, -1), -2)
  • Naming Conventions

Using descriptive and meaningful names for variables, functions, and classes enhances code readability and maintainability. Python’s naming conventions include using lowercase with underscores for function and variable names, and CamelCase for class names.


class RoomCalculator:
def calculate_area(length, width):
return length * width
  • Use of Linters and Autoformatters

To ensure adherence to best practices and coding standards, using linters and autoformatters is highly recommended. Tools like flake8 for linting and black for auto-formatting can automatically enforce style guidelines, saving time and improving code quality.

  • Principles to Keep in Mind

Beyond specific rules and guidelines, developing a good sense of coding taste and intuition is vital. Familiarizing oneself with the Zen of Python, a collection of guiding principles for writing Pythonic code, can provide valuable insights into effective Python programming.

3. GitRoll’s Mission, Vision, Inclusion

We pay the utmost respect and appreciate all open-source contributors for their contributions to the world, which has always been pushing our world’s frontiers continuously to a higher level.

In our journey of developing GitRoll and collecting CVs globally starting from our previous ventures, we uncovered a stark reality. Junior CS graduates from prestigious universities demand high salaries, while talented developers in other regions ask for far less. We spoke with a developer from Palestine facing desperate circumstances and looking for opportunities on our platform. This disparity highlights the need for change.

GitRoll is our response to this injustice-like phenomenon. Our goal is to establish an unbiased, standardized, transparent benchmark, uncovering talented programmers. Join us in reshaping the tech talent landscape and advocating for equity in our industry. Together, we can make a meaningful impact and create positive change. We hope to revolutionize the tech talent market, by providing a platform where anyone can effectively showcase their coding abilities.