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Clean Code with Python: A Beginner's Guide to Writing Readable and Maintainable Code

12 September 2023

Clean Code with Python: A Beginner's Guide to Writing Readable and Maintainable Code

Clean code is crucial for writing Python programs that are readable, reusable, and maintainable. While there are no strict rules for clean coding, there are many widely accepted practices and principles. This guide covers core concepts and top tips for improving code quality in Python. This is part of our series on Python and clean coding which is intended as a resource for JBI Trainings Clean Code with Python Course

Clean Code



What is Clean Code?

Clean code refers to source code that is easy to understand and adapt. The code should be readable, modular, documented, tested, and free of duplication. Overall, clean code aims to:

  • Improve readability and understandability
  • Simplify maintenance and updates
  • Reduce complexity and cognitive load
  • Facilitate collaboration and code reviews

Well-structured code makes it easier for developers to quickly comprehend what the code does without excessive cognitive effort. Clean code also enables more efficient troubleshooting, updating, and reuse.

Adhering to clean code principles is especially important for large, complex, and long-running projects where many developers collaborate over time. However, all code benefits from improved readability, extensibility, and reduced mistakes.

Characteristics of Clean Code

Although subjective, clean code often exhibits several key characteristics:

  • Descriptive names: Functions, variables, classes, etc. have intention-revealing names.
  • Modular design: Logical components are split into modules with minimal dependencies.
  • Reusability: Code contains generic functions and components usable across the codebase.
  • DRY principle: Logic is refactored to avoid duplication.
  • Readability: Code structure and style follows standard conventions with proper whitespace.
  • Simplicity: Straightforward logic that minimizes complexity and cognitive load.
  • Defensive coding: Input validation and error handling prevent bugs and failures.
  • Comments: Code is documented with effective comments explaining intent and complex sections.

Clean Coding Best Practices

Here are some recommended practices for writing cleaner Python code:

Use Descriptive Naming

Use intention-revealing names for functions, variables, classes, modules, etc. Descriptive names help improve understanding of code purpose and logic.


x = 43


age_in_years = 43

Refactor into Smaller Functions

Break code into smaller single-purpose functions rather than large complex functions. Smaller functions are easier to understand.


# long complex function
def process_data():
  # many lines of code 


# refactored into smaller functions
def extract_data():
  # get data
def filter_data():
  # filter data
def validate_data():
  # validate data
def load_data():
  # load data

Limit Line Length

Limit lines to around 79 characters to improve readability. Long lines are harder to scan visually.

Use Whitespace Properly

Use consistent indentation, vertical alignment, and blank lines to group related logic and make the flow more scannable.

Write DRY Code

Eliminate duplication in your codebase by refactoring repeated logic into reusable functions, modules, or classes.

Use Descriptive Comments

Use comments to explain intention behind complex logic sections, nuances in the code, edge cases to handle, etc. Avoid extraneous comments that just repeat the code.

Handle Exceptions

Handle errors and abnormal conditions properly with try/except blocks and raise meaningful exception messages. Do not ignore or fail silently.

Validate Input Data

Check user inputs, configure validation logic, and provide useful error messages to avoid bad data crashes.

Writing Clean Functions

Functions form the building blocks of most Python programs. Follow these principles for clean functions:

  • Do one thing: Functions should perform one specific task.
  • Use good names: Choose an intention-revealing name that summarizes what the function does.
  • Limit arguments: Ideal number of arguments is 0 to 3. More indicates a function doing too many things.
  • Avoid side effects: Functions should not modify states outside their scope like global variables, input arguments, etc.
  • Return values rather than modify inputs: Avoid in-place mutation of input arguments in a function.
  • Avoid deep nesting and complexity: Refactor nested if/else, for loops, etc. into separate helper functions.
  • Raise exceptions: Throw exceptions on errors instead of returning None or error flags. The calling code should handle exceptions.

Example Clean Function:

def calculate_avg_rating(rating_list):
  """Calculates average movie rating from a rating list"""
  if not isinstance(rating_list, list):
    raise ValueError('Invalid rating list')
  if len(rating_list) == 0:
    raise ValueError('Empty rating list')
  ratings_sum = 0
  for rating in rating_list:
    if rating < 1 or rating > 5:
      raise ValueError('Invalid rating')
    ratings_sum += rating
  return ratings_sum / len(rating_list)  

This function:

  • Performs one specific task
  • Has a descriptive name
  • Checks input validation
  • Avoids side effects
  • Returns value rather than modifying input
  • Handles invalid cases by raising exceptions
  • Keeps code nesting minimal

Writing Modular Code

Modular code splits the program into logically independent modules that can be developed and maintained more easily.

Guidelines for Modular Code:

  • Split code into logical modules (packages, modules, classes)
  • Minimize dependencies between modules
  • Define loose coupling interfaces between modules
  • Encapsulate implementation details within modules
  • Reuse common utility modules across project

Modular design enhances readability since each module focuses on a specific capability. It also allows easier updating, testing, and collaboration by decoupling components.

Improving Readability

Make code easier to quickly parse and understand by:

  • Following PEP8 style guide for code layout and conventions
  • Limiting line length
  • Using blank lines wisely to separate logical sections
  • Using descriptive names and comments
  • Keeping functions and classes short and concise
  • Handling edge cases and errors properly
  • Avoiding deeply nested or complex code blocks
  • Using Python debugger to step through code

Well-formatted code with descriptive naming and comments enables faster comprehension with minimal cognitive load.

Testing Your Code

Thorough testing is essential for clean code to catch bugs and edge cases early.

Testing Best Practices:

  • Write unit tests for modules and functions using unittest or pytest
  • Test with valid and invalid inputs for robustness
  • Leverage test doubles like mocks, stubs and fakes for dependencies
  • Check edge cases and failures pathways
  • Measure test coverage percentage
  • Run static analysis with linters like pyflakes, pylint
  • Enable Continuous Integration (CI) testing

Testing helps validate code quality and prevent regressions when updating code.

Adopting Python Best Practices

Some overall Python best practices for clean code:

  • Follow PEP8 style guide for code layout and formatting
  • Add type hints for function arguments and return values
  • Employ Pythonic idioms like list comprehension
  • Use if __name__ == '__main__' idiom
  • Leverage Python's rich standard library
  • Prefer generators like itertools instead of returning giant lists
  • Use Python debugger (pdb) for debugging
  • Enable linting, formatting, type checking

Python has many built-in capabilities to facilitate clean code. Leverage them fully when coding.

Automating Clean Code Enforcement

Adopting clean coding best practices can be accelerated through automation using modern tools:

  • Linters like pycodestyle, flake8 detect style violations
  • Autoformatters like black, autopep8 reformat code
  • Type checkers like mypy validate type hints
  • CI/CD pipelines run automated testing and checks
  • Pre-commit hooks with pre-commit validate changes
  • Code quality tools like radon measure complexity

These tools complement human code reviews for enforcing clean code standards and preventing mistakes early.

Clean Code Checklist

Here is a quick checklist to refer to when writing or reviewing Python code:

Python Clean Code Checklist

  • [ ] Intention-revealing naming
  • [ ] Modular design and separation
  • [ ] DRY code with minimal duplication
  • [ ] Descriptive comments where required
  • [ ] Input validation and error handling
  • [ ] Unit testing and edge cases covered
  • [ ] Follows PEP8 style guide
  • [ ] Short and simple functions/classes/modules
  • [ ] Whitespace, indentation and formatting
  • [ ] Docstrings for modules/functions/classes
  • [ ] Minimal dependencies between components
  • [ ] Generic reusable utilities and functions
  • [ ] Configuration externalized from code

Frequently Asked Questions

Q: What are some benefits of clean code?

A: Clean code improves readability, reduces bugs, lowers maintenance costs, enables collaboration, and allows faster feature development.

Q: Does clean code take longer to write initially?

A: Yes, it often requires more initial time investment to modularize, document, and test code. But it pays long-term dividends in maintainability.

Q: What are some indicators of messy code?

A: Duplicated logic, long functions/classes/files, deep nesting, lack of comments, dead code, and no tests are signals of unclean code.

Q: Can you show an example of a clean Python function?

A: Here is an example function that validates inputs, does one thing, uses a descriptive name, handles errors well, and minimizes complexity:

def validate_email(email):
  """Validates if email is in standard email format<name>@<domain> by checking for @"""
  if '@' not in email:
    raise ValueError('Invalid email format')
  name, domain = email.split('@')

  if len(name) < 1 or len(domain) < 3:
    raise ValueError('Invalid email components')

  return True

Key Takeaways

Some core principles to remember when writing clean Python code:

  • Strive for code that is readable, reusable and maintainable
  • Use descriptive naming, comments, error handling, and validation
  • Modularize code into logical components with minimal coupling
  • Refactor duplicate logic into reusable functions/classes
  • Write tests to cover edge cases and prevent bugs
  • Automate standards enforcement with linters and formatting
  • Iterate code quality through reviews and continuous improvement

While following all clean code principles can be difficult, aiming to improve parts of your codebase incrementally will pay dividends in the long run through more maintainable and extensible Python programs.

You might enjoy our next article How to Write Clean Code with Python: A Step-by-Step Guide or consider one of our training courses below. 

Or consider a course with JBI Training: Your Path to Code Excellence

At JBI Training, we understand that the success of every software project hinges on the quality of the codebase. That's why we're dedicated to equipping developers with the skills they need to produce clean, efficient, and maintainable code. Our comprehensive training programs cover a range of programming languages and practices, ensuring that you're always ahead of the curve in the ever-evolving world of software development.

  • Clean Code with Python: Master the art of writing clean, readable, and maintainable Python code. This course teaches you essential coding principles and best practices to ensure your Python projects are a joy to work on, whether you're a beginner or an experienced developer.
  • Clean Code with Javascript: Elevate your JavaScript skills to the next level with our Clean Code with JavaScript course. Learn how to write clear, concise, and efficient JavaScript code that not only solves problems but also enhances code maintainability.
  • Code Reviews for Developers: Effective code reviews are the cornerstone of collaborative software development. This course provides insights and techniques to conduct and participate in code reviews that result in improved code quality and foster a culture of continuous improvement.
  • Professional Code Practices: Take your coding skills to a professional level. This course covers a wide range of industry-standard practices, tools, and methodologies that will make you a standout developer, whether you're just starting your career or looking to advance it.
  • Python: Get a comprehensive introduction to Python, one of the most versatile and widely used programming languages. This course covers the fundamentals, syntax, and best practices to start building robust Python applications.
  • Python (Advanced): Ready to push the boundaries of Python development? Our advanced Python course delves into more complex topics and advanced techniques, equipping you with the skills to tackle challenging projects and excel in your Python career.

Why Train with JBI Training?

At JBI Training, we don't just teach code; we empower developers to excel in their careers and contribute to high-quality software projects. Our instructors are industry experts with real-world experience, and our courses are designed to be practical, hands-on, and up-to-date with the latest industry trends. Join us on a journey to become a more proficient and effective developer, armed with the knowledge and skills you need to make a lasting impact in the world of software development.

About the author: Daniel West
Tech Blogger & Researcher for JBI Training

+44 (0)20 8446 7555

[email protected]



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