12 September 2023
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 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:
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.
Although subjective, clean code often exhibits several key characteristics:
Here are some recommended practices for writing cleaner Python code:
Use intention-revealing names for functions, variables, classes, modules, etc. Descriptive names help improve understanding of code purpose and logic.
x = 43 foobar()
age_in_years = 43 calculate_average_price()
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 lines to around 79 characters to improve readability. Long lines are harder to scan visually.
Use consistent indentation, vertical alignment, and blank lines to group related logic and make the flow more scannable.
Eliminate duplication in your codebase by refactoring repeated logic into reusable functions, modules, or classes.
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 errors and abnormal conditions properly with
try/except blocks and raise meaningful exception messages. Do not ignore or fail silently.
Check user inputs, configure validation logic, and provide useful error messages to avoid bad data crashes.
Functions form the building blocks of most Python programs. Follow these principles for clean functions:
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)
Modular code splits the program into logically independent modules that can be developed and maintained more easily.
Guidelines for Modular Code:
Modular design enhances readability since each module focuses on a specific capability. It also allows easier updating, testing, and collaboration by decoupling components.
Make code easier to quickly parse and understand by:
Well-formatted code with descriptive naming and comments enables faster comprehension with minimal cognitive load.
Thorough testing is essential for clean code to catch bugs and edge cases early.
Testing Best Practices:
Testing helps validate code quality and prevent regressions when updating code.
Some overall Python best practices for clean code:
if __name__ == '__main__'idiom
pdb) for debugging
Python has many built-in capabilities to facilitate clean code. Leverage them fully when coding.
Adopting clean coding best practices can be accelerated through automation using modern tools:
flake8detect style violations
mypyvalidate type hints
These tools complement human code reviews for enforcing clean code standards and preventing mistakes early.
Here is a quick checklist to refer to when writing or reviewing Python code:
Python Clean Code Checklist
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
Some core principles to remember when writing clean Python code:
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.
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