Writing clean code in Python is not merely a practice but a necessity in the world of software development. A cleaner code in Python ensures readability, efficiency, and maintainability. Clean code minimizes complexity, making it easier to understand for team members.

In this AI and data age,Â Python is considered one of the most popular programming languages. Itâ€™s simple to implement, is highly scalable, and provides extensive library support.

The article explores the essential practices that every Python developer should follow to maintain code quality and efficiency. By adopting these practices, developers not only enhance their codeâ€™s functionality but also make it more adaptable and easier for others to work with.

Python languageâ€™s diverse applications are data science, machine learning algorithms, backend development, scientific computing, and scripting.Â â€

Also Read:Â Most Popular Python Interview Questionsâ€

Python Best Practices for Indentation, Spacing, and Maximum Line Length

As part of Python’s best practices for coding one needs to follow these indentation and maximum line length guidelines:

When you indent the code, use four spaces instead of one tab. This makes the code more consistent and easy to read across various code editors.
The maximum length of a line is 79. Even if you split the screen in two, the characters will appear in one line without line wrapping.
In the case of docstrings and comments where the block of text is large, the maximum length of the line is 72 characters. Docstring is a string used to describe what the Python module, class, function, or method does.
Here is an example of a docstring:
â€œThis function simply wraps the â€œ+â€ operator, and does not do anything interesting, except for illustrating what the docstring of a very simple function looks like.â€

There should beÂ two blank linesÂ above theÂ definitionÂ of a class.
<blank line>
<blank line>
Class MyFirstClass

If we need to define a function in a class, we can provideÂ one blank lineÂ after the class.
Class MyFirstClass
<blank line>
def myfirstfunction(self);
<blank line>
def mysecondfunction(self);

If each function has variousÂ sections, then each function needs to be separated with a blank line. For instance:
Class MyClass
<blank line>
def myfunction1(self)
# section 1
<blank line>
# section 2Also Read:Â Basic Python Interview Questions You Should Prepare

Python Best Practices for Whitespaces

As part of Python’s best practices for writing code, let us now look at the best practices for the placement of whitespaces:

The rule of thumb for including white spaces is â€œNever Use Unnecessary Whitespaces. However, Include Them When Requiredâ€

Use whitespaces if you are defining a list. For instance, if you have a list like this:
myfirstlist = [1, 2, 3, 4]
Have spaces along operators. For exampleÂ D = 10 + 20 + 30Â instead ofÂ D=10+20+30

If we have a code like the one below:
Z = X**2 + Y**3
We neednâ€™t haveÂ spacesÂ betweenÂ x and * and * and 2Â but we need to have spaces betweenÂ X**2, + and Y**3
When we define a function, for instance
def myfunction (x, y, someotherthing = None)Â we need to have whitespaces between someotherthing, =, and None

When we call this function, for instance:
myfunction (10, 20, someotherthing=30), we donâ€™t need to put whitespaces before and after the operator â€˜=â€™)

Python Best Practices for Importing Libraries

As part of the best practices for coding in Python, now letâ€™s look at the best practices for importing libraries:

Import libraries in different lines:
import mathÂ # line 1
import osÂ # line 2
Import sysÂ # line 3and notimport math, os, sys

If you want to import different functions from the same import library you can do so in the same linefrom sklearn.preprocessing import StandardScalar, MinMaxScalarand not
from sklearn.preprocessing import StandardScalar
from sklearn.preprocessing import MinMaxScalar
When importing libraries use wildcard characters minimallyFor example:
from sklearn.preprocessing import *This would import everything, even the libraries and functions you donâ€™t need.
When we are importing modules from different categories import them separately. Start by importing core Python libraries, then third-party libraries, and then local implementations.For instance:import os
import sys
import math

These three are core Python libraries

Import Numpy as np
Import matplotlib.pyplot as plt

These three are third-party libraries

Import audio

These three are local implementations

Also read:Â How to Stand Out in a Python Coding Interview

â€Python Best Practices to Import Commentsâ€

As part of Pythonâ€™s best practices for writing code, let’s look at the guidelines for importing comments.Always write comments in English unless you are 100% sure that you are writing comments for only a specific set of audiences who donâ€™t know English.

For instance, use the German language if only German speakers will view the code or the French language when only French language speakers will view the code.

English is considered to be the language of the Internet, so always use it for commenting in Python.

Use inline comments sparingly, especially when you donâ€™t need them:

For instance:

def myfirstfunction ();

X = Y**2Â #Â square Y and save it to X

The comment â€œsquare Y and save it to Xâ€ is irrelevant and adds no value to the reader, the operation isÂ too basic for any skilled Python programmer to understand.

However, if the function has something that is relevant in the context of the code but is not obvious, then include it in the comment.

As a general convention put two spaces after the #.

Python Best Practices: Naming Conventions

As part of best practices for writing better Python code, now letâ€™s look at the naming conventions for functions, variables, constants, and classes

ForÂ functionsÂ andÂ variables,Â naming conventions have to beÂ lowercase wordsÂ andÂ underscores

def iterative_fibonacci_solution

and not

def IterativeFibonacciSolutionÂ is an incorrect convention

Class names should start with uppercase and must followÂ camelCaseÂ (upper-lower) convention.Â For example:Â Class MyfirstClass
Use all capital letters for Constants.Â For example:Â PI=3.14
Use all lowercase for Modules: Add underscores between words if it improves readability.
Donâ€™t use single-character names in Python
Never use Python built-in names, such as naming a variable with the name â€œlist.â€

Python Best Practices: A Few Other Coding Recommendations

As part of best practices for coding in Python now look at a few recommendations for efficient coding.

Always Close and Opened File
For example:
try:
file = open(‘myfirstfile.txt’)
file.write(‘Hello Everyone’)
except Exception as e:
print(â€œFile canâ€™t be opened :â€, e)
finally:
# Make sure to close the file after
file.close()

Document Everything
Document everything that needs to be documented
def get_fehrenheit(celcius):
Here we document the function by providing its summary and description.
â€Summary: getting Fahrenheit from Celcius
Description: This function takes Celsius as an argument and returns Fahrenheit as an output.

Put Reusable Functions in Utils.Py
Instead of creating similar functions again and again, common functions can be put into a separateÂ utils.py fileÂ and can be reused several times.
If you need to read three files, instead of writing a code for reading a file three times, you can read it as a function and save time.
For example:
# function to read the file read
def file_read(filename):
with open(filename, ‘z’) as f:
return z.read()
parameters = file_read(‘parameter.txt’)
description = file_read(‘description.txt’)
summary = file_read(‘summary.txt’)â€

Tools Used to Implement These Practicesâ€

Various tools can help enforce these standards, including formatting tools, static code analysis tools, and integrated development environments (IDEs).

Some popular checkers include Pylint and Pyright.

Popular auto formatting tools include Black and AutoPep8

Common IDEs include PyCharm, Sublime Text, and Visual Studio Code

Learn Python With Interview Kickstart

Owing to its ease of use and versatility across various programming paradigms, Python is being taught as part of the curriculum in several courses of Interview Kickstart.

Interview Kickstartâ€™sÂ data science courseÂ covers all basic and advanced data science concepts including a module on Programming with Python.

The comprehensiveÂ machine learning courseÂ offered by Interview Kickstart. covers fundamental concepts to advanced machine-learning techniques. Python is covered as part of the 2-month foundation module.

Interview Kickstartâ€™sÂ Applied Gen AI courseÂ has a separate curriculum for software engineers, product managers, and tech professionals. However, all the 3 streams of the course have a 2-week foundation module in Python Programming.

Tech leads and hiring managers at top-tier IT companies have created these courses. They are also instructors of these courses making them idealÂ tech interview prep coursesÂ for AI career aspirants.

You can also read theÂ success storiesÂ of professionals who have acquired high-paying jobs in the AI/ML space after completing any of these courses offered by IK.

FAQs: Python Best Practices for Writing Clean and Efficient Codeâ€

Why Do We Need to Follow Python Best Practices?
Python coding best practices ensure that the code is consistently clean and readable. By following Python best practices, code reusability becomes easier, likelihood of bugs reduces, maintainence and restructuring becomes easier.â€

What Problems Can Python Solve?
Python is used for various tasks including task automation, data visualization, making websites and software. Itâ€™s easy to learn and implement, making it a language of choice for data scientists, machine learning engineers, even scientists and accounts for a range of day-to-day tasks.â€

Which Python Compiler Is the Fastest?
Among all the Python compilers,Â PyPy is considered to be the fastest for many applications. It also has better memory management. According to reported benchmarks, it is 7.6 times faster than CPython, the default Python compiler and interpreter.â€

Is Python a Compiler Language?
Python is first compiled and then line-by-line interpreted. So, we can call it a compiled and interpreted language.â€

Related Articlesâ€â€

Python Best Practices for Writing Clean and Readable Code

Your Resume Is Costing You Interviews

Uplevel your career with AI/ML/GenAI

Select a Date

Time slots

Java Float vs. Double: Precision and Performance Considerations Java

.NET Core vs. .NET Framework: Navigating the .NET Ecosystem

How We Created a Culture of Empowerment in a Fully Remote Company

How to Get Remote Web Developer Jobs in 2021

Contractor vs. Full-time Employment â€” Which Is Better for Software Engineers?

Coding Interview Cheat Sheet for Software Engineers and Engineering Managers

Top Python Scripting Interview Questions and Answers You Should Practice

Zoox Software Engineer Interview Questions to Crack Your Tech Interview

Zoox Software Engineer Interview Questions to Crack Your Tech Interview

Rubrik Interview Questions for Software Engineers

Top Advanced SQL Interview Questions and Answers

Twilio Interview Questions

Ready to Enroll?

Next webinar starts in

Technical Interview prep program

By Role

By Domain

By Platform

AI/Gen AI courses

Certification by Skill

By Role

Masterclass

Resources

About IK

Your PDF Is One Step Away!

Your PDF Is One Step Away!

Your PDF Is One Step Away!

Enter Your Details

🔥 While You Wait – Want to Go Deeper?

Register for our webinar

How to Nail your next Technical Interview

Select a Date

Time slots

Get tech interview-ready to navigate a tough job market

Next webinar starts in