Python Best Practices for Writing Clean and Readable Code in 2026

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, Python is considered one of the most popular programming languages. It’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 code’s functionality but also make it more adaptable and easier for others to work with.

Python language’s diverse applications are data science, machine learning algorithms, backend development, scientific computing, and scripting. ‍

Also Read: Most Popular Python Interview Questions‍

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:
“This function simply wraps the “+” operator, and does not do anything interesting, except for illustrating what the docstring of a very simple function looks like.”

There should be two blank lines above the definition of a class.
<blank line>
<blank line>
Class MyFirstClass

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

If each function has various 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: 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 “Never Use Unnecessary Whitespaces. However, Include Them When Required”

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 example D = 10 + 20 + 30 instead of D=10+20+30

If we have a code like the one below:
Z = X**2 + Y**3
We needn’t have spaces between x and * and * and 2 but we need to have spaces between X**2, + and Y**3
When we define a function, for instance
def myfunction (x, y, someotherthing = None) we need to have whitespaces between someotherthing, =, and None

When we call this function, for instance:
myfunction (10, 20, someotherthing=30), we don’t need to put whitespaces before and after the operator ‘=’)

Python Best Practices for Importing Libraries

As part of the best practices for coding in Python, now let’s look at the best practices for importing libraries:

Import libraries in different lines:
import math # line 1
import os # line 2
Import sys # 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 don’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: How to Stand Out in a Python Coding Interview

‍Python Best Practices to Import Comments‍

As part of Python’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 don’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 don’t need them:

For instance:

def myfirstfunction ();

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

The comment “square Y and save it to X” is irrelevant and adds no value to the reader, the operation is 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 let’s look at the naming conventions for functions, variables, constants, and classes

For functions and variables, naming conventions have to be lowercase words and underscores

def iterative_fibonacci_solution

and not

def IterativeFibonacciSolution is an incorrect convention

Class names should start with uppercase and must follow camelCase (upper-lower) convention. For example: Class MyfirstClass
Use all capital letters for Constants. For example: PI=3.14
Use all lowercase for Modules: Add underscores between words if it improves readability.
Don’t use single-character names in Python
Never use Python built-in names, such as naming a variable with the name “list.”

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(“File can’t be opened :”, 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.
‍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 separate utils.py file 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’)‍

Tools Used to Implement These Practices‍

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 Kickstart’s data science course covers all basic and advanced data science concepts including a module on Programming with Python.

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

Interview Kickstart’s Applied Gen AI course 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 ideal tech interview prep courses for AI career aspirants.

You can also read the success stories 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 Code‍

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.‍

What Problems Can Python Solve?
Python is used for various tasks including task automation, data visualization, making websites and software. It’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.‍

Which Python Compiler Is the Fastest?
Among all the Python compilers, 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.‍

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.‍

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