Python Best Practices – The only guide to become Python Expert

Free Python course with 25 projects (coupon code: DATAFLAIR_PYTHON) Start Now

Like any other language or tool, Python has some best practices to follow before, during, and after the process of writing your code. These make the code readable and create a standard across the industry. Other developers working on the project should be able to read and understand your code. We have listed out a few of these for you to follow and write cleaner and more professional code. Do you follow any of these?

Important Python Best Practices

So, here come some important Python best practices which you should always keep in mind.

1. Create a Code Repository and Implement Version Control

If you have ever been on GitHub, you must have noticed that a regular project’s structure looks like this:


Python coding practices

Python coding best practices

When you start a new python project, you can create a code repository and implement version control. GitHub is one provider of this, but you can use any other. Let’s talk about the structure of a project, but before that, I recommend you to check DataFlair’s latest python project on Speech Emotion Recognition.

Structure of the Python Project:


This is in the root directory and is where you should add a license for your project. Some licenses are:

Mozilla Public License 2.0
Apache License 2.0
MIT License
The Unlicense


This is in the root directory too and is where you describe your project and what it does. You can write this in Markdown, reStructuredText, or plain text.

Module Code

This holds your actual code that may be inside a subdirectory or inside root.


This is not mandatory, but if you use this, you put it in the root directory. Here, you mention the modules and dependencies of the project- the things it will not run without.

This script in the root lets distutils build and distribute modules needed by the project.


Readable documentation is essential. This is placed in the docs directory.


Most projects have tests- keep these in the tests directory.

Before you move ahead in this Python best practices article, I want to share the Python master guide with you. Learn all the concepts through a single guide. 

2. Create Readable Documentation

So, next in python best practices is readable documentation. Like we just said, readable documentation is important. You may find it burdensome, but it creates clean code. For this, you can use Markdown, reStructuredText, Sphinx, or docstrings. Markdown and reStructuredText are markup languages with plain text formatting syntax to make it easier to mark up text and convert it to a format like HTML or PDF. reStructuredText lets you create in-line documentation. And Sphinx is a tool to easily create intelligent and beautiful documentation. It lets you generate Python documentation from existing reStructuredText. It also lets you export documentation in formats like HTML. Docstrings are documentation strings at the beginning of each module, class, or method.

3. Follow Style Guidelines

The PEP8 holds some great community-generated proposals. PEP stands for Python Enhancement Proposals- these are guidelines and standards for development. There are other PEPs like the PEP20, but PEP8 is the Python community bible for you if you want to properly style your code. This is to make sure all Python code looks and feels the same. One such guideline is to name classes with the CapWords convention.

  • Use proper naming conventions for variables, functions, methods, and more.
  • Variables, functions, methods, packages, modules: this_is_a_variable
  • Classes and exceptions: CapWords
  • Protected methods and internal functions: _single_leading_underscore
  • Private methods: __double_leading_underscore

Use 4 spaces for indentation. For more conventions, refer to PEP8.

For revising any python concept, refer to this free Python Tutorials Library

4. Correct Broken Code Immediately

Like with the broken code theory, correct your broken code immediately. If you let it be while you work on something else, it can lead to worse problems later. This is what Microsoft does. It once had a terrible production cycle with MS Word’s first version. So now, it follows a ‘zero defects methodology’, and always corrects bugs and defects before proceeding.

5. Use the PyPI Instead of Doing it Yourself

One of the reasons behind Python’s popularity is the PyPI- this is the Python Package Index; it has more than 198,190 projects at the time of writing. This is a repository of software for Python and has thousands of Python projects. You should use code from this instead of writing it yourself- this saves time and lets you focus on the more important things. The PyPI has projects about everything, and you can install these using pip. You can also create and upload your own package here. Follow this best practice in Python, this will help you a lot.

6. The Zen of Python

Tim Peters wrote this short poem to express what values you should follow while coding in Python. You can get this by running “import this” in the IDLE:

>>> import this

The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren’t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one– and preferably only one –obvious way to do it.
Although that way may not be obvious at first unless you’re Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it’s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea — let’s do more of those!

7. Use the Right Data Structures

Know the benefits and limitations of different data structures, and choose the right one for your code.

8. Write Readable Code

You should use line breaks and indent your code. Use naming conventions for identifiers- this makes it easier to understand the code. Use comments, and whitespaces around operators and assignments. Keep the maximum line length 79 characters. You should stay consistent and write readable code.

Explore amazing python projects at DataFlair and start coding now.

9. Use Virtual Environments

You should create a virtual environment for each project you create. This will avoid any library clashes, since different projects may need different versions of a certain library.

10. Write Object-Oriented Code

Python is an object-oriented language, and everything in Python is an object. You should use the object-oriented paradigm if writing code for Python. This has the advantages of data hiding and modularity. It allows reusability, modularity, polymorphism, data encapsulation, and inheritance. Also, you should write modular and non-repetitive code. Use classes and functions in your code.

11. What Not to Do

Avoid importing everything from a package- this pollutes the global namespace and can cause clashes. Don’t implement best practices from other languages. Don’t turn off error reporting during development- turn it off after it. Don’t alter sys.path, use distutils for that.

Now you know what Python best practices to follow while writing your next project. Which of these do you follow? Tell us in the comments.

Build your career in Python. Enroll now for the best Python Programming Online Course with amazing offers. 

7 Responses

  1. Abdul says:

    Hello there, Data Flair.
    Could you please explain well on this point, “Use virtual environment”? Do you mean we should install virtual boxes for each projects we work to avoid crashes please I try to think about virtual environment but i cant get well what is concept here.


  2. Albert - MVS Molina - gmail says:

    Hi, in terms of naming convention, my idea for a user define function or variable is to use the 2 character prefix combination for identification like uf_FunctionName() for a user define function, vn_VariableName for a numeric variable etc. or what is what is the better approach?

  3. Panther says:

    No. By this he means creating a new enviroment for every project because then you can prevent version clashes and local settings

  4. Alan Lee says:

    Avoid using your own naming convention. You will be confused once import codes from 3rd parties. Stick with the best practise, variable_name and FunctionName. No need to add whatever prefix or suffix to identify as “your” class. You have already put “your” code in “your” class folder for separation and version control. There is no need to identy it when “using” it.

  5. Praveen Reddy says:

    Hey Abdul, a virtual environment will help you to install all the required packages in a single place for each project.

    For example, you might be working on 2 projects. project-1 needs package1,2,3 and project-2 needs package4,5,6.
    So, you need not to install all 6 packages in your system and use them. instead you can install the first 3 packages in a dedicated env for project1 and other 3 in project2’s env.

  6. praveen says:

    Hey Abdul, a virtual environment will help you to install all the required packages in a single place for each project.

    For example, you might be working on 2 projects. project-1 needs package1,2,3 and project-2 needs package4,5,6.
    So, you need not to install all 6 packages in your system and use them. instead you can install the first 3 packages in a dedicated env for project1 and other 3 in project2’s env.

  7. CCR says:

    The words mean we should create new “venv” before we begin a new project. Not install a virtual box, this is another concept. You can look at this link “”. Hope that can help you.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.