5 Python Best Practices That Every Programmer Should Follow | by Pranjal Saxena | Towards Data Science

Python, one of the most popular languages of recent times, has a huge community of developers and programmers across the globe.
Many beginners, as well as experienced programmers, are taking up Python as their preferred programming language. Also, as it is an open-source language, sharing knowledge within the community is also prominent.
That is why it becomes necessary to follow some coding conventions that help to improve the quality and the readability of the code.
This article will discuss five such Python best practices that can be really useful for Python programmers to improve their coding experience.
Whether you are writing a small script for making a detailed project, having a well-structured code with proper names of the modules, correct indentations, and documentation improves the code's usability in the future.
Especially while making projects, you must include a README file for describing your project, the setup.py file for properly setting up your project in a new environment, and the requirements.txt for describing the dependencies that are needed. And, also last but not least, documentation describing how all the components of your projects work. You can also include some tests in your repository.
Incorporating comments in your code can be the first step of having proper documentation. For example, what is the role of a specific module in your code, how a function works, or the role of a function, and much more can be described using comments.
In Python, two types of comments are available. One is single-line comments which start with a hash symbol (#). These types of comments can help us describe a specific line of code like what an expression does.
print(“hello world”) #one line comment: this prints hello world
The other type is a multiline comment which starts and ends with triple quotes (‘’’). They are more useful while describing a module or a function.
Having meaningful comments and proper documentation improves the readability and usability of your code a lot.
‘’’ this is a comment that can
Take multiple
Lines ‘’’
One of the most common mistakes among beginner Python developers is improper naming of variables, classes, functions etc. We should always avoid writing single-letter variable names for heavily abbreviated function or class names.
e.g. a variable that stores the temperature of a substance should be named temperature or temp instead of t. The PEP-8 is a popular guideline among Python programmers, which tells us how to name our variables, classes and functions properly.
For example:
A long variable should be separated using underscores(_), e.g. long_variable_name
While writing class name CamelCase should be used, e.g. LongClassName
For writing functions using underscores is advised e.g. function_name()
The best way to improve the quality of the codebase is to make it more compact and modular. The module is like a collection of functions that you can use in your code when needed.
Python has many modules and libraries under a repository known as PyPI or the python package index. It contains numerous models written by Python developers, which you can implement in your code just by installing them from the internet. This prevents you from implementing all the logic from scratch and makes your script compact and readable.
Another good side of using modules is that most of these existing models are written by programmers with years of experience, and they can give you the best possible solution, which will take a lot of time for you to think from scratch.
The main purpose of a virtual environment in Python is to create a separate environment for Python projects. This becomes particularly useful when you are using any third-party module or library in your project.
If two projects use different versions of the same module (third party), it becomes difficult for python to understand which version is required for which project as both the versions are saved in the same directory with the same filename.
This problem can be easily tackled by using a virtual environment for each project where the dependencies are stored independently.
To use the virtual environment, we have to install it using the following line of code.
pip install virtualenv
And to create a new environment, we can use:
python3 -m venv env
This makes your life a lot easier when you are working on different projects at the same time.
When you are working on a professional project along with the team, it becomes really necessary that your team members understand your code.
Following these practices will help you with that as these inventions are being used by millions of python developers across the globe. You can also implement these principles while doing personal projects to improve your understanding of the project and reflect professionalism in your work.
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