Python is widely adopted programming language and the wide range of its community allows for its continuous improvement and optimization.
As continuous efforts are made to make the language easy for everyone and not sacrifice its efficiency, so many have written on best practices, the do and don't.
In this article, I share the best practices that I have gathered through my personal experience and research, carefully filtered to provide you with the most valuable insights.
Avoid global variables
This appears in almost any programming article on "best practices", so I had to make it my number one point.
Writing global state variables comes with a lot of problems, like code readability issues, maintenance and namespace conflicts.
instead of global variables, you can instead use function parameters, local variables or class variables to minimize dependencies between different parts of your code.
The concept of DRY (Don't Repeat Yourself) emphasises the use of functions. Write reusable code for tasks that are going to appear in multiple parts of your code. This can greatly save time and reduces the amount of code you write.
Functions also help your code become modular, such that it allows you to break down complex code into smaller and more understandable ones.
Use virtual environment
Virtual environments are super useful and save tons of time. It allows you to manage dependencies and isolate your project dependencies and environment from the rest of your system.
Virtual environments enable you to have different versions of packages for different projects without conflicts and can ensure your working environment with all its dependencies is easily reproducible on any system.
Exception handling allows you to catch errors without crashing your entire program. It provides a great way for you to identify errors without having to deal with the issue of your entire program falling.
Additionally, with exceptions you can customize different set of solutions for different types of errors.
This is really a challenge for the majority of us, but truth be told, tests take your experience to a whole new level.
Tests allow you to verify your code works as expected under different conditions and inputs. It also ensures safety when refactoring your code, and makes it easier to catch and fix bugs before they make their way to production.
In conclusion, writing high-quality Python code requires knowledge, skill and attention to best practices. These tips and guidelines I have provided will hopefully help you write better and more reliable code.