Python Performance Optimization
Introduction
Resources are never sufficient to meet growing needs in most industries, and now especially in technology as it carves its way deeper into our lives. Technology makes life easier and more convenient and it is able to evolve and become better over time.
This increased reliance on technology has come at the expense of the computing resources available. As a result, more powerful computers are being developed and the optimization of code has never been more crucial.
Application performance requirements are rising more than our hardware can keep up with. To combat this, people have come up with many strategies to utilize resources more efficiently – Containerizing, Reactive (Asynchronous) Applications, etc.
Though, the first step we should take, and by far the easiest one to take into consideration, is code optimization. We need to write code that performs better and utilizes less computing resources.
In this article, we will optimize common patterns and procedures in Python programming in an effort to boost the performance and enhance the utilization of the available computing resources.
Problem with Performance
As software solutions scale, performance becomes more crucial and issues become more grand and visible. When we are writing code