Enhancing Python Code: Key Steps to Optimize Performance
Welcome to DS BizTech #15!
This issue brings you to Python code optimization. Python language stands as an instance of versatility and efficiency in programming, and we're delving deep into how to optimize Python Code performance to the maximum. Keep reading to learn more insights and explore Django Stars Cases of Python optimization, with a spotlight on practical strategies and insights.
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So, let’s dive into opportunities in Python performance optimization and what contexts it depends on.
Overview: Why Python is the Top Language for Code Crafting
The reasons to go for Python are simple and intelligible syntax, its ecosystem, and its relatively low entry barrier. Combined, these make the language an efficient tool in the developer’s hands.
This language is easy to read, maintain, and write in, and it all sums up Python efficiency, so it is appreciated by both engineers and stakeholders. Still, the language has maintained its place among the most widely used programming languages, according to Tiobe.
Python promotes writing clean, maintainable code by adhering to the following principle: There should be one–and preferably only one—obvious way to do it. As developers and engineers follow this guiding principle, the team makes sure Python code remains consistent and maintainable, even as it evolves or the team working on the product changes.
Python Slowness and Efficiency: Myth vs. Reality
For years, we’ve seen a lot of articles like “Why is Python slow?”, “How to speed up Python code?”, etc. These concerns view Python in a bad light but also show a lack of understanding of the language. And it's a big stereotype, as there is no “silver bullet” – the coolest, fastest, and most powerful programming language. If we define the terms “slow” and “fast” — both of these concepts characterize “speed”.
Read more: Asynchronous paradigm and Python AsyncIO.
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Python is a multi-purpose programming language, so it can be used to create all types of projects listed above and many more. In our context, we can define software speed as the time for which the algorithm of the given program is executed. And right now there is no “one-code-fits-all” solution for the universal problem of speed since the issues vary based on the context.
Discover all the misconceptions about Python speed and flexibility with our latest article about How to Optimize Python Code Performance 🔍 written by Alex Ryabtsev, Backend Competency & Tech Lead Django Stars. The article is based on Django Stars’ vast experience with real projects.
Industry News: The Django Stars' Perspective
👉 The White House recommends the use of memory-safe languages like Python. White House published a report recommending the use of memory-safe programming languages to eliminate an entire class of vulnerabilities affecting software. The Python Software Foundation’s response to the US Government's Request for Information noted Python's memory-safety, among other systems languages.
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