Python is among the favorite programming languages in the software developer community. It’s a great tool not just to produce quality code but also for new programmers to learn the basics of programming.
Easy syntax and multi-paradigm design make Python a valuable asset for software developers. But at the same time, it has its set of complications that can often frustrate the developers.
With many other alternatives silently entering the market, these loopholes need to be covered if Python must stay on top.
The Sudden Surge of Python
As surprising as it may sound, Python is not a newbie among programming languages. But developers only understood its value around 2010, and since then, it has changed the landscape of the development community.
So, what took Python so long to reach where it is right now? Since Python is an excellent language for production and enterprise projects, developers realized its true potential as the software industry hit it off in the 2010s.
And as the era of artificial intelligence emerged, Python became the ultimate programming language for machine learning applications.
What Python Must Improve to Stay on Top
Here is a look at some areas where Python causes problems for the developers:
- The Need to Improve Speed
Speed is a significant problem with Python as it can be up to 10 times slower than other programming languages. It’s an important area for improvement, and there are a few reasons why this problem persists with Python.
Firstly, you can type dynamically, so there is no need to assign data types to variables. However, while it may be great for the developers, it means more memory consumption as the program must reserve more memory for each variable. And with higher memory access, the processing time would also increase.
Moreover, Python may offer flexible data types meaning that it can execute only one task at a time. Compared to Python, an average browser can run multiple threads at once.
- Lambdas are Restrictive
Python works well with Lambdas, but they are only usable as expressions. You cannot implement lambdas as Python statements, which restricts the use of such flexible tools in Python.
It also creates a distinction between statements and expressions. Interestingly, you won’t see this distinction in other programming languages.
- Dynamic Scoping
Unlike many other languages that are statically scoped, Python is dynamically scoped. It means that Python tests an expression in every possible way. So, understandably, the process becomes longer and more complex.
What is Dynamic Scoping?
Here, it’s important to understand dynamic scoping too. In Dynamic Scoping, the compiler searches the current stack and then jumps to call more functions. Unfortunately, the process is quite complicated, and it’s therefore absent in most modern languages.
Even though Python tried shifting to static scoping, it only led to further confusion.
- Not the Best for Mobile Development
Even though Python introduced Kivy for mobile development, it’s not as widely used as some of the other mobile development languages. It’s primarily because Python isn’t a natural language for mobile development.
Considering that the focus is shifting from desktop to mobile app development, Python has some fierce competitors like Flutter, Cordova and React Native, etc., to deal with.
Possible Substitutes for Python
Since Python has some highly addressable flaws, it’s possible for any of the following programming languages to replace Python in the future:
- Rust – A highly preferred language according to a Stackoverflow survey.
- Julia – A one-stop solution for high-scale computation.
- Go – Much like Python and one of the best paying languages.
Python has some apparent competitors to deal with. Therefore, unless it improves its computation power, scoping, indentation issues, and comes up with more accessible mobile development features, it may lose its immense popularity in the tech circles.