One of the most widely used programming languages in recent years is Python. Due to several of its user-friendly qualities, the language has enjoyed prosperous years in the twenty-first century. Due to its accessibility and simple syntax, the programming language has emerged as the ideal coding language for novices. Additionally, it has other applications, including machine learning and web building.
Reasons For Python’s Popularity
Here are a few of the factors that contribute to Python’s popularity:
1. FLOSS (Free/Libre and Open Source Software) is software that is open source and free to use. A prime example of this is the Python programming language. It drastically reduces the cost of development for the tools that Python developers use because it is free.
2. Less code — Python is an easy-to-use and learning programming language. As a result, Python is very user-friendly compared to other general-purpose languages.
3. Beginner-Friendly– Python is user-friendly for beginners because it has very readable syntaxes. In Python programming, simply declaring a variable is sufficient; the data type is not required. Whether the data is an integer, float, boolean, or another type, it may infer the data type from the context.
Does that imply that Python’s prospects are excellent and that its growth trajectory is upward all the time? Alternatively, will Python replace other programming languages in the future? That might not be the case, though. There are drawbacks as well. Let’s look at the opposite.
Disadvantages of Python
Speed
Python is sluggish and extremely slow. Python typically takes 2–10 times more to perform a task than any other language does.
There are a number of causes behind that. One amongst them is that it’s dynamically typed; keep in mind that, unlike other languages, you don’t have to specify data types. Because the software needs to set aside sufficient room for every variable that it functions in any situation, a large amount of memory must be used. Additionally, extensive memory use requires extensive processing time.
Python can only carry out one job at a time, which is another factor. Python needs to make sure that every variable only has one data type, and parallel procedures could mess that up. This is a result of flexible data types.
In contrast, a typical web browser may manage twelve distinct threads concurrently.
Scope
Python was initially dynamically scoped. In essence, this means that a compiler first scans the current block, then subsequently all the calling functions, in order to evaluate an expression.
The issue with dynamic scoping is that each phrase must be rigorously verified in each potential context, which is time-consuming. Because of this, the majority of modern computer languages employ static scoping.
Python made an attempt to switch to static scoping but failed. Inner scopes, such as functions inside other functions, would often be capable of seeing and modifying outer scopes. Inner scopes in Python can only view outer scopes; they cannot modify them. There is a great deal of confusion as a result.
Mobile Development
It’s obvious that robust languages are needed to create mobile software as we observe the transition from desktop to smartphone.
However, Python-based mobile app development is a rare breed. That doesn’t imply it can’t be done, though; for this, there’s also a Python library called Kivy.
Python, however, wasn’t designed with smartphones in mind. So, even though it might deliver acceptable results for simple tasks, your best option is to choose a language designed for developing mobile apps. React Native, Flutter, Ionic, and Cordova are a few popular mobile development frameworks.
To be clear, desktop and laptop computers will continue to exist for a very long time. But it’s reasonable to argue that studying Python is insufficient to become a skilled all-around developer, given that mobile traffic has long overtaken that of the desktop.
Runtime Issues
A Python script isn’t first compiled, then run. Instead, it compiles each time you run it, causing any code mistakes to show up at runtime. This results in subpar performance, consumption of time, and a high number of test requirements. There are so many tests in the beginning phase only.
For novices, this is excellent because screening educates them a lot. However, attempting to debug a complicated Python application sends even seasoned coders astray. The main factor in setting a timer on Python is this speed issue.
Given Python’s current level of ubiquity, any one of these modern languages won’t be able to completely replace it for at least a decade.
It’s difficult to predict at this time which language it will be—Rust, Go, Julia, or a brand-new language of the future. However, one will surely replace it given the performance concerns inherent in Python’s architecture.