Slow speeds, runtime issues, and mobile application development woes have “ruined” Python’s future.
But how long will this trend continue? When will other languages replace Python, and why?
Python’s demise is hard to predict, so instead of giving an exact date, I’ll assess the strengths driving Python’s popularity now and the weaknesses that will lead to its demise in the future.
What advantages have made Python so popular today
Python’s success can be seen in Stack Overflow trends. This trend data counts the number of tags in posts on the platform. Given the sheer size of Stack Overflow, this is a good indicator of programming language popularity. Finally, if your time is not very tight, and you want to improve quickly, the most important thing is not afraid of hardship. I suggest you try to read these books:
- Book: Data Structures And Program Design Using Python – A Self-Teaching Introduction 2021
- ML Book: Machine Learning with Python Cookbook
- Book: Python Cookbook: Recipes for Mastering Python 3, 3rd Edition
Those are good, and developers can get improved quickly. As long as you can work super hard, you can give it a try.
The number of various programming languages is on StackOverflow.
R has held steady over the past few years, while the popularity of many other languages has steadily declined, but Python’s growth seems unstoppable. About 14% of StackOverflow problems are flagged as “Python,” and the trend is growing.
There are several factors behind this phenomenon.
Python’s Long History
Python has been around since the ’90s. Not only did this mean it had plenty of time to grow, but it also gave it a huge support community.
So if you run into any problems programming in Python, there’s a good chance you’ll solve it with a Google search because there’s a good chance someone has encountered your problem and written a helpful solution to it.
It’s friendly to beginners.
It’s not just that it’s been around for decades, giving programmers time to make great tutorials. But, more importantly, Python’s syntax is very readable.
First, it does not need to specify a data type. You have to declare one variable; Python will understand from the context whether it is an integer, floating-point, Boolean, or some other value. This is a huge advantage for beginners. If you program in C++, you know how frustrating it is to have your program fail to compile just because an integer replaces a floating-point number.
If you’ve compared Python to C++ code, you know how easy Python is to understand. On the other hand, although C++ was designed with readability in mind, it is pretty laborious to read compared to Python code.
It has a wide range of uses.
Because Python has been around for so long, developers have created packages for so many uses. So these days, you can find bags that work for just about everything.
- NumPy is your helper for working with numbers, vectors, and matrices.
- SciPy is the tool if you want to do calculations for the technology and engineering industry.
- Try Pandas if you want to make it big in data manipulation and analytics.
- Want to get started with ARTIFICIAL intelligence? Why not use SciKit-learn.
No matter what kind of computing task you’re trying to manage, there’s probably a Python package available. This keeps Python at the forefront of technology, as evidenced by the explosion in Python usage in machine learning over the past few years.
Python’s flaws, do they shake the foundation?
As mentioned above, you can imagine Python will remain popular for a long time to come. But like all technologies, Python has its weaknesses. I will analyze the most critical defects and evaluate whether they are fatal or not. Finally, if your time is not very tight, and you want to improve quickly, the most important thing is not afraid of hardship. I suggest you try to read these books. Those are good, and developers can get improved quickly. As long as you can work super hard, you can give it a try.
Python is slow, really slow. On average, it takes 2 to 10 times longer to complete a task in Python than in any other language.
There are several reasons for this. One is that Python is dynamically typed — remember, you don’t need to specify data types as you do in other languages. This means that it uses a lot of memory because the program needs to reserve enough space for every variable it might use. A lot of memory usage means a lot of computing time.
Another reason is that Python can only perform one task at a time. Again, this is a consequence of flexible datatypes — Python needs to ensure that there is only one data type per variable, and parallel processes can have problems here.
By contrast, your regular Web browser can run a dozen different threads at the same time. There are other factors as well.
But in the end, none of these speed issues matter. Computers and servers have become so cheap that it is only a fraction of a second slower. As a result, end-users don’t care that much whether their application loads in 0.001 seconds or 0.01 seconds.
At first, Python was dynamically scoped. This means that the compiler searches the current block first and then all the calling functions in turn to evaluate an expression.
The problem with dynamic scoping is that every expression needs to be tested in every possible context – it’s tedious. This is why most modern programming languages use static scopes.
Python tried to make the transition to static scope but failed. In general, an internal scope — such as a function within a function — can see and change an external scope. In Python, internal scopes can only see external scopes, not change them. It leads to a lot of confusion.
Although Python is quite flexible, the use of Lambda is quite limited. For example, Lambda can only be an expression in Python, not a statement.
Variable declarations and statements, on the other hand, are always statements. This means Lambda cannot be used for them.
This distinction between expressions and statements is entirely arbitrary and does not occur in other languages.
Whitespace makes code more readable but harder to maintain.
In Python, you can use Spaces and indentation to indicate different levels of code. This makes the code look friendly and easy to understand.
Other languages, such as C++, rely more on braces and semicolons. While this may not be aesthetically pleasing or beginner-friendly, it makes the code more maintainable. This approach is better for larger projects.
Newer languages like Haskell solve this problem: they rely on whitespace but provide an alternative syntax for those who wish to avoid it.
As we witness the mainstreaming of the software industry shift from the desktop to smartphones, it is clear that we need strong languages to build mobile software.
But not many mobile applications are developed using Python. That doesn’t mean it can’t do it — there’s a Python package called Kivy that does just that.
But Python was not designed for movement. Therefore, even though developers may use it to achieve acceptable results for basic tasks, it is best to use a language created for mobile application development. Some widely used mobile programming frameworks are ReactNative, Flutter, Iconic, and Cordova.
To be clear, laptops and desktops should be around for years to come. But with mobile traffic already exceeding desktop traffic, it’s safe to say that learning Python alone isn’t enough to become an experienced all-around developer.
Python scripts are not compiled and then executed. Instead, it compiles every time it executes, so any code errors show up at run-time. This leads to performance degradation, more time, and a lot of testing.
This is very useful for beginners because testing can teach them a lot. But for experienced developers, debugging a complex program in Python can throw them off course. This flaw is the most significant factor in setting timestamps on Python.
Which language could replace Python in the future?
Python has some nascent competitors in the programming language market:
- Rust provides the same security as Python – no variable can be accidentally overwritten. But it solves the performance problem through the concepts of ownership and borrowing. As a result, it is also one of the most popular programming languages of the past few years, according to StackOverflowInsights.
- Go is perfect for beginners who like Python. It’s also straightforward, and the code is much easier to maintain. An interesting point: Go developers are some of the highest-paid programmers on the market.
- Julia is a very new language that competes for head-on with Python. As a result, it fills a gap in large-scale technical computing. Typically, one would use Python or Matlab and patch the entire process flow with the C++ libraries necessary for large-scale computing. Now, people can use Julia and don’t have to speak two languages simultaneously.
While other language options are on the market, Rust, Go, and Julia successfully fill Python’s gaps. All of these languages excel in emerging areas of technology, especially artificial intelligence. While their market share is still tiny (as StackOverflow tag counts reflect), the trend is clear: up.
Given the current ubiquity of Python, it would undoubtedly take five or even a decade for any of these new languages to replace it.
Which language will win — Rust, Go, Julia, or some new language in the future — is hard to say.
But given the fundamental performance issues in the Python architecture, there will always be a place for some language.