Python programming language is one of the most popular programming languages of recent years. The language has seen bright years in the 21st century due to many of its user-friendly features. The python programming language has become the perfect coding language for beginners due to its readability and straightforward syntax. Besides, it has versatile usage from web development to machine learning. The below graph of stack overflow depicts the steady progress of Python in recent years. 

Not to mention, achieving this growth rate is not very easy. But one of the reasons for this growth rate is its versatile use in different application areas like app development, web development, scientific work like machine learning, game development, and many more.

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Reasons for Python’s popularity

Some of the reasons why the python programming language is popular are as follows:

1.    Open source and Free of charge — Python programming language is an excellent example of FLOSS (Free/Libré and Open Source Software). Since it is free, it reduces the overall development cost for the tools python developers are using for development.

2.    No support issue — Support is an essential criterion for any programming language that most programming languages face as a problem. Then there is an issue with proper documentation, which becomes tricky for developers during the build of a project. However, Python does not have such problems.

3.    Cross-platform support— Python programming language supports different operating systems like Ubuntu, Linux, Windows, etc. So, you can write code once and run it on any of those OSs. 

4. Fewer codes — Python programming language is simple to use and write. Hence, comparing to other general-purpose languages, Python is extremely convenient to use.

5.    Beginner-friendly: Python comes with very human-readable syntaxes. For Python programming, you don’t need to specify the data type; only declaring a variable is enough. It can understand the data type from the context, whether it is an integer, float, Boolean, or something else.

6.    Versatile: Python has a package for almost everything. For numbers, vectors, or metrics, it has NumPy. Similarly, for big data analysis, it has Pandas. For engineering calculation, it has SciPy or for artificial intelligence – Scikit-Learn. So, for every kind of computational task, there is one Python package.

Does that mean everything is rosy with the Python programming language, and the growth curve is continuously going to be upward? Or is Python going to be the programming language of the future? Well, that might not be the case. There are downsides too. Let’s explore the flip side.

Disadvantages of Python

Python is significantly slow. It may be easy to write, but you need 2-10 times more time to complete a task with Python than another programming. One of the primary reasons for that is Python is dynamically typed as you don’t need to specify data type during coding. It takes a lot of memory as the program reserves enough space for each of its unknown variables. Since lots of memory space is utilized, it takes lots of computing time. 

Additionally, Python can execute one task at a time. This is also because of the variable whose data types are not pre-defined. If you consider parallel processing here, then it would mess up. This is a drawback of Python even if you compare it with an average web browser, which can run multiple web browsers at once.

Scope: Python was dynamically scoped by default. This basically means if you want to evaluate an expression, a compiler starts its searching from the current block and then successively moves to all the calling functions. This means every expression will have to be tested in every possible context. This is a flaw that most modern languages overcome with static scoping.

Although Python intended for static scoping, it did not happen. In this case, functions within functions can see the change of the outer scope or functions. But in the case of Python, inner functions only can see the outer functions. However, they cannot change them. This creates a lot of confusion.

Lambdas: Python has many flexibilities, but interestingly, the usage of Lambdas is restrictive. You can use Lambdas only as an expression in Python and not the statements. On the contrary, variable declarations and statements are always statements. So, you can use Lambdas for them. This is indeed an arbitrary distinction between statements and expressions, which is not the case in other programming languages.

Whitespaces: In Python, to indicate different code levels, there is the use of whitespaces and indentations. However, this makes the code visually appealing and intuitive to understand but does not make the code maintainable. Comparing Python with other high-level programming languages like C++ mostly relies on semicolons and braces. As a beginner, it might be a little clumsy to match the braces or easy to make a mistake in putting semicolons, but it makes code more useful in case of bigger projects.

Mobile Development: Mobile development is an ever-growing area as we are observing more shifting from desktop to smartphone. This needs a robust language to build mobile software. Many programming languages are flourishing in this area.

However, we don’t see an aggressive move of Python in this promising area. Yes, we have Kivy – a Python package for this purpose, and it’s not like Python cannot make any result in mobile development, but honestly speaking, Python is not made for mobile development. We see wide use of Flutter, React Native, Ionic, and Cordova frameworks in this context. 

So, we can say learning Python is not the programming language of the future for mobile development.

Runtime Errors: Unlike other programming languages, a Python script does not compile first and then run. Instead, it compiles every time you execute the code. So, a coding error can generate at runtime. This leads to time consumption, poor performance, and the need for a lot of tests.

This might sound good for beginners since beginners can learn a lot through testing. But for seasoned developers, debugging a complex program in Python makes them bored. This performance issue sets timestamps on Python.

Languages that can replace Python in the future

A few new competitors on the market play as the fix weak points of Python programming language. Here are three key of them which are hitting Python’s weak areas very effectively and prevent it from becoming the programming language of the future:

·        Rust has the Python-like feature like restricting variable overwriting by accidentally. However, it has addressed the performance issue with the concept of borrowing and ownership. It is one of the most-loved programming languages of the last few years, according to StackOverflow Insights.

·        Go is excellent for beginners like Python. Besides, it’s code maintenance is also very easy. Go developers are among the highest-paid programmers on the market.

·        Julia is a very new language, but it competes head-on with Python. It is perfect for large-scale technical computations: Suppose for technical computation, you need Python or Matlab and aggregate the whole thing up with C++ libraries. With Julia, you no need to juggle with two languages.

Final thought

It is an undeniable fact that Python has ubiquitous popularity at the moment, and it is not going to be an overnight journey for competitive languages to overcome Python programming language. It may take 5-10 years for these new languages to replace Python’s popularity. 

However, it is hard to say which language will overtake Python in the future – Rust, Go, or Julia or something new. But Python’s performance issue is undoubtedly its most significant drawback, and a fundamental part of architecture will inevitably replace it in the future. So, we can conclude Python is not going to be the programming language of the future.

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