Why is Python gaining more and more popularity nowadays?

According to Stack Overflow Python is the fastest-growing programming language, and it has been around for more than 28 years now. Since its first release in 1991, Python has changed considerably over the years, but it’s still used for the same things it was back then. As a matter of fact that’s just one of the reasons why it has become so popular in recent years — it’s a production-based language meant for enterprise and first-class projects, and it has a long history. It can be used for just about anything, which is why it’s considered so versatile. You can build Raspberry Pi applications, scripts for desktop programs and configure servers all via Python, but it’s not limited to just those tasks.

Thus, one common question arises in minds of most people, especially newbies: why Python is popular in mainstream?

More Productive

First and foremost reason why Python is much popular because it is highly productive as compared to other programming languages like C++ and Java. It is much more concise and expressive and requires less time, effort, and lines of code to perform the same operations.

Because of its features, a large number of programmers across the world are showing interest in making use of this language to develop websites, GUI applications, mobile applications and more. The main reason that brings Python among the top coding languages is that it allows developers to figure out the concepts by developing readable and less code for developing complex and large applications.

Python is easy to learn and to work with

Since its creation by Guido van Rossum, Python has been specifically designed to be a general-purpose language. Simplified programming syntax with an emphasis on natural language, English-like commands, and code readability make coding in Python easier and more efficient.

Such Python features as one-liners and dynamic type system allow developers to write fewer lines of code for tasks that require more lines of code in other languages. This makes Python very easy-to-learn not only for seasoned programmers but also for newcomers and beginners. For instance, Python programs are slower than Java but they also take very less time to develop, as Python codes are 3 to 5 times shorter than Java codes. Moreover, Python is an interpreted language, which means that you can quickly experiment with changes to the code base.

Shorter development process

There were times when computer’s run time was the main challenge and the most expensive resource. But now things have changed. Computer, servers and other hardware have become much cheaper than ever and speed has become a less important factor. Today development time matters more in most cases rather than execution speed in terms of cost as employee’s time has become one of the most expensive resource. Reducing the time needed for each project saves companies lots of money.

But shorter development process doesn’t only save money, but also improves your competitiveness. Faster prototype and deliver enable companies to innovate and be ahead of the competition.That’s where Python gains its popularity as the time required to build a program using Python is very short compared to other programming languages.

Boost in AI, machine learning, and data science

Being the second most popular tool for analytics and data science (second only to R), Python powers countless data processing workloads in organizations around the world. Meanwhile, Python libraries such as OpenCV for computer vision and TensorFlow for neural networks are used in thousands of machine learning projects every day. ML requires continuous data processing, and Python’s libraries let you access, handle and transform data.

Moreover, Python for machine learning development can run on any platform including Windows, MacOS, Linux, Unix etc. To transfer the process from one platform to another, developers need to implement only several small-scale changes and modify some lines of code to create an executable form of code for the chosen platform.

Supportive community

Since Python has been around for three decades, there’s been plenty of time for a mature, supportive community to spring up around the language. From official documentation to YouTube tutorials, Python learners of all ages and skill levels can find the support they need to improve their knowledge of the language. A lot of Python documentation is available online as well as in Python communities and forums, where programmers and ML developers discuss errors, solve problems, and help each other out.

Python is also a popular language in academia, where it’s used to introduce students to computer science as much as it’s used for in-depth research projects.

A great library ecosystem

As a result of its strong community and corporate sponsorship, Python benefits from hundreds of different libraries and frameworks from NumPy and SciPy for scientific computing to Django for web development. These add-ons to the basic Python language can greatly enhance programs’ efficiency and cut down on the development cycle.

There are even a few libraries with a more specific focus, like scikit-learn for machine learning applications and nltk for natural language processing. In other words, there are library-like tools that offer cross-platform support, which is a huge benefit.

Some of the most popular Python libraries and frameworks are:

  • Django for server-side web development;
  • NumPy for scientific computing;
  • BeautifulSoup for XML and HTML parsing;
  • SciPy for mathematics, science, and engineering applications;
  • matplotlib for plotting graphs and charts.

To sum up, Python has every right to be the fastest-growing major programming language nowadays. However, we are not looking for a war between computer programming languages, or trying to impose the idea that Python’s the ace of aces. We rather believe that trends might be changing, and there’s always room for discussion. The number of users of a language doesn’t imply anything about its quality, and certainly can’t tell you which language is more appropriate for a particular project.