10 Reasons Why Python Is So Popular

6 min readDec 14, 2020

There is no question to the authenticity of Python as a programming language in the year 2021. The language has grown incredibly fast and was speculated to be a superior programming language by 2020 way back in 2017, even despite it’s low runtime latencies compared to compiled languages like C++ . A prophecy come true. The technical innovation and human like syntax behind the language that allows developer to write logical and maintainable code in significantly less lines makes it to be considered one of the top programming languages. But what exactly makes Python a popular programming language? Without much hassle, here is what to know about Python programming if you ever second guessed its legitimacy as one of the best.

Python has impressive corporate sponsors

Ideally, the sponsors of an initiative or program are subtle heralds of the same product they represent. So, While C# has Microsoft, and PHP has Facebook, Python has Google in its corner which adopted the language in 2006 and commonly use Python for much of their internal tooling, software automation, and even entire backend servers using it.

In addition to Google’s heavy use of Python there are other huge website using the Python Django framework such as Spotify, Pinterest, Instagram, DropBox, and Google’s own YouTube.

Ease of Use

Python continues to impress its new users and entry-level programmers with ease of functions. The language is rated as one of the most accessible programming languages because it has a simplified syntax, which is rarely complicated, and emphasizes natural language. Following the ease of learning and usage, python codes are simply written and have quick execution time when compared to other interpreted languages. It also gives an edge of experimentation via the simplicity of refactoring a Python code base. This may be due to its developer’s general-purpose language design where code leads to self documenting itself through intuitive and elegant logic elements.

Many people can even learn the core Python principles within a week or two of studying the language through free YouTube tutorial playlists such as our own intro video series. Then after learning Python well enough many people can even start making money from home with their new skills quicker than learning programming concepts in more challenging languages.

Versatility

Multipurpose programming languages are quite more popular than limited-applicable ones. Python is bound to be popular with multiple applications across different fields. Some of the fields include Data science, web development, systems automation and administration, mapping and geography, mathematical computing, finance and trading, game development, and application scripting.

Well-Paying Career Options

It’s no secret that careers in technology are some of the most widely available, fastest growing, and lucrative careers out there. New technological advances such as AI, Machine learning, and data science are creating exciting new career opportunities for people that are willing to put in the hard work to learn the skills required for these careers.

Although some people fear being put out of work through technology automating manual labor; this process will create higher paying and safer jobs for people to create and maintain the systems that will pack shipments, cook food in automated restaurants, and in the future potentially replace taxi drivers through autonomous driving cars such as Tesla just to give a few examples.

Impressive Python Libraries

With its excellent libraries that are able to boost development, Python follows core programming principles of “don’t repeat yourself” by exposing many rich features that developers can reuse in their own programs. It has a host of programs and platforms that users can browse through using the “PIP” dependency management tool.

While many people are grateful that Python is a very simple language to understand what they should actually be grateful for is the extensive libraries that are being created as a result of Python being simple to learn and work with.

Some of my favorite industry standard libraries are listed below:

SciPy — engineering applications
Scikit — Machine Learning applications
Numpy — Powerful matrix library
Pandas — Dataframe library
Beautiful Soup — HTML parsing and web scraping
Django — Web development framework
Flask — Web application micro framework. Check out our beginner tutorial here.
SQL Alchemy — Library for creating Domain Object Models that can interact with relational databases such as MySQL and Postgres
Tensor Flow — Used for making production quality machine learning applications

Efficiency

We don’t just take the words of program developers for it. The best way to be sure would be to find out what developers say. From different platforms such as Facebook groups, or LinkedIn circus of professionals, Quora, and Reddit, most developers have an agreement that Python is efficient, fast, and reliable albeit not as fast as compiled languages. It is appealing to most beginners, too. Python’s speed and efficiency, coupled with its versatility, make it a preferred and suggested choice among developers, therefore increasing its popularity.

But even more efficient than the speed of Python’s program execution is the speed of development. With Python you can write simple code in a few lines that would take 10 times as many lines in other languages with complicated pointers, iterators, and memory management that Python abstracts to improve its simplicity.

Python is Flexible

Flexibility is a core principle that developers opt for in a programming language. With zero restrictions and dynamic variable typing, there is plenty of room for users to maneuver the language in a way that works best for them while developing any application. This freedom is not guaranteed with other languages. So, if for nothing else, Python is known for this feature.

This can also lead to bad coding practices if used too carelessly by newer developers that don’t take care to structure their code well as an application’s code base grows larger.

Big Data, Machine Learning, and Cloud Computing

These three aspects of computer programming are the hottest trends at the moment. That is because they would be getting to use more trusting, reliable, and efficient computing methodologies to improve their operations. Python is the second-most-popular tool, following R language when it comes to data science and analytics.

However, most of the workloads in different organizations are powered only by Python. The research and development processes in some organizations are also powered by python language due to the ease of analysis and usable data organization. Libraries that support such operations include Boto3 for interacting with AWS cloud infrastructure, OpenCV for computer vision, and TensorFlow for neural network training in machine learning applications.

Academic usage of Python

Owing to its simplified language and ease of use, many teachers prefer to teach their students with this being their core language in schools and colleges. That also hinges mainly on its versatile applications in AI, ML, Data Science, etc. Python is also used to teach students in introduction to computer science classes in high schools across the world.

Software Automation

There is no denying that with the going of the tech world, automation is a big deal and to some an ice breaker into coding thanks to great books like “ Automating the boring stuff with Python “. Programming languages make software running more effective and efficient to run. It runs automation by using simple software frameworks and tasks, all of which can be done without being overly-dependent on humans. Python’s simplicity allows developers improved ability to create automation code, thus, making it popular for that purpose and is a part of most developers toolkits.

Conclusion

Python is an awesome programming language that is vast in multiple computing operations. The popularity is because of its efficiency, reliability, technicality, and of course, its economic benefit to the pockets of those who work with them.

Originally published at http://beapython.dev on December 14, 2020.