Ever since Python was released in the early 1990s, it has generated a lot of hype. Sure, it took the programming community at least 2 decades to appreciate its existence, but since then, it has far surpassed C, C#, Java and even Javascript in popularity.

Although Python dominates the fields of Data Science and Machine Learning, and, to some extent, Scientific and Mathematical computing, it does have its share of disadvantages when compared to newer languages like Julia, Swift and Java.

What made Python so popular?

One of the main driving points behind Python’s meteoric growth was how easy it was to learn and how powerful it was to use, making it extremely appealing to beginners and even those who shied away from programming because of the hard, unfamiliar syntax of languages like C/C++.

The language, at its very core, emphasised extensively on _code readability. _With its concise and expressive syntax, it allowed developers to express ideas and concepts without writing tons of lines code (as would be the case in lower-level languages like C or Java). Its simplicity a given, Python seamlessly integrates with other programming languages (like offloading CPU-intensive tasks to C/C++), making it an added bonus to polyglot developers.

Yet another reason for Python’s versatility is its heavy usage by enterprises (FAANG included) as well as countless smaller ventures. Today, you’ll find a Python package for pretty much anything you can think of — for scientific computing, you’ve got Numpy, Sklearn for Machine Learning and Caer for Computer Vision.

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