Python is a language that most programmers these days have at least a basic understanding of. First developed in the late 1980’s and released in 1991 by Dutch Computer Scientist Guido Van Rossum, the impact of Python on programming has been profound. Yet why has it experienced this success?
 
Some programming languages are designed and optimised with a specific purpose in mind. Swift, for example, is designed for iOS app development, while Kotlin is designed for Android app development and JavaScript is designed and optimised for the Web. Python, on the other hand, is extremely versatile and is used for a wide range of difficult tasks. Python is widely used in many fields, including machine learning, quantum computing, ethical hacking, data science, and many others.
 
Python is very similar to the English language. As you become more acquainted with programming concepts, you should concentrate on the tasks you want to complete rather than trying to match the number of opening and closing parenthesis. Python will also not judge if you don't end your line of code with semicolons, for example, something that is prevalent in languages such as C. When dealing with logic problems in your code, Python is better as it offers simplicity, allowing you not to have to deal with complicated syntax errors that come with the “highly optimised” tools of other languages.
 
But Python's simplicity comes at the expense of speed and performance. There is an unavoidable trade-off for performance when optimising a programming language for simplicity. Python is slower to execute because rather than being executed directly by the CPU, it gets executed by a Virtual Machine (an interpreter), which is in turn emulated on the CPU. This level of abstraction makes the execution slow. But as slower runtimes are only a problem on massive scales, which Python isn’t designed to compute, this compromise is ultimately beneficial.
 
But one of Python’s biggest strengths is the size of its community, meaning that problems faced by novice coders can be easily solved with a quick Google search. This is known as the "network effect." More people are likely to join a community where there is a large network of people working together and helping each other. Consider thousands of software engineers working with Python at the same time you are. Any problems you might have faced have probably been faced before by other programmers, who in turn have posted their solutions online. And in many cases, you will be able to solve at least a portion of your project using existing tools and libraries.
 
And it is these libraries where Python shines – there are few languages that can match Python in terms of ease of accessibility to libraries. The utilisation libraries like NumPy help combat runtime slowness with greater efficiency in mathematics. Libraries like TensorFlow and PyTorch have helped propel python above competing languages for advancements in machine learning. TensorFlow is a testament in fact to Python’s dominance in the field, as it was developed by Google and so is constantly updated and has a very large support base.
 
Therefore, Python’s success can be traced back to three things: its simplicity, size of its consumer base and the versatility provided by its many libraries. Its success is seen with the widespread use among corporations like Google, Facebook, and Spotify. Even some of the most technically advanced software is built with Python; for instance, Tesla’s autopilot is programmed using PyTorch. Python is also the most popular programming language according to the TIOBE index. With its foothold in so much of modern technology, it is unlikely to disappear any time soon.