Python is simple, easy to use, versatile and has numerous libraries which allow developers to write complex codes with the least number of lines.
In terms of the languages used, the field of data sciences has seen fierce competitions. As the choices of programming languages multiplied with the overall boom in the data sciences, so did the tussle between them to dominate the market in terms of popularity.
In the initial days, after Artificial Intelligence was founded by John McCarthy in 1958, languages like Lisp and Prolog were commonly used. Languages like Java and C++ have also had their share of popularity in the field of data sciences and, to a great extent, they still do. In the recent past, R was to the most commonly used language for a long period of time.
Over the last few years, the popularity of Python has steadily grown and it has now replaced R, at least, in terms of popularity. Although mostly used in the financial sector, all other kinds of data science projects can be efficiently handled using the Python framework.
In this article, let us explore some of the major factors which have led to the boom in Python's popularity over the years.
Minimalism is the trend of our times, wherein less is always more. In fact, one of the fundamental principles which motivated the founders of the Python language is that “simple is better than complex”.Thus, it goes without saying that Python is indeed the simplest programming language made till date. So much so, that it can boast of enabling developers to make programs with the least possible lines of coding.
Primarily, this is made possible by Python’s identification and association capacities which automatically groups similar data types. Moreover, it also allows for well-structured, easy-to-read codes by making use of indentations.
Being simple as it is, Python allows developers to write complex codes in much lesser time than while using languages like Java and so on.
The fact that Python is simple, doesn’t entail the fact that it isn’t versatile. Rather, the opposite is very much the case. In its capacity of a general-purpose language, Python is, by far, the most versatile and universal programming language at present.
Apart from the core syntactical framework, Python allows developers to use a number of potent libraries which greatly enhance its scope. Consequently, Python can be used to develop programs of all kinds, ranging from simple web applications to the more complex machine learning models.
Python also comes with a range of potent by free tools, such as TensorFlow, PyTorch and so on, which are designed to enable its users to create deep learning environments with relative ease and minimal coding.
The finance sector’s obsession with Python is not without reasons, at all. With access to platforms like PySpark or Hadoop, Python allows its users to seamlessly handle large volumes of data.
In case using Spark is not feasible, users may also make use of the MPI binding, which is designed to handle distributed processing with great efficiency.
Support communities play a very crucial role in the success of any programming language. With its steadily expanding and responsive community, Python has made an indelible mark in this respect, as well.
With platforms like PyPi, users can easily grasp the magnitude and the variety of programs being developed using Python. This not only helps developers to gather inspirations for their own projects but also acts as a boon at times when she is faced with any problem.
Often, it has been argued by many that Python isn’t a good choice when it comes to the speed (speed of functioning, not of writing the codes). Well, to a great extent, the allegations are indeed true. Python isn’t one of the fastest programming languages and, in this respect, it lags behind its predecessors like C++ and Java.
However, this doesn’t mean the end of the world for this emerging leader of programming languages. Python’s speed issues can be substantially mitigated by using its superset, Cython. This is a perfect blend of Python-style coding with C-style performance.
To conclude, it can well be said that, at present, Python is virtually unmatched by any other programming language in the market. It is not only simple to use and easy to read but, it also has to ability to develop a range of programs starting from simple to complex. The ability to use additional libraries significantly expands the scope further. The problems of being relatively slow are also overcome by the use of platforms like Cython. In all, Python is transforming the field of machine learning by enabling users to develop complex networks with marvellous ease and flexibility.
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