As data science becomes more and more applicable across every industrial sector, the collection of raw data from different sources and ways to analyze the data and convert it into useful information is the need of the hour. The field of data sciences has clearly helped businesses with growth by helping them make the best and extremely well-informed decisions. Data scientists use many kinds of languages to arrive at a conclusion about the data and the most preferred computer languages amongst them being “Python”.
Python is a general-purpose programming language like many other languages, which include R, Scala, Java, or C++. Additionally, it’s an interpreted language with a very simple syntax. It’s a swift, but a dominant tool with numerous capabilities. It’s also the undisputed king of deep learning. Guido van Rossum created python in 1991; it derived its name from the British comedy group called Monty Python.
Reasons that make Python a data science powerhouse include:
User friendly: It is a popular language for making programs work with the least lines of code possible. It has the capability to automatically identify and associates data types and follows an indentation based nesting structure. Thus making the language easy to use and requiring less time and effort to code.
Easy Readability: Python’s code is highly readable; some programmers even say that it looks almost like the English language. It even helps the programmer to revisit their code and fix any bugs or add on a new feature even at a later stage. To add to this, even others can do the same with ease.
High level of integration: It is very good at extracting knowledge from other tools and domains and putting them into your own space. It is seldom referred to as “glue language” because of its easiness to the integration with other components. It is thus an obvious preference of the programmers as it is a perfect fit for many of the newest trends in software development, which favor modular programming.
The extensive number of libraries: It has a lot to offer for all types of users as an increasing number of people are adopting this programming language for their work as it provides plenty of libraries that can be used for plotting and visualizations. To name a few, Python has libraries like pandas, numpy, scipy, and scikit-learn, which can come in handy for doing data science-related work. Its most popular libraries are seborn and matplotlib. Other general-purpose programming languages do not provide the same scientific libraries and community support as Python.
Universal: Since it is a general-purpose programming language that comes with a solid ecosystem; it provides the developers with the opportunity to build their own machine learning models, web applications, and anything else that they in one language. It is a popular choice for app development among enterprises that may already use a variety of different systems, technologies, and existing databases.
Scalability: Python has established a lead by emerging as a scalable language and it is much faster than other languages compared to in its domain.
There are many more points contributing to the phrase that Python is the most sought after language for data science. Python has always been the first choice for data scientists because of its features and extensive libraries. A study revealed that scripting languages like Python are more productive than conventional languages, such as C and Java, for programming problems. Not only for data science projects but Python has received acknowledgment in the field of finance. Giants like Bank of America use it as a tool for crunching financial data. People want to use python because of its intuitiveness, beauty, philosophy, and readability. Python has a huge space of capability. Many mammoths like Google, Yahoo, Wikipedia, Instagram, Spotify, NASA, Facebook, and Amazon, etc. all use Python. Python has found its application even in Artificial Intelligence projects, which is the future in the data science field. Python endeavors to provide a simpler, less-cluttered syntax and grammar, hence giving the developers a choice in their coding methodology.