R is an interactive, open-source platform for statistical analysis. Although it’s not truly a programming language at all, it does offer one to aid with analysis.
R is a “integrated software suite facilities for data manipulation, calculation, and graphical display which contains a vast, coherent, integrated collection of intermediate tools for data analysis,” according to the R project’s website. R was a pioneer in data science and has long been a mainstay in academia, despite not being the first tool of its kind.
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Python, on the other hand, is described on the project’s website as a “interpreted, object-oriented, high-level programming language with dynamic semantics.” But this truly doesn’t do it credit. Given that it has a long history as a teaching language, Python is a general-purpose, simple-to-learn language that is frequently the first one a developer learns.
Children use it, and non-programmers can learn it in a weekend, according to Peter Wang, CEO of Anaconda. This has been a fundamental component of the architecture from the very start and was quite intentional, not accidental.
Python has always been excellent as a glue language, which is a near consequence. In that logic, it makes a lot of sense for businesses to invest in Python as a method of investing in their current code, as RedMonk analyst Rachel Stephens has emphasised. In other words, Python enables businesses to incorporate legacy code with their more contemporary data science objectives.
The main advantage of Python for data science may stand out in this situation because it is well-known.
According to Van Lindberg, general counsel for the Python Software Foundation, “Python is the second best language for anything.” Python is the second-best programming language for statistics after R. and the second-best for (insert use case here), ML, web services, shell tools, etc.
The direction of Lindberg’s statement, “If you want to do more than simply stats, then Python’s breadth is an overwhelming win,” is right, even though he may be understating Python’s superiority in some areas. It’s obvious that Python is not always second best.
To put it another way, Python is effective enough that programmers and other users favour using it for a variety of use scenarios. Although Python is a general-purpose programming language like Java, it’s far simpler to learn and use than Java. As a result, it is employed for a variety of purposes, resulting in, in Wang’s words, “explosive expansion.” Therefore, it should come as no surprise that, as Terence Shin has shown, if we examine the relative growth and drop between Python and R in data scientist job postings from 2019 through 2021, as Terence Shin has, then it’s clear that Python is gaining at R’s expense.
