This is the Most Trending debut of Aspiring data scientists
Here we will try to understand the differences between both programming Language
In the battle of data science tools, python and R both have their pros and cons. Selecting one over the other will depend on the use-cases, the cost of learning, and other common tools required. And I am sure that you will not have any confusion left after completing this post .
Here are strengths and weaknesses that come into play:
In many ways, the two open source languages are very similar. Free to download for everyone, both languages are well suited for data science tasks — from data manipulation and automation to business analysis and big data exploration. The main difference is that Python is a general-purpose programming language, while R has its roots in statistical analysis. Increasingly, the question isn’t which to choose, but how to make the best use of both programming languages for your specific use cases.
What is R?
R is a programming language that is specifically for statistical computing and graphical computation. R project is an extension of the S language that was developed at Bell Laboratories by John Chambers and his colleagues. It is a language and environment for statistical computing and graphics.
Data Analysts, Data Scientists, Statisticians make use of R programming language to analyze the data and carry out statistical analysis through graphs and other types of visualizations. R allows the users to perform analysis on large datasets. This language is continuously growing with thousands of packages that can be used readily for many applications.
There are various statistical and graphical techniques that R provides such as linear and non-linear modeling, statistical testing, time-series analysis, classification, regression, clustering and many more.
The main strength of R is its well-publicized quality plots that we can produce using its many functionalities and packages. The user also retains full control over the design choices in their graphics.
To learn more about R programming language, check my previous posts and for Installation of R , R tools and R studio, please find the below URL.
What is Python?
Python is an interpreter based, high level and an object-oriented programming language that consists of dynamic semantics. Python is built-in high-level data structures that we can combine with dynamic typing as well as dynamic binding to make it very attractive for Rapid Application Development. We can also avail it for scripting or an adhesive language to combine several components into one framework.
Python is simple, easy to learn and also provides emphasis on readability that significantly reduces the overall cost of program maintenance. Python has an easy learning curve and advocates for program modularity as well as code reusability. The Python interpreter, as well as the extensive standard library, are present in the binary form that do not charge for major platforms and provides free distribution.
Programmers who are new to programming often fall in love with Python due to its vast functionalities and ease of learning. Since Python has no compilation step, the provision of the edit-test-debug cycle is significantly fast. In Python, the process of debugging programs is incredibly easy. Unlike other languages like C and C++, an inappropriate input will never lead to a segmentation fault. When an interpreter finds an error, it raises an exception to the user right after we enter the input. If however, the program does catch the exception, the interpreter provides a stack trace. All of these features added with the massive libraries for a variety of purposes make Python an efficient programming language. Using a source-level debugger one can easily inspect local as well as global variables.
The below are Key differences :
Hope this post clarifies the main differences between R & Python..
Let’s Start Programming Python in the next Post…