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With the massive growth in the importance of Big Data, Machine Learning and Data Science in the software industry or software service companies, two languages have emerged as the most favorable ones for the developers. The comparison on R vs Python will provide you with a crisp knowledge about the two most popular and favorite languages for the data scientists and data analysts. This R vs Python blog will provide you with a complete insight into the languages in the following sequence:
R is considered to be the best programming language for any statistician as it possesses an extensive catalog of statistical and graphical methods. Python on the other hand, can do pretty much the same work as R but it is preferred by the data scientists or data analysts because of its simplicity and high performance. R is a powerful scripting language and highly flexible with a vibrant community and resource bank whereas Python is a widely used, object oriented language which is easy to learn and debug.
So let’s move ahead with the comparison on R vs Python and have a look at the comparison factors.
R was introduced for data analytics whereas Python was developed as a general purpose language. The former is mostly preferred for hoc analysis and exploring datasets whereas the latter one is suitable for data manipulation and repeated tasks.
Let’s look at the factors we will be using for the comparison on R vs Python:
|Ease of Learning|
|Data Handling Capabilities|
|Graphics & Visualization|
Python emphasizes on productivity and code readability which makes it one of the simplest programming languages. It is preferable due to its ease of learning and understandability.
R is a low-level programming language due to which it requires longer codes for simple procedures. This is one reason for the reduced speed.
Python is a high-level programming language and it has been the choice for building critical yet fast applications.
R is convenient for analysis due to the huge number of packages, readily usable tests and the advantage of using formulas. But it can also be used for basic data analysis without the installation of any package.
Visualized data is understood efficiently and more effectively than raw values. R consists of numerous packages that provide advanced graphical capabilities.
It is easy to use complex formulas in R and also the statistical tests and models are readily available and easily used.
Now if we look at the popularity of both the languages, they started from the same level a decade ago but Python witnessed a huge growth in popularity and was ranked first in 2016 as compared to R that ranked 6th in the list.
Python users are more loyal to their language when compared to the users of the latter as the percentage of switching from R to Python is twice as large as Python to R.
The software companies have been more inclined towards technologies such as Machine learning, Artificial Intelligence and Big data which explains the growth in the demand for Python developers. Although, both the languages can be used for statistics and analysis, Python has a slight edge over the other due to its simplicity and ranks higher on the job trends.
Commercial softwares usually offer paid customer support but R and Python do not have customer service support which means you are on your own if you face any trouble. Both the languages have online communities for help though. Python has a greater community support as compared to R.
Now with this we have come to the end of the comparison on R vs Python. Both the languages give a head to head fight in the world of data science and data analytics. But Python emerge as the winner out of the two because of its immense popularity and simplicity in writing codes.
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Python Certification Training will help you gain expertise in Quantitative Analysis, data mining, and the presentation of data to see beyond the numbers by transforming your career into Data Scientist role.
Data Analytics with R training will help you gain expertise in R Programming, Data Manipulation, Exploratory Data Analysis, Data Visualization, Data Mining, Regression, Sentiment Analysis and using R Studio for real life case studies on Retail, Social Media.