The left column shows the ranking in 2017 and the right column in 2016. The IEEE Spectrum ranking is a metrics that quantify the popularity of a programming language. R consists various packages and libraries like tidyverse, ggplot2, caret, zoo whereas Python consists packages and libraries like pandas, scipy, scikit-learn, TensorFlow, caret.R can be used on the R Studio IDE while Python can be used on Spyder and Ipython Notebook IDEs.Both R and Python can handle huge size of database.R is integrated to Run locally while Python is well-integrated with apps.R is difficult to learn at the beginning while Python is Linear and smooth to learn.R provides flexibility to use available libraries whereas Python provides flexibility to construct new models from scratch.R users mainly consists of Scholars and R&D professionals while Python users are mostly Programmers and Developers.The primary objective of R is Data analysis and Statistics whereas the primary objective of Python is Deployment and Production.
R is mainly used for statistical analysis while Python provides a more general approach to data science.In fact, if you need to use the results of your analysis in an application or website, Python is the best choice. Python, on the other hand, makes replicability and accessibility easier than R. Most of the data science job can be done with five Python libraries: Numpy, Pandas, Scipy, Scikit-learn and Seaborn. Recently, Python is catching up and provides cutting-edge API for machine learning or Artificial Intelligence. Years ago Python didn’t have many data analysis and machine learning libraries. Python codes are easier to maintain and more robust than R. Python is a tool to deploy and implement machine learning at a large-scale. Python can pretty much do the same tasks as R: data wrangling, engineering, feature selection web scrapping, app and so on. Communicating the findings with a presentation or a document is easy. R has fantastic tools to communicate the results. The cutting-edge difference between R and the other statistical products is the output. The rich variety of library makes R the first choice for statistical analysis, especially for specialized analytical work. It is possible to find a library for whatever the analysis you want to perform. There are around 12000 packages available in CRAN (open-source repository). R has now one of the richest ecosystems to perform data analysis. R, however, is built by statisticians and encompasses their specific language.Īcademics and statisticians have developed R over two decades. Python is a general-purpose language with a readable syntax. R and Python requires a time-investment, and such luxury is not available for everyone. Learning both of them is, of course, the ideal solution. R and Python are state of the art in terms of programming language oriented towards data science. R is mainly used for statistical analysis while Python provides a more general approach to data science. New libraries or tools are added continuously to their respective catalog. R and Python are both open-source programming languages with a large community.