R VS Python

Data Science, AI and ML
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Comparison between R and Python

Data science deals with identifying, extracting, and representing meaningful information from the data source. R, Python, SAS, SQL, Tableau, MATLAB, etc. are the most useful tools for data science. R and Python are the most used ones. But still, it becomes confusing to choose the better or the most suitable one among the two, R and Python.

Comparison Index R Python
Overview “R is an interpreted computer programming language which was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand .” The R Development Core Team currently develops R. R is also a software environment which is used to analyze statistical information, graphical representation, reporting, and data modeling. Python is an Interpreted high-level programming language used for general-purpose programming. Guido Van Rossum created it, and it was first released in 1991. Python has a very simple and clean code syntax. It emphasizes the code readability and debugging is also simple and easier in Python.
Specialties for data science R packages have advanced techniques which are very useful for statistical work. The CRAN text view is provided by many useful R packages. These packages cover everything from Psychometrics to Genetics to Finance. For finding outliers in a data set both R and Python are equally good. But for developing a web service to allow peoples to upload datasets and find outliers, Python is better.
Functionalities For data analysis, R has inbuilt functionalities Most of the data analysis functionalities are not inbuilt. They are available through packages like Numpy and Pandas
Key domains of application Data visualization is a key aspect of analysis. R packages such as ggplot2, ggvis, lattice, etc. make data visualization easier. Python is better for deep learning because Python packages such as Caffe, Keras, OpenNN, etc. allows the development of the deep neural network in a very simple way.
Availability of packages There are hundreds of packages and ways to accomplish needful data science tasks. Python has few main packages such as viz, Sccikit learn, and Pandas for data analysis of machine learning, respectively.
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R is better if you’re mostly working with data, Python is better if you need to work with data on top of general purpose programming.

Dataframes and vectors are native data structures in R and are thus supported everywhere, unlike in python where only a few modules support a pandas data frame or numpy vectorization.

Both are just wrappers for C code so what you can do in one you can do in another, but broadly speaking R is far better for stats and data, Python is far better for general computing (especially on unix-like OSs)