Python vs R

In the event that you are into Data Science, the two programming languages that may promptly strike a chord are R and Python. Both Python and R are mainstream programming languages for Statistics. R and Python, are magnificent tools in their own privilege however are all the time considered as opponents. While R’s usefulness is created in view of statisticians, Python is frequently commended for its straightforward and easy-to-understand syntax.

In this blog, we will feature a portion of the contrasts among R and Python, and how the two of them have a spot in the data science and statistics world.


Python was created by Guido Van Rossem in 1991 and has been extremely popular and is widely used in data processing. Some of the reasons for its extreme popularity are:

  • General-purpose scripting language
  • Has a lot of extensions and incredible community support
  • Simple and easy to understand and learn, not as many libraries as R
  • Packages like pandas, numpy and scikit-learn, make Python an excellent choice for machine learning


Ross Ihaka and Robert Gentleman created the open-source language R in 1995 and since then it has gone on to become one of the most used tools for statistics and data science in the industry.

  • Consists of packages for almost any statistical application one can think of
  • CRAN currently hosts more than 10k packages
  • Comes equipped with excellent visualization libraries like ggplot2, plotly, and esquisse
  • Dependencies between libraries

Python has compelling libraries for data science, machine learning, and Artificial Intelligence. You can think of Python as an unbeatable player in AI.

Python is the best tool for Machine Learning integration and deployment but not for business analytics. On the other hand, R is more suitable for business analytics, if you need to write a report and create a dashboard.

The uplifting news is R is developed by academicians and scientists. It is intended to answer statistical and machine learning problems. R is the correct tool for data science in light of its incredible libraries. Moreover, R is equipped with numerous packages to perform time series analysis and data mining.

From one perspective, Python incorporates extraordinary libraries to code the algorithms. As a learner, it might be easier to learn how to build a model from scratch and then switch to the functions from the machine learning libraries. Then again, you already know the algorithm or want to go into the data analysis right away, then both R and Python are okay, to begin with. One advantage for R in case you’re going to focus on statistical methods. Secondly, if you want to do more than statistics, let’s say deployment and reproducibility, Python is a better choice.

In a nutshell, the statistical gap between R and Python are getting closer. Most of the jobs can be done by both languages. You would be advised to pick the one that suits your needs yet, in addition, the tool your associates are utilizing. It is better when every one of you communicates in a similar language. After you know your first programming language, learning the subsequent one is less complex.

Hope you find it useful.

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