Is Python preferred over R for Data Analysis?

Python serves as a general-purpose programming language that is used by developers to build desktop GUI applications, data analysis, web applications, and predictive modeling. On the other hand, R specifically deals with statistical computing and data analysis. Another feature is that both are open source programming languages allowing data analysts to use data analysis libraries as well as the frameworks. Let us analyze the feature and arrive at a conclusion.

Design Goal

Presently, Python is the widely used language and has syntax rules that facilitate developers in building applications having a readable codebase. On the other hand, R is not a general-purpose programming language and has a focus on statistical computing and data analysis.


In Python as well as in R there, are many packages for Data Analysts. Python, helps the data analysts to use Pandas for aggregating, manipulating, and visualizing relational data and Seaborn for the statistical models. Additionally, its advanced package has like TensorFlow, and Theano to optimize data analysis by adopting machine learning and deep learning. The R packages feature the functions and data. Programmers have the option to select from the contributed packages for data analysis like the caret, dplyr, ggplot, and lattice for effective data analysis.


Among the users, there is a comparison between Python Programming and R covering their speed and individual performances. Several studies suggest that Python is comparatively faster and also can be speeded up after using tools and algorithms. R was since initiation better for the statisticians and data analysts. Additionally, the quality of code has a direct impact on the performance of R programs. There are packages like Riposte, FastR, pqR, and renjin for a good speed in R programs.

Data Visualization

The data analysts seek robust data visualization tools so that the managers are in a position to detect trends, and patterns. Python Programming allows for the data visualization libraries like Altair Seaborn, Matplotlib, and Bokeh for the benefit of data analysts to facilitate huge volumes of data in a visual format that is easily comprehensible. The packages like googleVis, rCharts, gplot2, and ggvis make R a better option in comparison to Python.


Python is a good option when the need is for simplifying data analysis. It is extensively used for predictive and routine data analysis processes, analyzing the data from various sources and presenting the outcome through charts or maps. R is good for projects involving heavy statistics without writing additional code.

Learning process

Python has simple syntax rules enabling the programmers to express concepts without writing additional code. The programming language helps programmers to write codes that are clean, readable, and maintainable whereas R has a learning curve and the learner has to put in extra time and effort.


Data Analysts can easily accelerate data analysis by integrating Python and R code. Using any of the two basically depends on the peculiarities of the situation and the questions seeking the answer. By using python, getting any data is easy but in a few cases with R, it is hard.

Python is an ideal choice in data cleaning and R is good for building and testing the statistical models. The libraries like Scipy, Matplotlib, Numpy Seaborn pandas, Scikit-learn, are very useful and can be easily integrated with the web services making Python a popular and widely used language by data scientists. Thus on the comparative analysis, the conclusion is learning R and Python is an ideal solution. Through Python deploying and implementing machine learning is possible and easy. Moreover, its codes are robust and easy to keep up. Python is adapting many modern measures and offers advantages in AI and machine learning.

Market angle

As per the analysis of the job market conducted by experts about the popularity of data science software for 2019 by KDnuggets Python is in the lead with 27,374 jobs, followed by SQL with 25,877. Java and Amazon’s Machine Learning at 17,000 followed by R and the C variants are at, 13000. Thus python is way ahead in popularity.

Python and R experienced steady growth every year in the past twenty years. There is Python-R dominance in the domain of data science. Since 2013 Python has overtaken R as the most popular language in data science, based on the Stack Overflow developer survey. Python stays ahead owing to its versatility, easy use for data science, utility programming, and web development, besides being a general-purpose programming language. Presently it is facilitating full-stack development also.

Finally Python is the go to language for data pre-processing, data cleaning, data wrangling, its preparation, outlier detection and also for the missing data values management section.


Using any of the two basically depends on the peculiarities of the situation and the questions seeking the answer. Both are mutually complementary and offer relative advantages. Also in Python, there is accessibility and it is easier to reproduce the matter than R. But going with the trend, and assessing the features it is safe to say that Python is the answer and the better of the two.




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Shubhangi Singh

Shubhangi Singh

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