Python vs R: What is the difference between Python and R languages

Python vs R

In this blog, we have discovered some of the major differences in Python vs R languages. Both Python and R languages are good and popular choices. There are some factors that may change your decision one way or the other.

Python vs R

Both Python and R are open-source programming languages with a broad community. New tools or libraries are continuously added to their particular catalog. R is essentially used for statistical analysis on the other hand Python gives an extra general approach to data science. In terms of programming language Python and R are states of the art-oriented towards data science. Learning both languages is, of course, the best solution. Both Python and R require more time to learn, which is not available for everyone.

What is Python?

Both Python and R do pretty much the same task: engineering, wrangling, app and many more. Python is a tool to use and execute machine learning at a high-scale. As compared to the R language, Python code maintenance is easy and more robust. Before Python did not have too many machine learning and data analysis libraries. Recently, For Artificial Intelligence or machine learning, Python is providing cutting-edge API.

Most tasks of data science can be accomplished with five Python libraries: Scipy, Numpy, Scikit-learn, seaborn and Pandas. Python makes accessibility and replicability easier than R. In fact, if you need to use your analysis result in a website or application, Python is the most suitable choice.

What is the R language?

R is developed by statisticians and academics over two decades. However, this language is a statistical language. The main use of R is data analysis and developing statistical software. Since then the study of data and data mining has become popular, R became popular. 

R also provides a broad variety of libraries along with statistical techniques for graphical techniques. It can create static graphs that can be used for publication-quality graphs. The availability of interactive and Dynamic graphs are also there. It is a command-line language but various interfaces give interactive GUI to reduce tasks of developers.

Comparison of Python vs R

Codes of Python are easy to maintain.Codes of R require more maintenance.
For deployment and development python is used as a general-purpose language.R is a statistical language and also can be used graphical techniques.
Python libraries’ learning can be a bit complicated.R is simple to start with. It has more simplistic plots and libraries.
Python is faster.As compared to Python, R is slower but not that much.
For deep learning Python is better.For data visualization, R is better used.
According to Python “there should be one and only one obvious way to do it”. Hence it has some main packages to achieve the job.R has several ways to achieve the same job. It has various packages for one job.
Statistical packages of Python are less powerful.For data analysis R is developed, hence it has more important statistical packages.
Python is fit for creating something new from scratch. It can be used for application development also.R makes it simple to use complex statistical tests and mathematical calculations.
Python is a multi-paradigm language that means python helps various paradigms like structured, object-oriented, aspect-oriented programming and functional.R helps only procedural programming for some object-oriented programming and functions for other functions.
As compared to R, Python is more popular.R is not that popular but still, it has so many users.

Key Difference between Python vs R

Performance and speed:

Although both languages are used for large data analysis if you compare performance-wise then python is better than R for making critical yet fast applications. R is a little slower than Python but still, it can handle large data operations.

Visualization and Graphics:

One can understand data easily if it can be visualized. For graphical interpretation of data R provides different packages. For visualization python also has libraries but it can be a little complicated then R.

Deep Learning:

According to the rising popularity of machine learning and data science both Python vs R gain their popularity. While python gives many finely tuned libraries R has the KerasR interface of python’s learning. For deep learning, both languages have a good collection of packages.

The correctness of Statistics:

Also, for data statistics, R is developed that’s why for statistics R will provide better libraries and support. When it comes to application deployment and development at that time Python is best. But for data analysis R implements large R and its libraries implement a large variety of graphical and statistical techniques. 

Unstructured Data:

Data produced by social media is often unstructured. Python gives PyPI, scikit-image, NLTK unstructured data. For unstructured data analysis R also offers libraries but not as good as Python. Still, both Python vs R languages can be used for the analysis of unstructured data.

Community Support:

When we compare community support for Python vs R languages its actually excellent. Both Python and R have StackOverflow groups, codes, a user mailing list, and user-contributed documents. Both Python and R don’t have customer support. which means users have developer’s documents and online communities for help.


Both Python vs R languages have their advantages and disadvantages, it is difficult to find which one is better. Python looks to be a bit more successful amongst data scientists, but that does not make R language complete failure. R is made for analysis of statistics and it is very great at that.

On the other hand, Python is a general-purpose language for the development of applications. Both Python and R languages give an extensive range of packages and libraries, the cross-library guide is also available in a few cases. Hence it completely depends on the requirement of the user which one to choose.

As a result, if you want to get python assignment help or any programming assignment help within a given deadline, Our experts are available for your help.

Comments are closed.