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

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 your particular catalog. R is essentially used for statistical analysis, on the other hand, Python offers an additional general approach to data science. In terms of programming language, Python and R are cutting-edge states oriented to data science. Learning both languages is, of course, the best solution. Both Python and R require more time to learn, which is not available to everyone.

What is python?

Both Python and R do more or less the same task: engineering, disputes, applications and many more. Python is a tool for using and running machine learning on a large scale. Compared to the R language, maintaining Python code is easy and more robust. Python didn't have too many machine learning and data analytics libraries before. Recently, for Artificial Intelligence or Machine Learning, Python provides state-of-the-art APIs.

Most data science tasks 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 analytics result in a website or application, Python is the most appropriate option.

What is the R language?

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

R also provides a wide variety of libraries along with statistical techniques for graphical techniques. You can create static charts that can be used for publish-quality charts. The availability of interactive and dynamic charts is also there. It is a command-line language, but several interfaces provide an interactive GUI to reduce developers' tasks.

Comparison of Python vs R

Python	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 when comparing performance, python is better than R for making applications critical but fast. R is a little slower than Python but still can handle large data operations.

Visualization and graphics:

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

Deep learning:

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

The accuracy of statistics:

In addition, for data statistics, R is developed, so for R statistics it will provide better libraries and support. When it comes to deploying and developing applications at that time, Python is the best. But for data analysis, R implements large R and its libraries implement a variety of graphical techniques and statistics.

Unstructured data:

The data produced by social networks 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 unstructured data analysis.

Community support:

When we compare community support for Python vs R languages, it's actually excellent. Both Python and R have stack overflow groups, codes, a user mailing list, and user-contributed documents. Python and R have no customer support. which means users have developer documents and online communities to get help.

Conclusion

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.

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