Programming Languages: Python vs R


Guido van Rossum developed Python in 1991 as a multi-paradigm language.

Python is a high-level object-oriented programming language that is interpreted. It is a language that is dynamically typed. It supports multiple programming models, including object-oriented, imperative, functional, and procedural paradigms, and has an interface to many OS system calls.

R Programming Language:

R is a statistical programming language. It is used in the creation of statistical software and data analysis. R has grown in popularity as data mining and data analysis has grown in popularity. R provides a wide range of libraries for graphical techniques in addition to statistical techniques. It can generate static graphs, which are used to create graphs suitable for publication. There are also dynamic and interactive graphs available. For all of the packages that it supports, R has a package archive network (CRAN- Comprehensive R Archive Network). It has over 10,000 packages in it. R is a command-line language, but several interfaces offer interactive graphical user interfaces to help developers with their work.

The primary distinction between R and Python is in the goals of data analysis.

The main difference between the two languages is how they approach data science. Both open-source programming languages have large communities that are constantly expanding their libraries and tools. However, while R is primarily used for statistical analysis, Python offers a more general approach to data manipulation.

R vs Python

                                       R                             PYTHON
R codes require more upkeep/maintenance Python code is more robust and simpler to maintain.
The R programming language is primarily used by scholars and researchers.


Python is the programming language of choice for the majority of programmers and developers.
R is more suitable for data visualization. Python is preferable for deep learning.
R is primarily a statistical language, but it is also used for graphical techniques. Python is a general-purpose programming language used for development and deployment.
R has hundreds of packages or methods for performing the same task. It has several packages for a single task. Python was created with the idea that “there should be one, and preferably only one obvious way to do it.” As a result, it has a few main packages to complete the task.
R is a simple language to learn. It has less complicated libraries and plots. Learning Python libraries can be difficult.
For some functions, R only supports procedural programming, while for others, it supports object-oriented programming. Python is a multi-paradigm programming language. Python supports a variety of paradigms, including object-oriented, structured, functional, and aspect-oriented programming.
This is a command-line interpretive programming language. Python strives for simplification in its syntax. It is related to the English language.
R is slightly slower than Python, but only marginally. It is faster and more efficient
R makes it simple to perform complex mathematical calculations and statistical tests. Python is ideal for creating something new from the ground up. It is also used in application development.
Because R was created for data analysis, it includes more powerful statistical packages. The statistical packages in Python are less powerful.

Let’s take a look at some more significant differences.

1. Collection of Data:

Python supports a wide range of data formats, from comma-separated value (CSV) files to JSON retrieved from the web. SQL tables can also be directly imported into Python code. The Python requests library for web development makes it simple to retrieve data from the web and use it to build datasets. R, on the other hand, is intended for data analysts who want to import data from Excel, CSV, or text files.

R dataframes can be created from Minitab or SPSS files. While Python is more versatile for retrieving data from the web, modern R packages such as Rvest are designed for basic web scraping.

2. Exploration of Data:

Pandas, Python’s data analysis library, can be used to explore data in Python. In a matter of seconds, you can filter, sort, and display data. R, on the other hand, is designed for the statistical analysis of large datasets and provides a variety of data exploration options.

R allows you to create probability distributions, perform various statistical tests, and employ standard machine learning and data mining techniques.

3.Visualisation of Data:

Visualization is not one of Python’s strong suits, you can use the Matplotlib library to create basic graphs and charts. Furthermore, the Seaborn library enables you to create more appealing and informative statistical graphics in Python.

R, on the other hand, was designed to display the results of statistical analysis, with the base graphics module allowing you to quickly create basic charts and plots. ggplot2 can also be used to create more complex plots, such as complex scatter plots with regression lines.

4. Community Support:

Both R and Python have a robust community. Both languages have a mailing list for users, StackOverflow groups, user-contributed documents, and code. As a result, there is a tie between the two languages. However, neither language has customer service. As a result, users are limited to online communities and developer documentation for assistance.

5.Unstructured Data:

Unstructured data accounts for 80% of the world’s data. The majority of the data generated by social media is unstructured. To analyze unstructured data, Python provides packages such as NLTK, scikit-image, and PyPI. R has libraries for analyzing unstructured data, but the support isn’t as good as it is in Python. Nonetheless, both languages can be used to analyze unstructured data.