Parameter Python R
General Being a general-purpose programming language, Python is widely used for data analysis and scientific computing. R is predominantly used for statistical computing and graphics due to its functional programming environment.
Release Date First Released in 1991 as Python 0.9.0 First released in August 1993
Designed By Guido van Rossum Ross Ihaka and Robert Gentleman
Objective A general purpose language used for Data Science, Web Development, and Embedded Systems A statistical programming language used for Data Science and Statistical Modeling
IDE PyCharm, Spyder, Thonny, IPython RStudio, Eclipse, StatET, R KWARD
Packages and Libraries Numpy, Pandas, Pytest, Matplotlib, Requests, TensorFlow, sci-kit-learn, PyTorch, Theano Ggplot2, data.table, dplyr, Plotly, tidyr, readr, stringr, lubridate, shiny
Syntax Python has a relatively simple syntax and is easy to learn. R has a complex syntax and a relatively large learning curve.
Workability Python consists of many easy-to-use packages R easily performs matrix computation and optimization.
Integration Programs that Run Locally Well-integrated with web apps
Database Handling Capacity Handles huge database size Handles all database sizes
Community Python has a more robust community for ongoing support and development. R Community is comparatively smaller.
Learning Curve Linear and smooth Difficult at the beginning
Machine Learning Excellent for machine learning with libraries such as Scikit-learn and TensorFlow Equally good for machine learning with libraries such as Caret and H2O.
Data Handling Capabilities Python handles structured data effectively and the libraries are efficient for data manipulation, data cleaning, visualization, and for importing and exporting the data. R is well suited to handle both, structured and unstructured data. With different syntax, it provides similar data manipulation and cleaning functionality along with visualization.