If you work in data science or analytics, you’re probably well aware of the Python vs. R debate. Although both languages are bringing the future to life – through artificial intelligence, machine learning and data-driven innovation – there are key differences that set them apart and how to choose the right one for your situation.
What is Python?
Python is a general-purpose, object-oriented programming language that emphasizes code readability through its generous use of white space.
- Several Python libraries support data science tasks including Numpy, Pandas, Matplotlib, and Jupyter Notebooks.
Other key differences
Data collection: Python supports all kinds of data formats, from comma-separated value (CSV) files to JSON sourced from the web.
- R is optimized for statistical analysis of large datasets, and it offers a number of different options for exploring data.
- Data modeling: Python has standard libraries for data modeling, including Numpy for numerical modeling analysis, SciPy for scientific computing and calculations and scikit-learn for machine learning algorithms. R has a specific set of packages known as the Tidyverse that make it easy to import, manipulate, visualize and report on data.
Which is right for you?
Python is a production-ready language used in a wide range of industry, research and engineering workflows
- R is a statistical tool used by academics, engineers and scientists without any programming skills
- It is better suited for statistical learning, with unmatched libraries for data exploration and experimentation
- How important are charts and graphs? R applications are ideal for visualizing your data in beautiful graphics while Python applications are easier to integrate in an engineering environment
What is R?
R is an open-source programming language optimized for statistical analysis and data visualization.
- Popular among data science scholars and researchers, R provides a broad variety of libraries and tools for the following: Cleansing and prepping data, Creating visualizations, Training and evaluating machine learning and deep learning algorithms, R is commonly used within RStudio.
Learn more about Python and R
For computer science purists, Python stands out as the right programming language for data science
The main difference between R and Python: data analysis goals
Python provides a more general approach to data wrangling
- R leans heavily on statistical models and specialized analytics
- Data scientists use R for deep statistical analysis, supported by just a few lines of code and beautiful data visualizations
