Python is a general-purpose, object-oriented programming language that emphasizes code readability through its generous use of white space. Released in 1989, Python is easy to learn and a favorite of programmers and developers. In fact, Python is one of the most popular programming languages in the world, just behind Java and C.

Several Python libraries support data science tasks, including the following:

• Numpy for handling large dimensional arrays

• Pandas for data manipulation and analysis

• Matplotlib for building data visualizations

Plus, Python is particularly well suited for deploying machine learning at a large scale. Its suite of specialized deep learning and machine learning libraries includes tools like scikit-learn, Keras and TensorFlow, which enable data scientists to develop sophisticated data models that plug directly into a production system. Then, Jupyter Notebooks are an open source web application for easily sharing documents that contain your live Python code, equations, visualizations and data science explanations.

R is an open source programming language that’s optimized for statistical analysis and data visualization. Developed in 1992, R has a rich ecosystem with complex data models and elegant tools for data reporting. At last count, more than 13,000 R packages were available via the Comprehensive R Archive Network (CRAN) for deep analytics.

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, an integrated development environment (IDE) for simplified statistical analysis, visualization and reporting. R applications can be used directly and interactively on the web via Shiny.

The main distinction between the two languages is in their approach to data science. Both open source programming languages are supported by large communities, continuously extending their libraries and tools. But while R is mainly used for statistical analysis, Python provides a more general approach to data wrangling.

Python is a multi-purpose language, much like C++ and Java, with a readable syntax that’s easy to learn. Programmers use Python to delve into data analysis or use machine learning in scalable production environments. For example, you might use Python to build face recognition into your mobile API or for developing a machine learning application.

R, on the other hand, is built by statisticians and leans heavily into 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. For example, you might use R for customer behavior analysis or genomics research.

• Data collection: Python supports all kinds of data formats, from comma-separated value (CSV) files to JSON sourced from the web. You can also import SQL tables directly into your Python code. For web development, the Python requests library lets you easily grab data from the web for building datasets. In contrast, R is designed for data analysts to import data from Excel, CSV and text files. Files built in Minitab or in SPSS format can also be turned into R dataframes. While Python is more versatile for pulling data from the web, modern R packages like Rvest are designed for basic webscraping.
• Data exploration: In Python, you can explore data with Pandas, the data analysis library for Python. You’re able to filter, sort and display data in a matter of seconds. R, on the other hand, is optimized for statistical analysis of large datasets, and it offers a number of different options for exploring data. With R, you’re able to build probability distributions, apply different statistical tests, and use standard machine learning and data mining techniques.
• 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. For specific modeling analysis in R, you’ll sometimes have to rely on packages outside of R’s core functionality. But the specific set of packages known as the Tidyverse make it easy to import, manipulate, visualize and report on data.
• Data visualization: While visualization is not a strength in Python, you can use the Matplotlib library for generating basic graphs and charts. Plus, the Seaborn library allows you to draw more attractive and informative statistical graphics in Python. However, R was built to demonstrate the results of statistical analysis, with the base graphics module allowing you to easily create basic charts and plots. You can also use ggplot2 for more advanced plots, such as complex scatter plots with regression lines.

Choosing the right language depends on your situation. Here are some things to consider:

• Do you have programming experience? Thanks to its easy-to-read syntax, Python has a learning curve that’s linear and smooth. It’s considered a good language for beginning programmers. With R, novices can be running data analysis tasks within minutes. But the complexity of advanced functionality in R makes it more difficult to develop expertise.
• What do your colleagues use? R is a statistical tool used by academics, engineers and scientists without any programming skills. Python is a production-ready language used in a wide range of industry, research and engineering workflows.
• What problems are you trying to solve? R programming is better suited for statistical learning, with unmatched libraries for data exploration and experimentation. Python is a better choice for machine learning and large-scale applications, especially for data analysis within web applications.
• How important are charts and graphs? R applications are ideal for visualizing your data in beautiful graphics. In contrast, Python applications are easier to integrate in an engineering environment.

Note that many tools, such as Microsoft Machine Learning Server, support both R and Python. That’s why most organizations use a combination of both languages, and the R vs. Python debate is all for naught. In fact, you might conduct early-stage data analysis and exploration in R and then switch to Python when it’s time to ship some data products.

For computer science purists, Python stands out as the right programming language for data science every time. Meanwhile, R has its own champions. See for yourself on development communities like Stack Overflow. To learn more about the possibilities for data analysis via Python and R, consider exploring the following Learn Hub articles. Checking out the languages of data science tutorial on the IBM Developer Hub is also recommended.

• What is Machine Learning?
• What is Data Mining?
• The languages of data science tutorial

To learn more about accelerating data science development with open source languages and frameworks, explore IBM Watson Studio.