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 strengths and weaknesses that come into play.
In many ways, the two open source languages are very similar. Free to download for everyone, both languages are well suited for data science tasks — from data manipulation and automation to business analysis and big data exploration. The main difference is that Python is a general-purpose programming language, while R has its roots in statistical analysis. Increasingly, the question isn’t which to choose, but how to make the best use of both programming languages for your specific use cases.
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:
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:
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.
Choosing the right language depends on your situation. Here are some things to consider:
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.
To learn more about accelerating data science development with open source languages and frameworks, explore IBM Watson Studio.
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