Python vs. R: Which tool should you choose?
The two most popular programming languages for data scientists and analysts are Python and R.
Python is a general multi-purpose programming language that was created by Dutch programmer Guido Van Rossum in the late 80s and released in 1991. Van Rossum was a huge fan of the Monty Python comedy series and named the programming language Python as a tribute. Python was based on ABC language and is focused on code readability and productivity.
R is a domain-specific programming language released in 1995. The R programming language is popular among academics and research professionals.
While these two languages are similar in what they can do when it comes to data and analytics, there are certain areas where one prevails over the other. Here is a brief explanation of the major strengths and weaknesses of both.
The advantages of Python
Python is the language of choice when it comes to repeated tasks, data manipulation, and machine learning.
It offers a whole world of ready-to-use libraries for developing any statical operation or model building. Numpy, Pandas, Scipy, and Matplotlib are just some of the most widely used and popular libraries.
In addition to its analytical capabilities, Python can also be used as a scripting language for websites and other types of applications. This makes it a better option for organizations looking to integrate data analysis with proprietary web applications and databases.
Software engineers that wish to focus on deep dives into data analysis tend to prefer working with Python over R. The coding and QA process is also easier and faster in Python.
Another important advantage Python has over R is that it is easier to learn and use (especially for newcomers, since it’s similar in style with other programming languages.) This means there are far more highly skilled Python developers than R.
Advantages of R
When it comes to statistics, data analytics, and forecasting, R is the programming language to choose. It is more user-friendly and intuitive as far as data analysis and graphical models, as it only takes a few lines of code to create complex statistical scenarios in R.
R wins out over Python when a project is based on standalone computing or analysis. Plus, there are way more packages and libraries available for R than for Python. R is also the preferred language in academic and research organizations.
Both Python and R have their own individual strengths and weaknesses as programming languages. The general consensus among developers is still that Python is better for machine learning projects while R is better suited for biological and academic research applications.
Many data scientists and analysts use a combination of both languages in order to achieve optimal results. Since both languages are free and open source, it is cost-effective to use either. Of course, you may have a tougher time finding developers who are highly experienced with R as opposed to Python.
Created by Alex Sokolov