Python vs R for Data Analysis – Which is Better?Saraschandraa M
Python vs R, are often compared to each other as they are both used for data analysis and statistical computing. Both are high-level programming languages that are commonly associated with data science.
And both have their strengths and weaknesses when it comes to data analysis – sometimes the one performs better than the other and vice versa.
The comparison is going to be difficult, as both languages have great packages for data analysis – R with its Tidyverse packages or Python with Pandas and Scikit-learn.
However, to make this easier for you, we have put together a side by side comparison of Python vs R. Let’s take a look:
Comparison between Python vs R
If you just talk about the number of data analysis packages, Python is already the winner; but R has more statistical models built-in. In terms of ease of use, Python is a bit easier to get started with whereas R takes a bit more effort.
Clearly, the two languages have different strengths, and you should carefully decide to go with either of them.
Here’s a quick graphic in tabular format to help you out:
Now, take a detailed look at all the differences.
What is Python?
Python is a high-level programming language that was created by Guido van Rossum in the late 1980s. It is an interpreted language, meaning that it is not directly compiled into machine code. Instead, Python scripts are compiled into bytecode which is then executed on the fly by the Python Virtual Machine (PVM).
Over the years, Python has become increasingly popular for data analysis due to the availability of a variety of open-source libraries making it an excellent and easy tool for data analysis. Other major reasons why Python is used for data analysis are because it is powerful, flexible, and simple to use.
There are hundreds of data analysis libraries available for Python including some popular ones like Numpy, Pandas, and SciPy.
Related: 7+ Best Python IDEs and Code Editors
What is R?
R is a programming language for statistical computing and graphics. It can be used for data analysis, scripting, and debugging. It is free, open-source software that works on most operating systems. It is used by statisticians and data miners, machine learning experts, environmental scientists, and computer programmers of all kinds.
R is gaining popularity as it allows you to easily manipulate and visualize data. It also offers a wide range of statistical methods for handling data, including linear regression, classification, clustering, and survival analysis.
Learning about R’s built-in data structures such as vectors, matrices, arrays, and lists is very helpful for data analysis.
Choosing between Python vs R
Python is much more popular than R, but there’s no doubt about it that R is the king when it comes to data analysis. It’s very mature in its ability to do everything from small-scale statistical analysis to big-data processing on a supercomputer. On the other hand, Python is a great choice for beginners because it’s very easy to pick up and use. It’s highly configurable and can also be a great choice for machine learning and deep learning.
Python is a versatile language that works well with both text and numerical data. With Python, you can also perform basic statistical operations such as calculating the mean, median, and standard deviation. On the other hand, R is a more advanced language suited for higher-level statistical tasks. The R toolset includes statistical functions such as linear regression and classification.
As you can see that there can’t be a one-line answer to, “which is better for data analysis – Python or R?”.
However, as a beginner, we recommend going with Python, and then if you feel the need to become more advanced in the data analysis field, then learn R. Also, learning R becomes a bit easier if you know the basics of Python.
To start learning either of the languages, you can take various online courses. NextStacks has a great Python course that takes you from the level of beginner to expert.
Ultimately, which language you choose will come down to many factors like learning curve, demand, career path, use cases, etc.
But in general, we believe that Python offers more flexibility than R for most beginners and for the majority of tasks.
However, R is still a solid and viable language for data analysis and it has its place.
Also read: Commonly Asked Python Interview Questions
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