Python vs R
The Ultimate Guide to know the basic difference between Python and R
It’s tough to know whether to use Python or R for data analysis. And that’s especially true if you’re a newbie data analyst looking for the right language to start with. In this story, I will help you go through a few aspects, which will surely help you choose a programming language.
Let’s get started.
- Purpose — Both languages are suitable for almost any data science task, from data manipulation and automation to ad-hoc analysis and exploring datasets. Users may leverage both languages for different purposes, Python is a general-purpose programming language for data analysis and scientific computing while R is a language and environment for statistical programming which includes statistical computing and graphics.
- Usability — People with a software engineering background may find Python comes more naturally to them than R. Any piece of functionality is always written the same way with Python. If you have no coding experience, then R may be easier to learn. The same piece of functionality can be written in several ways with R.
- Flexibility — Python is more flexible for creating something that has never been done before. Developers can also use it for scripting websites or other applications. While in R it is easy to use complex functions. All kinds of statistical tests and models are readily available and easily used.
- Ease of Learning — Python’s focus on readability and simplicity means its learning curve is relatively linear and smooth. Python is considered a good language for beginner programmers. R is easier to learn when you start out, but the intricacies of advanced functionalities make it more difficult to develop expertise. R is not hard for experienced programmers to learn.
Many say that R is specifically designed for statisticians (especially when it comes to easy and strong data visualization capabilities). It’s also relatively easy to learn especially if you’ll be using it mainly for data analysis. On the other hand, Python is somewhat flexible because it goes beyond data analysis. Many data scientists and machine learning practitioners may have chosen Python because the code they wrote can be integrated into a live and dynamic web application.
Although it’s all debatable, Python is still a popular choice, especially among beginners or anyone who wants to get their feet wet fast with data analysis and machine learning. It’s relatively easy to learn and you can dive into full-time programming later on if you decide this suits you more.
Extensive Use of Python for Data Analysis
There are now many packages and tools that make the use of Python in data analysis and machine learning much easier. TensorFlow (from Google), Theano, scikit-learn, NumPy, and Pandas are just some of the things that make data science faster and easier.
Another reason for Python’s widespread use is there are countless resources that will tell you how to do almost anything. If you have any questions, it’s very likely that someone else has already asked that and another that solved it for you (Google and Stack Overflow are your friends). This makes Python even more popular because of the availability of resources online.
Conclusion
In the end, I would like to reiterate that Python is the best programming language for data analysis. We can say that Python is a simple, clear, and intuitive programming language. That’s why many engineers and scientists choose Python for many scientific and numeric applications.
We discussed Python and R by considering a few aspects. In my point of view, Python is perfect and the ideal platform where for data analysis and machine learning. R may have advantages and features Python has not. But still, Python is a good starting point and you may get a better understanding of data analysis if you use it for your study and future projects.
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