Python and R for Data Science:
Today, we will discuss the two most popular languages used in the field of Data Science- Python and R. When one makes the decision of learning this exciting field, they often have to choose between one of these languages. Both Python and R have their pros and cons. Hence this decision depends on various factors such as how much programming experience one has, whether they’ll be working in an academic or an industry setting, etc.
Python is a general-purpose programming language that is easy to write as well as understand. It reads more like a regular human comprehensible language such as English. Over the past few years, it’s popularity has been increasing. It now finds uses in a wide variety of fields including web development and scientific computing. R, on the other hand, is a language that is built almost exclusively for statistical computing. Researchers in statistics widely use R.
In this post, we compare the two on eight factors. This should serve as a good resource for making the decision on which one is better for you!
#1. The Start
It is a lot easier to build your first model on R than it is on Python. You take a data set, import it, and use one of the built in libraries to run an algorithm and generate an output. In Python, however, it is harder as you have a number of options (such as the data structure to be used). That being said, after the initial obstacle, Python is easier to learn. This brings us to our next point.
#2. Learning Curve
R has a steeper learning curve than Python. This comes down mostly to syntactical details, on which Python wins by a mile. It has an extremely expressive syntax that is in some ways very similar to regular English. Most universities teach their introductory programming courses in Python. It’s much easier to pick up and get started with Python than with other languages such as C or Java which are relatively verbose in nature.
Libraries (or “packages” in Python) make your life a lot easier by providing you with pre-written code that you can use. Both languages come pre-loaded with a number of codes. Python has one of the most popular machine learning libraries named scikit-learn. R also has its fair share of libraries, of which ggplot2 is extremely popular for making powerful visualizations.
Python being a general purpose programming language, has a much bigger community of people using it. R has also been catching up though. Its popularity has grown over time with the internet now having many data science examples that are written in R.
In the beginning, R was fairly slower than Python. But as time has progressed, a majority of R has been rewritten in C, thus making it pretty fast. Python, too, has libraries such as numpy (written in C), fastening operations!
RStudio is way popular than any other IDE for R. RStudio makes it easier to manage R libraries. In fact, it is one of the reasons for the rise in the popularity of R in recent times. Python’s counterpart to RStudio is Spyder (Though PyCharm is another amazing IDE.)
#7. Data Visualization
As we’ve already mentioned, R’s ggplot2 is a fantastic library for data visualization. It feels natural; it is easy to learn; and is flexible enough to produce any visualization of your choice! Python has matplotlib as its standard visualization package and a lot of other Python packages also use it.
Many companies employ a Python-based application stack. So, they prefer to use Python for their Data Science needs as well, since it makes integrating everything together easier, rather than using a completely different language such as R.
Python and R are indeed the popular languages for Data Science! Choosing from the two can indeed be very tough! Hopefully these 8 points help you in making that choice.
The final verdict- So, if you’re new to programming and are looking to learn more in a short period of time, go with Python. If you’re looking to work as a Data Scientist in a company, Python is a better option then too! But if you are looking to build statistical models in an academic setting, R may be the one to go with!