Practical guide for comparing two popular data manipulation libraries

Data science ecosystem is full of highly effective and practical tools and frameworks. New ones are introduced and the existing ones are improved continuously.

Having a variety of selections is a good thing and likely to increase the efficiency in most cases. However, it might also make it hard to decide which one to pick.

In this article, we will compare popular data manipulation libraries in arguably the two most commonly used programming languages in data science domain.

We will see how basic operations are done in Pandas (Python) and Data.table ®. The goal is not to determine if one is superior to or better than the other. I just want to make you familiar with the syntax and show similar or different approaches.

There are many options to use these packages. I’m using R-studio IDE for R and VSCode for Python.

We start with importing the libraries and creating the basic data structures by reading data from a csv file.

#python #r-programming #data-science #artificial-intelligence #data-analysis