This series of blog posts will explain why Xorbits is better and how it works. The initial post will provide an overview of designs and internals of Xorbits, to help you quickly understand Xorbits.
In the world of data science, Pandas is the most popular and widely used Python library for data science, Numpy is the fundamental package for scientific computing with Python and Scikit-learn is the most useful and robust library for machine learning in Python. However, as the amount of data continues to grow, it becomes increasingly difficult for data scientists to process millions or billions of data using these libraries, especially when data volume exceeds the memory capacity of a single machine. Xorbits was created to address this challenge, providing a solution for scaling and accelerating data science workloads, including, but not limited to, those utilizing the aforementioned libraries.
This series of blog posts will explain why Xorbits is better and how Xorbits works. As the first in this series, this article provides the overview of Xorbits internals and designs.