Research says that Data scientists spend about 45% of their time on data preparation tasks, including loading (19%) and cleaning (26%) the data. Pandas is one of the most popular python libraries for tabular data processing because of its diverse utilities and large community support. However, due to its performance issue with the large-scale data processing, there is a strong need for high-performance data frame libraries for the community. Although there are many alternatives available at t...
