Exploring Indexing and Slicing in NumPy Arrays

Abstract

Abstract This article provides an in-depth analysis of the application differences between indexing and slicing NumPy arrays to provide a clear guide to scientific computing and data analysis. The article first introduces the basics of NumPy arrays, discusses the deep copy and shallow copy mechanisms, and the role of copies and views in memory management. Then it shows the specific application methods of integer indexing, boolean indexing, fancy indexing, and slicing of one- and two-dimensional arrays through examples. It can be concluded from the experiments that slices are views of the original arrays, and modifications to the views affect the original arrays. While the index returns a copy of the original array, the modification of the copy does not affect the original array. Finally, the syntactic forms, differentiation methods and application scenarios of array indexes and slices are summarized. The research in this paper aims to be able to help researchers and developers better master the indexing and slicing techniques of NumPy arrays, and improve their ability to process and analyze data in practice.