An empirical study of Python hash tables

Abstract

This research thesis explores the performance aspects of hash tables in Python, with a specific emphasis on Python sets, examining the internal factors that influence their efficiency. Hash tables are fundamental data structures used extensively in various applications, and their performance plays a crucial role in the overall performance of the systems utilizing them. By investigating the internal factors, such as collision resolution strategies, load factor management, and table resizing techniques, this study aims to identify key optimizations to enhance hash table performance. The research involves experimental evaluations, performance measurements, and comparative analysis to validate the proposed optimizations. The findings contribute to a better understanding of hash table performance in Python and provide guidelines for achieving optimal performance.