Reconsidering Python Syntax to Enhance Programming Productivity

Abstract

Abstract: Data analytics plays a crucial role in today's society across various domains, driven by technological advancements and exponential data growth. Handling large-scale data poses a challenge due to increased computational and storage requirements. The heterogeneity of tasks in data analytics programming languages complicates integration and interaction, necessitating effective cross-language integration for productivity and extended capabilities. This paper proposes a generalized interpreter accepting various language syntaxes, primarily based on Python and MATLAB, with comparisons to R and Julia. Findings reveal Python's beginner-friendly learning curve and rich resources, Julia's high-performance computing, MATLAB’s numerical prowess and specialized toolbox, and Python and R's focus on flexibility. Both Python and R boast active communities, while Python offers extensive portability, and Julia emphasizes interoperability. Despite syntactic differences, a common interpreter offers flexibility and efficiency, benefiting developers by enabling language selection based on project needs. Challenges can be mitigated through good design and technical solutions. Encouragement for research and innovation in universal interpreter development fosters collaboration, enhancing opportunities in data analysis and scientific computing. Active participation from developers and researchers is encouraged for continual improvement and advancement in the field.