A Performance Comparison of Microsoft Excel and Python for Tabular Data Analysis

Abstract

Microsoft Excel is commonly treated as a simple spreadsheet program that is mainly applied to do simple calculations, and Python has become a common solution to complex data analysis based on the programming language. In this work, a technical performance analysis of Microsoft Excel and Python (Pandas) is provided as an attempt to assess their compatibility with basic data analysis functions. The comparison is done which consists of loading datasets, cleaning data, calculating features, aggregating and searching them. The main performance parameter used is execution time and measurements are taken at every processing phase so as to provide a detailed and equitable evaluation. To ensure a robust assessment, the methodology utilized two distinct transactional datasets: a medium-scale over 500000 records and a large-scale dataset over one million records. A key contribution of this work is the implementation of a search loop algorithm as a stress test, where execution time was measured for three specific scenarios: searching for a record at the beginning, at the end, and for a non-existent value. According to the experimental findings, Excel can efficiently handle all of the analyzed tasks and its execution time is as approximate Python as both systems are able to complete the tasks in an approximate time interval. Despite Python having more accuracy in procedures and measurements of time, more automation, and higher reproducibility due to code-based workflows, Excel has competitive analytical capabilities in interactive processing of data. These results underscore the idea that Microsoft Excel can no longer be viewed as an exclusively computational device, and instead of that, it can be viewed as a technically competent data analysis platform capable of performing similarly to Python on large-scaled analytical workloads.