APPLYING PANDAS FOR THE UNIFICATION OF DATA WITH MODAL DISTRIBUTIONS

Abstract

 This work explores the use of the Pandas library for handling and unifying modal distributions in datasets, which are common in real-world data containing multiple peaks or clusters. Modal distributions often represent different subgroups within the data that vary in scale, range, or frequency, making direct analysis or machine learning challenging. Using Pandas, these distributions can be efficiently organized, segmented, normalized, and standardized, allowing each mode to be represented consistently. The library’s functions such as DataFrame, groupby(), and pd.cut() enable easy preprocessing, statistical summarization, and preparation of multimodal data for AI modeling. This approach improves data quality, reduces bias, and ensures reliable input for machine learning and predictive analytics.