IN-DEPTH ANALYSIS OF ENERGY CONSUMPTION ACROSS VARIOUS REGIONS USING PANDAS DATA ANALYSIS TOOLS - Meliboev, Azizjon (University Research Base, 2024)
This paper presents a comprehensive analysis of energy consumption patterns across various regions utilizing the Pandas data analysis library in Python. The primary objective is to identify underlying trends, regional disparities, and key factors influencing energy usage by examining a rich dataset encompassing variables such as population density, industrial activities, climatic conditions, and socioeconomic indicators. Leveraging the robust data manipulation and analytical capabilities of Pandas, we efficiently processed large-scale datasets, performed intricate calculations, and generated insightful visualizations. Our findings reveal significant variations in energy consumption correlated with economic development levels, climate zones, and industrialization rates among the regions studied. These insights offer valuable implications for policymakers and energy sector stakeholders aiming to formulate targeted strategies for enhancing energy efficiency and promoting sustainable practices. The study underscores the efficacy of using Pandas as a powerful tool for conducting large-scale data analyses in energy consumption research.