Life expectancy analysis with Python

Abstract

Life expectancy analysis is a crucial aspect of public health and demographic research, providing insights into population health trends, socio- economic development, and policy effectiveness. The abstract presents a comprehensive overview of life expectancy analysis methodologies using python programming language. The analysis begins with data acquisition, covering various publicly available datasets such as World Bank indicators, WHO mortality data, and national statistical data bases. Python libraries like Pandas, NumPy, and Requests facilitate data retrieval, preprocessing and cleaning. Following data acquisition, exploratory data analysis (EDA) techniques are applied to understand the distributions, trends and relationships within the dataset. Visualization libraries such as Matplotlib, Seaborn, and Plotly are employed to create insightful plots and charts, aiding in identifying patterns and anomalies