Visualizing Global Climate Change Trends: A Data-Driven Analysis of Temperature Anomalies and Regional Patterns

Climate change represents one of the most pressing challenges of the 21st century, with global temperature anomalies serving as critical indicators of environmental transformation. This study employs comprehensive data visualization techniques to analyze global temperature trends from 1880 to 2024, utilizing datasets from the National Oceanic and Atmospheric Administration (Met Office Hadley Centre). Through the application of multiple visualization methodologies—including time series plots, heatmaps, grouped bar charts, and comparative regional analyses—this research transforms complex climatological data into accessible insights. The visualizations reveal significant warming trends, with average global temperatures increasing by approximately 1.1°C since the pre-industrial era, accelerated warming in recent decades, and notable regional variations. The study demonstrates how effective data visualization can bridge the gap between scientific data and public understanding, supporting evidence-based policy decisions and climate action. By employing Python libraries such as Matplotlib and Seaborn, this work creates high-quality, publication-ready visualizations that highlight temporal patterns, seasonal variations, and geographical disparities in temperature changes. The findings underscore the urgency of climate mitigation efforts and illustrate the power of data visualization in communicating complex environmental phenomena to diverse audiences, including researchers, policymakers, and the general public. **Keywords:** Climate Change, Data Visualization, Temperature Anomalies, Global Warming, Environmental Data Analysis, Matplotlib, Seaborn ---