Exploratory Data Analysis and Visualization for Global Warming Trends

In the 21st century, global warming has become an extreme matter of concern, increasing several environmental challenges and affecting the ecosystems all over the world. Researchers are investigating distinguished pieces of concern, such as CO2 emissions, deforestation, ocean acidication, renewable natural resources, air pollution, and many others. However, for a new researcher in this domain, it is dicult to identify a starting point and understand how these environmental or social features aect a country's global warming condition. This paper presents an exploratory data analysis and visualization of global warming trends over multiple countries, primarily focused on 21st-century data. A benchmark dataset is used for the experimentation that includes data from 195 countries from 1900 to 2023, including 22 impacting factors for global warming. The impact of dierent environmental factors on global warming in the most aected countries over the last decade has beenanalyzed. The aecting pattern between dierent environmental factors with the temperature anomaly of a country has been analyzed. Finally, the correlation among all the 22 features over all countries during the 124 years is investigated. Recent python data science libraries such as pandas, matplotlib and seaborn have been used for the data analysis and visualization.