Visual Analysis of Countries

Tell stories about nations through data and visuals. Countries are often compared using numbers—population, GDP, urbanization, literacy, or other socio-economic measures. But numbers alone are not enough: we need visuals to make patterns and differences clear. In this project, you’ll perform a Visual Analysis of Countries, using Pandas to prepare data and Seaborn to bring insights to life. You’ll begin by loading a dataset containing countries, their cities, and key attributes such as population, GDP per capita, or development indicators. Using Pandas, you’ll clean and transform the data: handling missing values, creating new calculated columns, and filtering subsets (e.g., specific continents or income groups). This ensures your dataset is ready for visualization. Next, you’ll use Seaborn to create powerful and expressive visuals. With bar plots, scatter plots, box plots, and categorical plots, you’ll examine how countries differ in population size, economic performance, or demographic factors. For example, you might compare GDP vs. population, visualize regional differences in literacy, or highlight the spread of urbanization across continents. By the end of this project, you will be able to: Use Pandas to clean, filter, and prepare socio-economic datasets. Apply Seaborn to create comparative visualizations of countries and cities. Identify relationships and patterns across regions and categories. Communicate findings through visuals that are clear and impactful. This project combines data preparation and visualization to transform abstract statistics into stories that anyone can understand. By practicing with real-world style country data, you’ll gain confidence in creating visuals that reveal insights, support arguments, and make data-driven storytelling more effective.