An attempt to visualize and interpret 13 indicators for demographics and economics and 9 indicators for health from the Gapminder World data is presented. Basic correlations are evaluated to help answer the original project questions "What makes us sick?" and "How does the environment we live in influence our health?". Python 3 functions are implemented for the visualization of: two variables for all countries over time (animated GIF), choropleth for one variable all countries over the world map and one vatiable over time for a given list of countries.