This project, "Insights Through Data Visualization," showcases the application of three different types of data visualization methods to extract meaningful insights from datasets sourced from World Bank Open Data. The visualization techniques used include a line plot, pie chart, and bar graph, each chosen to best represent the nature of the data and to highlight key trends and comparisons.
Line Plot:
Dataset: Adolescent fertility rate in four countries (India, Pakistan, Kuwait, China) from 1999 to 2020.
Insight: The line plot reveals a significant decline in adolescent fertility rates in India, a steady decline in Pakistan and Kuwait, and a minor decline followed by an increase in China. The line plot was selected to effectively show trends over time.
Pie Chart:
Dataset: Internet users per 100 individuals in five European countries in 2010.
Insight: The pie chart indicates that the Netherlands had the highest percentage of internet users, while Ukraine had the lowest. The pie chart was chosen to easily compare the proportion of internet users across different countries.
Bar Graph:
Dataset: Suicide mortality rate in the United States from 2000 to 2019, broken down by gender.
Insight: The stacked bar graph shows that suicide rates for men remained static before rising, while rates for women steadily increased. The bar graph was chosen to compare mortality rates over time and between genders.
The visualizations were created using Python's matplotlib.pyplot for graphing and pandas for data manipulation. The code includes functions for each visualization method, designed to be reusable with different datasets. This project demonstrates appropriate use of visualization techniques, quality figures, clear explanations, and adherence to good coding practices.