This Python program analyzes teacher salary data across the United States and compares it to cost-of-living metrics. It allows users to load and clean the data, explore specific statistics, generate visual summaries, and interpret trends based on user-selected options. Each function guides the user through a different aspect of the dataset with informative output and visuals.Â
The program is structured using modular functions. It uses key Python libraries such as pandas for data manipulation, matplotlib.pyplot for visualizations, and csv/numpy for data processing. Control structures like if/elif, while loops, and input validation ensure user-friendly interactions. Data cleaning involves type conversions and NaN removal. The program also uses dictionaries and lists to organize intermediate results and menu options.Â
Option 1: Load and clean data (removes duplicates, handles missing values, converts data types).
Option 2: Show average salaries by occupation or region.
Option 3: Filter and display teacher-related jobs.
Option 4: Visualize salary distribution using histograms or bar charts.
Option 5: Compare cost of living with salaries.
Option 6: Perform statistical analysis (e.g., correlation).
Option 7: Summary stats or graphs to highlight data patterns.
Option 8: Exit the program.
After running the program, the user can expect cleaned datasets, printed statistical summaries (mean, max, counts), and a variety of plots like histograms or bar graphs. The final output depends on user-selected options but typically includes readable tables and charts showing salary comparisons, job titles, and cost-of-living insights.Â