एडवांस्ड फ़िल्टरिंग और विश्लेषण

public class DataExploration {
    public static void main(String[] args) {
    
        // Start with the high earners table from previous exercises
        Table employees = Table.read().csv("employees.csv");

        // Remove outliers (salary < $40K or > $250K)
        Selection outliers = employees.intColumn("Salary").isLessThan(____)
            .or(employees.intColumn("Salary").isGreaterThan(____));
        Table cleanedData = employees.____(outliers);
        
        // Count rows removed after filtering
        System.out.println("Employees after removing outliers: " + (employees.____() - cleanedData.____()));
        
        // Map salaries to Junior, Mid, and Senior grades
		StringColumn salaryGroup = cleanedData.intColumn("Salary").map(
    	salary -> {
        	if (salary < 100000) return "____";
        	else if (salary < 200000) return "Mid";
        	else if (salary >= 200000) return "____";
        	else return "Error";
    	},StringColumn::create).setName("SalaryGrade");
        cleanedData = cleanedData.addColumns(salaryGroup);

        // Filter for Mid/Senior grades under age 30
        Selection highPotential = cleanedData.stringColumn("____")
            .isEqualTo("Mid")
            .or(cleanedData.stringColumn("SalaryGrade").isEqualTo("Senior"))
            .and(cleanedData.intColumn("____").isLessThan(30));

        Table highPotentialEmployees = cleanedData.where(____);
        
        System.out.println(highPotentialEmployees.rowCount());
        System.out.println(highPotentialEmployees.first(5));

	}
}