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This approach involves iterating over the list of accounts, checking each account's income against the defined salary ranges, and incrementing counters for each category accordingly. The final step is to return a list or dictionary of these counts for each category.
Time Complexity: O(n), where n is the number of accounts.
Space Complexity: O(1), as no additional space other than a few variables is used.
1
public class Program {
public static void CountSalaryCategories(int[,] accounts) {
int lowSalaryCount = 0, avgSalaryCount = 0, highSalaryCount = 0;
for (int i = 0; i < accounts.GetLength(0); i++) {
int income = accounts[i, 1];
if (income < 20000) {
lowSalaryCount++;
} else if (income <= 50000) {
avgSalaryCount++;
} else {
highSalaryCount++;
}
}
Console.WriteLine("+----------------+----------------+");
Console.WriteLine("| category | accounts_count |");
Console.WriteLine("+----------------+----------------+");
Console.WriteLine($"| Low Salary | {lowSalaryCount} |");
Console.WriteLine($"| Average Salary | {avgSalaryCount} |");
Console.WriteLine($"| High Salary | {highSalaryCount} |");
Console.WriteLine("+----------------+----------------+");
}
public static void Main() {
int[,] accounts = {{3, 108939}, {2, 12747}, {8, 87709}, {6, 91796}};
CountSalaryCategories(accounts);
}
}This C# program features a method CountSalaryCategories which iterates through a 2D array of accounts and incomes. It categorizes each account's income into low, average, or high categories, storing the count of each. The results are output in a styled table format.
This approach leverages data aggregation methods available in different languages, such as Java's streams, or Python's `collections` library to group and count salary categories, aiming for a more functional programming approach.
Time Complexity: O(n), stemming from the sequential stream processing.
Space Complexity: O(m), where m is the number of unique salary categories, as it stores results in a map.
1import java.util.Arrays;
2import java.util.Map;
3import java.util.function.Function;
4import java.util.stream.Collectors;
5
6public class Main {
7
8 public static void main(String[] args) {
9 int[][] accounts = {{3, 108939}, {2, 12747}, {8, 87709}, {6, 91796}};
10
11 Map<String, Long> categoryCounts = Arrays.stream(accounts)
12 .map(account -> {
13 int income = account[1];
14 if (income < 20000) return "Low Salary";
15 else if (income <= 50000) return "Average Salary";
16 else return "High Salary";
17 })
18 .collect(Collectors.groupingBy(Function.identity(), Collectors.counting()));
19
20 System.out.println("+----------------+----------------+");
21 System.out.println("| category | accounts_count |");
22 System.out.println("+----------------+----------------+");
23 System.out.printf("| Low Salary | %d |\n", categoryCounts.getOrDefault("Low Salary", 0L));
24 System.out.printf("| Average Salary | %d |\n", categoryCounts.getOrDefault("Average Salary", 0L));
25 System.out.printf("| High Salary | %d |\n", categoryCounts.getOrDefault("High Salary", 0L));
26 System.out.println("+----------------+----------------+");
27 }
28}This Java implementation utilizes streams to categorize the salary of each account into either "Low Salary", "Average Salary", or "High Salary". The stream elements are grouped and counted using Collectors.groupingBy() and Collectors.counting(). Missing categories are handled by default values when fetched from the map.