Create a New Column - Practice Coding Problems

Create a New Column - Problem

Databse Easy

You are given a DataFrame employees with the following structure:

Column Name	Type
name	object
salary	int

A company plans to provide its employees with a bonus.

Write a solution to create a new column named bonus that contains the doubled values of the salary column.

Input & Output

Example 1 — Basic Employee Bonus

$ Input: employees = [{"name": "Alice", "salary": 50000}, {"name": "Bob", "salary": 60000}]

› Output: [{"name": "Alice", "salary": 50000, "bonus": 100000}, {"name": "Bob", "salary": 60000, "bonus": 120000}]

💡 Note: Alice's bonus = 50000 * 2 = 100000, Bob's bonus = 60000 * 2 = 120000

Example 2 — Single Employee

$ Input: employees = [{"name": "Charlie", "salary": 75000}]

› Output: [{"name": "Charlie", "salary": 75000, "bonus": 150000}]

💡 Note: Charlie's bonus = 75000 * 2 = 150000

Example 3 — Multiple Employees

$ Input: employees = [{"name": "David", "salary": 45000}, {"name": "Eva", "salary": 55000}, {"name": "Frank", "salary": 65000}]

› Output: [{"name": "David", "salary": 45000, "bonus": 90000}, {"name": "Eva", "salary": 55000, "bonus": 110000}, {"name": "Frank", "salary": 65000, "bonus": 130000}]

💡 Note: Each employee gets bonus = salary * 2: David: 90000, Eva: 110000, Frank: 130000

Constraints

1 ≤ employees.length ≤ 10⁴
1 ≤ salary ≤ 10⁶
name contains only alphabetic characters

Visualization

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Understanding the Visualization

Input DataFrame

Original employees data with name and salary columns

Apply Formula

Calculate bonus = salary * 2 for each employee

Output DataFrame

Extended DataFrame with new bonus column

Key Takeaway

🎯 Key Insight: DataFrame vectorized operations allow efficient column-wide transformations in a single line of code

Asked in

N Netflix 15 A Airbnb 12

The key insight is to use DataFrame vectorized operations for efficient column manipulation. The optimal approach uses employees['bonus'] = employees['salary'] * 2 to create the new column in one line. Time: O(n), Space: O(1)

Common Approaches

Approach	Time	Space	Notes
✓ Row-by-Row Iteration	O(n)	O(1)	Iterate through each row and manually calculate bonus
Vectorized Column Operation	O(n)	O(1)	Use DataFrame vectorized operations to create bonus column

Row-by-Row Iteration — Algorithm Steps

Step 1: Initialize empty bonus column
Step 2: Loop through each row
Step 3: Calculate salary * 2 for each row
Step 4: Assign to bonus column

Visualization

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Step-by-Step Walkthrough

Initialize

Start with original DataFrame

Iterate

Loop through each row and calculate salary * 2

Result

DataFrame with new bonus column

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <cjson/cJSON.h>

char* solution(char* employees_json) {
    cJSON *json = cJSON_Parse(employees_json);
    cJSON *employee = NULL;
    
    // Create new bonus column by iterating through rows
    cJSON_ArrayForEach(employee, json) {
        cJSON *salary = cJSON_GetObjectItem(employee, "salary");
        int bonus_value = salary->valueint * 2;
        cJSON *bonus = cJSON_CreateNumber(bonus_value);
        cJSON_AddItemToObject(employee, "bonus", bonus);
    }
    
    char *result = cJSON_Print(json);
    cJSON_Delete(json);
    return result;
}

int main() {
    char input[10000];
    fgets(input, sizeof(input), stdin);
    input[strcspn(input, "\n")] = 0;
    
    char* result = solution(input);
    printf("%s\n", result);
    
    free(result);
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Must visit each of the n rows once to calculate bonus

✓ Linear Growth

Space Complexity

O(1)

Only creates one new column, no additional data structures

✓ Linear Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Row-by-Row Iteration — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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