Modify Columns

Modify Columns - Problem

Database Easy

You have a DataFrame employees with two columns:

Column Name	Type
name	object
salary	int

A company intends to give its employees a pay rise. Write a solution to modify the salary column by multiplying each salary by 2.

The result should be the modified DataFrame with updated salaries.

Input & Output

Example 1 — Basic Employee DataFrame

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

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

💡 Note: Alice's salary: 50000 × 2 = 100000, Bob's salary: 60000 × 2 = 120000

Example 2 — Single Employee

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

› Output: [{"name": "John", "salary": 150000}]

💡 Note: John's salary doubled: 75000 × 2 = 150000

Example 3 — Multiple Employees

$ Input: employees = [{"name": "Emma", "salary": 40000}, {"name": "Mike", "salary": 80000}, {"name": "Sara", "salary": 55000}]

› Output: [{"name": "Emma", "salary": 80000}, {"name": "Mike", "salary": 160000}, {"name": "Sara", "salary": 110000}]

💡 Note: All salaries doubled: Emma 40000→80000, Mike 80000→160000, Sara 55000→110000

Constraints

1 ≤ employees.length ≤ 10⁴
1 ≤ salary ≤ 10⁶
name consists of alphanumeric characters and spaces

Visualization

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Asked in

G Google 15 a Amazon 12 M Microsoft 10 f Facebook 8

The key insight is to use vectorized DataFrame operations to modify entire columns at once. The optimal approach uses employees['salary'] = employees['salary'] * 2 for efficient bulk operations. Time: O(n), Space: O(1)

Common Approaches

✓ Row-by-Row Modification

⏱️ Time: O(n) Space: O(1)

Loop through each row in the DataFrame and individually multiply each salary value by 2. This approach treats each row separately and updates the salary column one value at a time.

Vectorized Column Operation

⏱️ Time: O(n) Space: O(1)

Use pandas vectorized operations to multiply the entire salary column by 2 in a single operation. This is the most efficient and pythonic way to modify DataFrame columns.

Row-by-Row Modification — Algorithm Steps

Iterate through each row in the DataFrame
Access the salary value for current row
Multiply salary by 2 and update the row

Visualization

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Step-by-Step Walkthrough

Start

Begin with original DataFrame

Process Row

Multiply current row's salary by 2

Continue

Move to next row and repeat

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

typedef struct {
    char name[100];
    int salary;
} Employee;

void solution(Employee* employees, int size) {
    for (int i = 0; i < size; i++) {
        employees[i].salary *= 2;
    }
}

int parseEmployees(char* input, Employee* employees) {
    int count = 0;
    char* ptr = input;
    
    while ((ptr = strstr(ptr, "\"name\":")) != NULL) {
        ptr += 8; // Skip '"name":'
        while (*ptr == ' ' || *ptr == '"') ptr++; // Skip spaces and quote
        
        int nameLen = 0;
        while (ptr[nameLen] != '"') nameLen++;
        strncpy(employees[count].name, ptr, nameLen);
        employees[count].name[nameLen] = '\0';
        
        ptr = strstr(ptr, "\"salary\":");
        if (ptr) {
            ptr += 9; // Skip '"salary":'
            while (*ptr == ' ') ptr++; // Skip spaces
            employees[count].salary = atoi(ptr);
            count++;
        }
        ptr++;
    }
    return count;
}

int main() {
    char input[10000];
    fgets(input, sizeof(input), stdin);
    
    Employee employees[1000];
    int size = parseEmployees(input, employees);
    
    solution(employees, size);
    
    printf("[");
    for (int i = 0; i < size; i++) {
        if (i > 0) printf(",");
        printf("{\"name\":\"%s\",\"salary\":%d}", employees[i].name, employees[i].salary);
    }
    printf("]\n");
    
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Single loop through all n rows in the DataFrame

✓ Linear Growth

Space Complexity

O(1)

Only modifying existing DataFrame in-place, no extra space

✓ Linear Space

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Input & Output

Constraints

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Related Problems

Common Approaches

Row-by-Row Modification — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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