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Modify Columns - Problem

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DataFrame Salary Modification

You are working as a data analyst for a company that has decided to give all employees a 100% salary increase due to exceptional performance this year! 🎉

Given a DataFrame employees with columns name and salary, you need to modify the existing salary column by multiplying each employee's current salary by 2.

Input: A pandas DataFrame with employee names and their current salaries
Output: The same DataFrame with all salaries doubled

Note: This is a DataFrame manipulation problem focusing on column modification operations.

Input & Output

example_1.py — Basic Salary Update

$ Input: employees = pd.DataFrame({'name': ['Alice', 'Bob'], 'salary': [50000, 60000]})

› Output: pd.DataFrame({'name': ['Alice', 'Bob'], 'salary': [100000, 120000]})

💡 Note: Each employee's salary is doubled: Alice goes from $50,000 to $100,000, and Bob goes from $60,000 to $120,000

example_2.py — Single Employee

$ Input: employees = pd.DataFrame({'name': ['Charlie'], 'salary': [75000]})

› Output: pd.DataFrame({'name': ['Charlie'], 'salary': [150000]})

💡 Note: With only one employee, Charlie's salary is doubled from $75,000 to $150,000

example_3.py — Multiple Employees

$ Input: employees = pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie', 'David'], 'salary': [50000, 60000, 70000, 80000]})

› Output: pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie', 'David'], 'salary': [100000, 120000, 140000, 160000]})

💡 Note: All four employees receive the salary doubling: 50k→100k, 60k→120k, 70k→140k, 80k→160k

Constraints

1 ≤ number of employees ≤ 1000
1 ≤ salary ≤ 10⁶
All salaries are positive integers
Employee names are non-empty strings

Visualization

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Understanding the Visualization

Access Column

Select the entire salary column from the DataFrame

Apply Operation

Use vectorized multiplication to double all values at once

Update DataFrame

The modified column replaces the original salary column

Return Result

Return the DataFrame with all salaries successfully doubled

Key Takeaway

🎯 Key Insight: Vectorized operations in pandas allow us to perform mathematical operations on entire columns simultaneously, making data transformations both fast and intuitive.

Asked in

N Netflix 25 ♪ Spotify 20 A Airbnb 15 U Uber 12

The optimal solution uses pandas vectorized operations with employees['salary'] *= 2. This leverages pandas' optimized backend to perform bulk operations efficiently across the entire column in O(n) time with minimal overhead.

Common Approaches

Approach	Time	Space	Notes
✓ Brute Force (Row-by-Row Iteration)	O(n)	O(1)	Iterate through each row individually and update salary values one by one
Vectorized Operation (Optimal)	O(n)	O(1)	Use pandas vectorization to multiply entire salary column by 2 in one operation

Brute Force (Row-by-Row Iteration) — Algorithm Steps

Iterate through each row using iterrows() or index-based loop
Access the salary value for current row
Multiply the salary by 2
Update the DataFrame with the new value

Visualization

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Step-by-Step Walkthrough

Start with first row

Access first employee's salary

Double the salary

Multiply current salary by 2

Update DataFrame

Store the new value back

Repeat for next row

Move to next employee and repeat

Code -

solution.c — C

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

typedef struct {
    char name[50];
    int salary;
} Employee;

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

Time & Space Complexity

Time Complexity

⏱️

O(n)

Must visit each of the n rows exactly once

✓ Linear Growth

Space Complexity

O(1)

Modifies DataFrame in-place without additional space

✓ Linear Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Brute Force (Row-by-Row Iteration) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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