Modify Columns - Practice Coding Problems

Modify Columns - Problem

Database Easy

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

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.

Time & Space Complexity

Time Complexity

⏱️

O(n)

Linear time but with highly optimized vectorized operations

✓ Linear Growth

Space Complexity

O(1)

Modifies column in-place without creating additional copies

✓ Linear Space

Constraints

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

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
✓ 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)	O(n)	O(1)	Iterate through each row individually and update salary values one by one

Vectorized Operation (Optimal) — Algorithm Steps

Access the entire salary column using employees['salary']
Apply multiplication operation (* 2) to the entire column
Pandas automatically handles the vectorized operation across all rows
The result is assigned back to the salary column

Visualization

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Step-by-Step Walkthrough

Select entire column

Access salary column as a vector

Apply operation

Multiply entire column by 2 at once

Update in parallel

All values updated simultaneously

Return result

Modified DataFrame with all salaries doubled

Code -

solution.c — C

#include <stdio.h>

void modifySalaryColumn(Employee employees[], int n) {
    // Simple loop - C doesn't have vectorized operations
    for (int i = 0; i < n; i++) {
        employees[i].salary *= 2;
    }
}

// Using pointer arithmetic
void modifySalaryColumnPtr(Employee* employees, int n) {
    Employee* end = employees + n;
    for (Employee* emp = employees; emp < end; emp++) {
        emp->salary *= 2;
    }
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Linear time but with highly optimized vectorized operations

✓ Linear Growth

Space Complexity

O(1)

Modifies column in-place without creating additional copies

✓ Linear Space

Constraints

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

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Input & Output

Visualization

Time & Space Complexity

Related Problems

Constraints

Common Approaches

Vectorized Operation (Optimal) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Constraints

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