How do you solve Modify Columns on LeetCode?

Modify Columns (LeetCode #2884) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(1) space complexity. Key insight: Understanding how to manipulate DataFrames is crucial for data-related tasks.

What is the brute force solution for Modify Columns?

The brute force approach for Modify Columns is Brute Force, with O(n) time complexity and O(1) space complexity. In this approach, we will iterate through each employee's salary and multiply it by 2. It's straightforward but not the most efficient way to handle the task. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Modify Columns?

Always consider built-in functions that can simplify your code. Explain your thought process clearly when discussing your approach. Practice writing clean and concise code, as readability is important.

What are common mistakes when solving Modify Columns?

Not using built-in methods for DataFrame manipulation, leading to inefficient code. Overcomplicating the solution when a simple operation suffices.

#2884

Modify Columns

Easy

DataFrame manipulationVectorized operations

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n)	O(n)
Space	O(1)	O(1)

💡

Intuition

Time O(n)Space O(1)

This approach leverages built-in DataFrame operations to modify the salary column directly, which is efficient and concise. It avoids explicit loops in favor of vectorized operations.

⚙️

Algorithm

2 steps

1Step 1: Use the DataFrame's built-in functionality to multiply the salary column by 2 directly.
2Step 2: Return the modified DataFrame.

solution.py9 lines

1import pandas as pd
2
3def modify_salaries(employees):
4    employees['salary'] *= 2
5    return employees
6
7# Example usage
8employees = pd.DataFrame({'name': ['Jack', 'Piper', 'Mia', 'Ulysses'], 'salary': [19666, 74754, 62509, 54866]})
9print(modify_salaries(employees))

ℹ

Complexity note: The time complexity remains O(n) because we still iterate through the list of employees, but we do it in a more efficient manner. The space complexity is O(1) as we modify the salaries in place.

1Understanding how to manipulate DataFrames is crucial for data-related tasks.
2Vectorized operations in libraries like pandas are often more efficient than manual loops.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.