What is the time complexity of modifying columns in a 2D array?

The time complexity is O(n) where n is the number of rows in the array, as each row needs to be processed for type conversion.

What is the space complexity of the direct iteration approach?

The space complexity is O(1) additional space for in-place modification, as we only modify existing elements without creating new data structures.

What is the difference between direct iteration and using map?

Direct iteration modifies the array in-place with O(1) additional space, while using map creates a new array with O(n) space but provides a more functional programming style.

Modify Columns | N3mo LC

Difficulty: Easy | Acceptance: 92.30% | Paid: No Topics: Array, Matrix

You are given a 2D array employees where each row represents an employee with columns employee_id, name, and salary. The salary column (index 2) contains string values. You need to modify the salary column by converting all string values to integers.

Examples
Constraints
Approach 1: Direct Iteration
Approach 2: Using Map

Examples

Example 1

Input:

DataFrame employees
+---------+--------+
| name    | salary |
+---------+--------+
| Jack    | 19666  |
| Piper   | 74754  |
| Mia     | 62509  |
| Ulysses | 54866  |
+---------+--------+

Output:

+---------+--------+
| name    | salary |
+---------+--------+
| Jack    | 39332  |
| Piper   | 149508 |
| Mia     | 125018 |
| Ulysses | 109732 |
+---------+--------+

Explanation: Every salary has been doubled.

Constraints

- The array will have at least 1 row.
- Each row will have exactly 3 elements.
- The salary column contains valid integer values as strings.
- employee_id and name columns remain unchanged.

Approach 1: Direct Iteration

Intuition Iterate through each row and convert the salary column value from string to integer.

Steps

Loop through each row in the 2D array.
For each row, access the salary column (index 2).
Convert the string value to integer using parseInt or equivalent.
Update the row with the converted value.

python

from typing import List

def modifySalaryColumn(employees: List[List[str]]) -&gt; List[List[object]]:
    for row in employees:
        row[2] = int(row[2])
    return employees

Complexity

Time: O(n) where n is the number of rows in the array
Space: O(1) additional space (in-place modification)
Notes: Simple and direct approach for type conversion.

Approach 2: Using Map

Intuition Use the map function to transform each row, converting the salary column value.

Steps

Use map to iterate over each row.
For each row, create a new row with the salary value converted to integer.
Return the mapped array.

python

from typing import List

def modifySalaryColumn(employees: List[List[str]]) -&gt; List[List[object]]:
    return [[row[0], row[1], int(row[2])] for row in employees]

Complexity

Time: O(n) where n is the number of rows in the array
Space: O(n) for creating a new array
Notes: Functional approach that creates a new array instead of modifying in-place.