Apply Transform Over Each Element in Array

Apply Transform Over Each Element in Array - Problem

JavaScript Easy

Given an integer array arr and a mapping function fn, return a new array with a transformation applied to each element.

The returned array should be created such that returnedArray[i] = fn(arr[i], i).

Please solve it without the built-in Array.map method.

Input & Output

Example 1 — Add One to Each Element

$ Input: arr = [1,2,3], fn = function plusone(n) { return n + 1; }

› Output: [2,3,4]

💡 Note: Apply fn to each element: fn(1,0)=2, fn(2,1)=3, fn(3,2)=4

Example 2 — Add Index to Element

$ Input: arr = [1,2,3], fn = function plusI(n, i) { return n + i; }

› Output: [1,3,5]

💡 Note: Add index to each element: fn(1,0)=1+0=1, fn(2,1)=2+1=3, fn(3,2)=3+2=5

Example 3 — Constant Function

$ Input: arr = [10,20,30], fn = function constant() { return 42; }

› Output: [42,42,42]

💡 Note: Function returns constant: fn(10,0)=42, fn(20,1)=42, fn(30,2)=42

Constraints

0 ≤ arr.length ≤ 1000
-10⁹ ≤ arr[i] ≤ 10⁹
fn returns an integer

Visualization

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Understanding the Visualization

Input

Array [1,2,3] and function fn(x,i) = x + i + 1

Process

Apply function to each element with its index

Output

Transformed array [2,4,6]

Key Takeaway

🎯 Key Insight: Transform each element using both its value and index position

Asked in

G Google 25 F Facebook 20 A Amazon 15

Brief HTML summary: The key insight is to iterate through the array and apply the transformation function to each element with its index. Best approach is iterative transformation using a single pass. Time: O(n), Space: O(n)

Common Approaches

Approach	Time	Space	Notes
✓ Iterative Transformation	O(n)	O(n)	Loop through array and apply function to each element with its index
Pre-allocated Array	O(n)	O(n)	Pre-allocate result array for better memory efficiency

Iterative Transformation — Algorithm Steps

Step 1: Create a new empty array with same length
Step 2: Loop through each element with its index
Step 3: Apply fn(element, index) and store result

Visualization

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Step-by-Step Walkthrough

Input Array

Original array with elements and indices

Apply Function

Transform each element using fn(element, index)

Result Array

New array with transformed values

Code -

solution.c — C

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

void solution(int* arr, int size, char* fnType, int* result) {
    for (int i = 0; i < size; i++) {
        if (strcmp(fnType, "plusone") == 0) {
            result[i] = arr[i] + 1;
        } else if (strcmp(fnType, "plusI") == 0) {
            result[i] = arr[i] + i;
        } else if (strcmp(fnType, "constant") == 0) {
            result[i] = 42;
        }
    }
}

int main() {
    char line[1000];
    fgets(line, sizeof(line), stdin);
    
    // Parse array manually
    int arr[100];
    int size = 0;
    char* token = strtok(line, "[,]");
    while (token != NULL) {
        if (strlen(token) > 0 && token[0] != '\n') {
            arr[size++] = atoi(token);
        }
        token = strtok(NULL, "[,]");
    }
    
    fgets(line, sizeof(line), stdin);
    line[strcspn(line, "\n")] = 0; // Remove newline
    
    int result[100];
    solution(arr, size, line, result);
    
    printf("[");
    for (int i = 0; i < size; i++) {
        printf("%d", result[i]);
        if (i < size - 1) printf(",");
    }
    printf("]\n");
    
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Single pass through array of n elements

✓ Linear Growth

Space Complexity

O(n)

New array to store n transformed elements

⚡ Linearithmic Space

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Medium Frequency

~10 min Avg. Time

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Smart Actions

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Iterative Transformation — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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