What is the time complexity of Transform Array by Parity?

The optimal solution has a time complexity of O(n log n).

What data structures are used to solve Transform Array by Parity?

This problem uses Array, Sorting, Counting. Understanding these concepts is essential for solving this Easy problem efficiently.

What are the key insights for solving Transform Array by Parity?

The problem can be solved in two distinct phases: transforming the array elements and then sorting the transformed array. In-place modification of the input array is efficient as it avoids creating a new array for the transformed values.

Which companies ask LeetCode 3467. Transform Array by Parity in interviews?

This problem is frequently asked at Infosys.

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Transform Array by Parity - LeetCode 3467 Solution

Transform Array by Parity - Complete Solution Guide

Transform Array by Parity is LeetCode problem 3467, a Easy level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

You are given an integer array nums . Transform nums by performing the following operations in the exact order specified: Replace each even number with 0. Replace each odd numbers with 1. Sort the modified array in non-decreasing order. Return the resulting array after performing these operations. Example 1: Input: nums = [4,3,2,1] Output: [0,0,1,1] Explanation: Replace the even numbers (4 and 2) with 0 and the odd numbers (3 and 1) with 1. Now, nums = [0, 1, 0, 1] . After sorting nums in non-de

Detailed Explanation

The problem asks you to transform an integer array `nums` according to a specific set of rules. First, replace each even number with 0 and each odd number with 1. Second, sort the modified array in non-decreasing order. Finally, return the resulting array. The input is an integer array, and the output is the transformed and sorted array. Constraints specify that the array length is between 1 and 100, inclusive, and each element's value is between 1 and 1000, inclusive.

Solution Approach

The provided solution uses a straightforward approach. It iterates through the input array, replacing even numbers with 0 and odd numbers with 1. Then, it uses the built-in sorting function of the respective language (like `Arrays.sort()` in Java, `sort()` in Python and C++, and a bubble sort implementation in the C example) to sort the modified array in ascending order. The sorted array is then returned as the result.

Step-by-Step Algorithm

Iterate through each element `nums[i]` in the input array `nums`.
Check if `nums[i]` is even using the modulo operator (`%`).
If even, replace `nums[i]` with 0; otherwise, replace it with 1.
After the transformation, sort the array `nums` in non-decreasing order using a built-in sorting algorithm.
Return the sorted array `nums`.

Key Insights

The problem can be solved in two distinct phases: transforming the array elements and then sorting the transformed array.
In-place modification of the input array is efficient as it avoids creating a new array for the transformed values.
The `sort()` function (or its equivalent in different languages) is the most efficient way to sort the transformed array.

Complexity Analysis

Time Complexity: O(n log n)

Space Complexity: O(1)

Topics

This problem involves: Array, Sorting, Counting.

Companies

Asked at: Infosys.

Transform Array by Parity | LeetCode 3467

Easy

Array Sorting Counting

Time: O(n log n)Space: O(1)

Solution Code

Available in:

import java.util.Arrays;

class Solution {
    public int[] transformArray(int[] nums) {
        for (int i = 0; i < nums.length; i++) {
            if (nums[i] % 2 == 0) {
                nums[i] = 0;
            } else {
                nums[i] = 1;
            }
        }
        Arrays.sort(nums);
        return nums;
    }
}

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Problem Info

Description

Problem 3467: Transform Array by Parity - Detailed explanation and solution approaches.

Topics

Array Sorting Counting

Companies

Infosys

Stats

Difficulty:Easy

Languages:4

Companies:1

Problem #:3467

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

5 steps

1
Iterate through each element `nums[i]` in the input array `nums`.
2
Check if `nums[i]` is even using the modulo operator (`%`).
3
If even, replace `nums[i]` with 0; otherwise, replace it with 1.
4
After the transformation, sort the array `nums` in non-decreasing order using a built-in sorting algorithm.
5
Return the sorted array `nums`.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(n log n)

✨ Log-linear - Great for sorting algorithms!

💾

Space Complexity

O(1)

💪 Constant space - Uses same memory regardless of input!

📝

Why?

The time complexity is dominated by the sorting step, which typically has a time complexity of O(n log n) for efficient algorithms like merge sort or quicksort. The transformation step has a linear time complexity O(n). The space complexity is O(1) because the solution modifies the input array in-place; it doesn't use any extra space that scales with the input size.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Counting Sort

Since the transformed array only contains 0s and 1s, a counting sort can be used to sort the array in linear time, O(n).

Time: O(n)Space: O(1)

Trade-offs: Counting sort is more efficient than general-purpose sorting algorithms like merge sort or quicksort when dealing with a small, fixed range of integers (0 and 1 in this case). However, it's not as versatile; it's only suitable for this specific scenario.

✨

Key Insights

1The problem can be solved in two distinct phases: transforming the array elements and then sorting the transformed array.
2In-place modification of the input array is efficient as it avoids creating a new array for the transformed values.
3The `sort()` function (or its equivalent in different languages) is the most efficient way to sort the transformed array.

📚

Key Terms Explained

For Students

Array

A collection of items stored at contiguous memory locations. Like a row of boxes.

Sorting

Arranging elements in a specific order (ascending/descending).

Edge Cases

Empty array: The solution handles an empty array gracefully (although the problem constraints specify a non-empty array). Many sorting algorithms handle empty input efficiently.
Array with all even numbers: The resulting array will contain only zeros after the transformation and sorting.
Array with all odd numbers: The resulting array will contain only ones after transformation and sorting.

⚠️

Common Mistakes to Avoid

✗Forgetting to sort the array after transformation: The problem explicitly states that the array must be sorted after the transformation.

✗Inefficient sorting: Using a slow sorting algorithm like bubble sort (as seen in the C example) will result in a time complexity of O(n^2), which is less efficient than O(n log n). This becomes significant for larger input sizes.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Data preprocessing: This type of transformation and sorting could be a preprocessing step in machine learning algorithms where binary representation of data is beneficial.

🏦Signal processing: Converting continuous signals into binary representations (0 for below threshold, 1 for above threshold) and then analyzing the patterns in the sorted binary data is common in signal processing.

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

When dealing with arrays, always consider if sorting would simplify the problem. Many array problems have O(n) solutions using hash maps or two-pointer technique.

🎯 Interview Tip

Don't rush. Explain your thought process clearly. Easy problems are often about communication.

⚠️ Watch Out

Off-by-one errors are the most common mistake. Double-check your loop bounds and array indices.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: July 18, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

I was asked this exact problem at my Google interview. The interviewer wanted me to optimize space complexity.

@InterviewProJul 13

Try solving this without using extra space first. It's a great exercise for understanding the underlying pattern.

@CodeNinjaJul 6

In a real interview, always clarify constraints first. Ask about input size and value ranges.

@TechLeadJun 29

❓

Frequently Asked Questions

When should I use in-place modification vs creating a new array?

Use in-place modification when space complexity matters (O(1) space requirement) and the original array can be modified. Create a new array when you need to preserve the original data or when the problem involves significant restructuring that would complicate in-place logic.

What should I learn from easy problems?

Easy problems introduce core patterns that appear in harder problems. Master basic operations (iteration, conditionals, simple data structures), recognize common patterns (counting, searching, basic transformations), and practice explaining your thought process clearly.

What is the importance of time and space complexity analysis?

Complexity analysis is crucial because: 1) Interviewers always ask about it, 2) It helps you choose between approaches, 3) It demonstrates CS fundamentals. Always state both time and space complexity, and be prepared to explain how you derived them.

What is the optimal approach for "Transform Array by Parity"?

For this easy-level Array problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether sorting techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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