What is the time complexity of Distribute Candies?

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

What data structures are used to solve Distribute Candies?

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

What are the key insights for solving Distribute Candies?

Insight 1: The number of different candy types Alice can eat is limited by either the number of unique candy types or half the total number of candies. Insight 2: Using a set (or a hash table) efficiently determines the number of unique candy types.

Which companies ask LeetCode 575. Distribute Candies in interviews?

This problem is frequently asked at LiveRamp.

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Distribute Candies - LeetCode 575 Solution

Distribute Candies - Complete Solution Guide

Distribute Candies is LeetCode problem 575, 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

Alice has n candies, where the i th candy is of type candyType[i] . Alice noticed that she started to gain weight, so she visited a doctor. The doctor advised Alice to only eat n / 2 of the candies she has ( n is always even). Alice likes her candies very much, and she wants to eat the maximum number of different types of candies while still following the doctor's advice. Given the integer array candyType of length n , return the maximum number of different types of candies she can eat if she on

Detailed Explanation

Alice has 'n' candies of different types represented by an integer array 'candyType'. She needs to eat only 'n/2' candies due to health reasons but wants to maximize the number of *different* candy types she eats. The problem requires you to determine the maximum number of different candy types Alice can consume while adhering to the 'n/2' limit.

Solution Approach

The solution finds the number of unique candy types using a set (or hash table) and then returns the minimum between the number of unique candy types and half the total number of candies. The basic idea is to first calculate the maximum number of candies Alice is allowed to eat, which is n/2. We then find out how many different types of candies there are. Finally, we return the minimum of these two values.

Step-by-Step Algorithm

Step 1: Calculate the maximum number of candies Alice can eat, which is candyType.length / 2.
Step 2: Create a set (or hash table) to store the unique candy types.
Step 3: Iterate through the candyType array and add each candy type to the set. The set automatically handles duplicates, storing only unique values.
Step 4: Determine the size of the set, which represents the number of unique candy types.
Step 5: Return the minimum value between the maximum number of candies Alice can eat (from Step 1) and the number of unique candy types (from Step 4).

Key Insights

Insight 1: The number of different candy types Alice can eat is limited by either the number of unique candy types or half the total number of candies.
Insight 2: Using a set (or a hash table) efficiently determines the number of unique candy types.
Insight 3: We must return the smaller value between the number of unique candy types and n/2 since she can't eat more candies than allowed (n/2), even if there are more unique types available.

Complexity Analysis

Time Complexity: O(n)

Space Complexity: O(n)

Topics

This problem involves: Array, Hash Table.

Companies

Asked at: LiveRamp.

Distribute Candies | LeetCode 575

Easy

Array Hash Table

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

Solution Code

Available in:

class Solution {
    public int distributeCandies(int[] candyType) {
        Set<Integer> distinctCandies = new HashSet<>();
        for (int candy : candyType) {
            distinctCandies.add(candy);
        }
        int maxCandies = candyType.length / 2;
        return Math.min(distinctCandies.size(), maxCandies);
    }
}

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Problem Info

Description

Problem 575: Distribute Candies - Detailed explanation and solution approaches.

Topics

Array Hash Table

Companies

LiveRamp

Stats

Difficulty:Easy

Languages:4

Companies:1

Problem #:575

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
Step 1: Calculate the maximum number of candies Alice can eat, which is candyType.length / 2.
2
Step 2: Create a set (or hash table) to store the unique candy types.
3
Step 3: Iterate through the candyType array and add each candy type to the set. The set automatically handles duplicates, storing only unique values.
4
Step 4: Determine the size of the set, which represents the number of unique candy types.
5
Step 5: Return the minimum value between the maximum number of candies Alice can eat (from Step 1) and the number of unique candy types (from Step 4).

💡

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)

👍 Linear - Good! Visits each element once.

💾

Space Complexity

O(n)

📦 Linear space - Memory grows with input size.

📝

Why?

The time complexity is determined by the loop that iterates through the input array to insert each candy type into the set. The space complexity is determined by the size of the set, which can grow up to 'n' in the worst case where all elements are unique.

📖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

Sorting and Counting

Sort the array and then iterate through it, counting the number of distinct elements. This avoids using a hash set.

Time: O(n log n) due to the sorting step.Space: O(1) if sorting is done in place; otherwise, O(n) for sorting algorithms like merge sort.

Trade-offs: This approach might be slower than using a hash set due to the sorting step, but it could be more memory-efficient if in-place sorting is possible and memory is very limited. The set based approach is generally preferred because of constant time complexity for insertion (average case)

✨

Key Insights

1Insight 1: The number of different candy types Alice can eat is limited by either the number of unique candy types or half the total number of candies.
2Insight 2: Using a set (or a hash table) efficiently determines the number of unique candy types.
3Insight 3: We must return the smaller value between the number of unique candy types and n/2 since she can't eat more candies than allowed (n/2), even if there are more unique types available.

📚

Key Terms Explained

For Students

Array

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

Hash Table

A data structure that maps keys to values for quick lookup. Like a dictionary!

Edge Cases

Edge case 1: All candies are of the same type (e.g., [6, 6, 6, 6]). The set will only contain one element, and the function will return 1.
Edge case 2: The number of unique candies is greater than n/2. In this case, the function will return n/2, ensuring that Alice eats the maximum number of *different* candies within the limit.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Returning the total number of candies (n) instead of n/2 or the number of unique candies.

✗Common mistake 2: Not considering the constraint that Alice can only eat n/2 candies and simply returning the number of unique candy types, even if it exceeds n/2.

✗Common mistake 3: Using a list and checking for duplicates instead of using a set which makes it inefficient as checking list membership has O(n) complexity.

✅

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?

💼Real-world scenario 1: Recommending a limited number of distinct items to a user based on their purchase history or preferences. The 'candies' are the items, 'n' is the maximum items to recommend, and 'n/2' is the limit of the number of distinct recommendations.

🏦Real-world scenario 2: Allocating a limited budget across different categories of resources. The candies are resource allocation, the different candy types are categories, and the number of total candies represents available budget. The problem seeks to maximize the number of categories getting funds.

💡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.

👨‍💻 From Experience

This is one of the first problems I solved when learning Array. It's a great foundation for more complex problems.

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 6, 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

Don't forget to handle the edge case when the input is empty. Many candidates miss this in interviews.

@DevExpert92Jul 13

The key insight here is recognizing the pattern. Once you see it, the solution becomes straightforward.

@AlgoMasterJul 6

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

@InterviewProJun 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.

How do I choose between a hash map and a hash set?

Use a hash set when you only need to track presence/absence of elements (like detecting duplicates). Use a hash map when you need to associate values with keys (like counting frequencies or storing indices). Hash sets use less memory since they only store keys.

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 "Distribute Candies"?

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 hash table techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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