How do you solve Find the Most Common Response on LeetCode?

Find the Most Common Response (LeetCode #3527) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Using a set efficiently removes duplicates.

What is the time complexity of Find the Most Common Response?

The optimal solution for Find the Most Common Response runs in O(n) time and uses O(n) space. This complexity is efficient due to direct counting and using a set to manage duplicates.

What is the brute force solution for Find the Most Common Response?

The brute force approach for Find the Most Common Response is Brute Force, with O(n²) time complexity and O(1) space complexity. Check each response for duplicates and count occurrences manually. This is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Find the Most Common Response?

Find the Most Common Response uses the following concepts: Array, Hash Table, String, Counting. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Find the Most Common Response?

Explain your thought process clearly. Discuss edge cases, like empty responses. Practice coding on a whiteboard or online compiler.

What are common mistakes when solving Find the Most Common Response?

Not using a set to remove duplicates. Forgetting to handle ties lexicographically.

#3527

Find the Most Common Response

Medium

ArrayHash TableStringCountingHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

Use a HashMap to efficiently count unique responses across all days, ensuring O(n) time complexity.

⚙️

Algorithm

3 steps

1Step 1: Initialize a HashMap to count occurrences of each response.
2Step 2: For each day's responses, convert to a set to remove duplicates and update counts.
3Step 3: Find the response with the highest count, resolving ties lexicographically.

solution.py9 lines

1from collections import defaultdict
2
3def most_common_response(responses):
4    count = defaultdict(int)
5    for day in responses:
6        unique_responses = set(day)
7        for response in unique_responses:
8            count[response] += 1
9    return min((response for response in count if count[response] == max(count.values())), key=lambda x: x)

ℹ

Complexity note: This complexity is efficient due to direct counting and using a set to manage duplicates.

1Using a set efficiently removes duplicates.
2Counting with a HashMap allows for quick frequency checks.

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