How do you solve Sender With Largest Word Count on LeetCode?

Sender With Largest Word Count (LeetCode #2284) 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 hash map allows for efficient counting and retrieval of word counts.

What is the brute force solution for Sender With Largest Word Count?

The brute force approach for Sender With Largest Word Count is Brute Force, with O(n²) time complexity and O(1) space complexity. In the brute force approach, we will iterate through each message and count the words for each sender. This is straightforward but inefficient as it requires multiple passes through the data. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Sender With Largest Word Count?

Sender With Largest Word Count uses the following concepts: Array, Hash Table, String, Counting. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Sender With Largest Word Count?

Always clarify edge cases, such as what to do in case of ties. Think about the data structures that can help optimize your solution. Practice explaining your thought process as you code.

What are common mistakes when solving Sender With Largest Word Count?

Not considering the case sensitivity in sender names. Failing to handle ties correctly when multiple senders have the same word count.

#2284

Sender With Largest Word Count

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)

The optimal solution uses a single pass through the messages and senders to count the words for each sender using a hash map. This approach is efficient because it minimizes the number of iterations and leverages direct access to counts.

⚙️

Algorithm

5 steps

1Step 1: Initialize a hash map to store the total word counts for each sender.
2Step 2: Iterate through the messages and senders simultaneously.
3Step 3: For each message, calculate the word count and update the corresponding sender's count in the hash map.
4Step 4: After processing all messages, iterate through the hash map to find the sender with the maximum word count.
5Step 5: Handle ties by comparing sender names lexicographically.

solution.py14 lines

1# Full working Python code
2messages = ["Hello userTwooo", "Hi userThree", "Wonderful day Alice", "Nice day userThree"]
3senders = ["Alice", "userTwo", "userThree", "Alice"]
4
5def largest_sender(messages, senders):
6    word_count = {}
7    for i in range(len(messages)):
8        count = len(messages[i].split())
9        sender = senders[i]
10        word_count[sender] = word_count.get(sender, 0) + count
11    max_sender = max(word_count.keys(), key=lambda x: (word_count[x], x))
12    return max_sender
13
14print(largest_sender(messages, senders))

ℹ

Complexity note: The time complexity is O(n) because we only make a single pass through the messages and senders. The space complexity is O(n) due to the hash map storing the word counts for each sender.

1Using a hash map allows for efficient counting and retrieval of word counts.
2Lexicographical comparison is crucial when handling ties.

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