Top K Frequent Words
Given a list of words and integer k, return the k most frequent words sorted by frequency (descending), then alphabetically for ties.
Output one word per line.
Example:
i,love,leetcode,i,love,coding 2
i love
- The input string is split into a list of words:
['i', 'love', 'leetcode', 'i', 'love', 'coding'] - The frequency of each word is calculated:
{'i': 2, 'love': 2, 'leetcode': 1, 'coding': 1} - The words are sorted by frequency in descending order, then alphabetically for ties:
['i', 'love', 'coding', 'leetcode'] - The top k=2 words are selected from the sorted list and output one per line:
iandlove
Constraints:
- 1 <= len(words) <= 500
- 1 <= k <= number of unique words
Background Knowledge
The "Top K Frequent Words" problem involves hash maps and counting techniques, which are fundamental in data structures and algorithms. A hash map is a data structure that stores key-value pairs in an array using a hash function to map keys to indices of the array. In this problem, we can use a hash map to count the frequency of each word in the given list. The counting technique is used to keep track of the frequency of each word.
To solve this problem, we also need to understand the concept of sorting and priority queues. Since we need to return the k most frequent words, we can use sorting to arrange the words based on their frequency in descending order. For ties, we need to sort the words alphabetically. A priority queue is a data structure that allows us to efficiently extract the maximum or minimum element based on a given priority. In this case, the priority is the frequency of the word.
The problem also involves string comparison, which is used to sort the words alphabetically. In Python, we can use the built-in sorted function to sort a list of strings. We can also use the heapq module to implement a priority queue.
Algorithm/Approach
The general approach to solve this problem is to use a hash map to count the frequency of each word, and then use a priority queue or sorting to extract the k most frequent words. We can also use a bucket sort approach, where we create buckets based on the frequency of the words and then sort the words within each bucket.
Step-by-Step Strategy
Here's a step-by-step breakdown of the solution:
- Create a hash map to store the frequency of each word.
- Iterate through the list of words and update the frequency count in the hash map.
- Use a priority queue or sorting to extract the k most frequent words.
- For ties, sort the words alphabetically.
- Output the k most frequent words, one word per line.
Common Pitfalls
Some common pitfalls to watch out for when implementing the solution include:
- Not handling ties correctly, where words with the same frequency are not sorted alphabetically.
- Not using an efficient data structure, such as a hash map, to count the frequency of each word.
- Not using a priority queue or sorting to extract the k most frequent words.
Time & Space Complexity
The expected time complexity of the solution is O(n log k), where n is the number of words in the list and k is the number of most frequent words to return. The space complexity is O(n), where n is the number of unique words in the list. The time complexity is dominated by the sorting or priority queue operations, while the space complexity is dominated by the hash map used to store the frequency of each word.