Given a non-empty list of words, return the k most frequent words sorted by frequency (highest to lowest). If frequencies match, sort alphabetically (lower words first)

Approach To answer the interview question effectively, follow this structured framework: Understand the Problem : Identify the requirements, such as input types (list of words, integer k) and expected output (k most frequent words). Plan the Solution :…

Approach

To answer the interview question effectively, follow this structured framework:

1. Understand the Problem: Identify the requirements, such as input types (list of words, integer k) and expected output (k most frequent words).
2. Plan the Solution: Determine the steps needed to achieve the goal, including counting word frequencies and sorting.
3. Implement the Solution: Write the code, ensuring it is clear and efficient.
4. Test the Solution: Validate the implementation with various test cases to ensure accuracy.

Key Points

• Clarity on Requirements: Ensure you understand the input and output specifications.
• Efficiency: Aim for a solution that efficiently counts and sorts words.
• Handling Edge Cases: Consider cases such as ties in frequency and empty inputs.
• Code Readability: Write clean, well-documented code for better understanding.

Standard Response

Below is a fully-formed sample answer to the interview question, demonstrating best practices:

from collections import Counter

def topKFrequent(words, k):
 # Step 1: Count the frequency of each word
 count = Counter(words)
 
 # Step 2: Sort the words first by frequency (descending), then alphabetically (ascending)
 sorted_words = sorted(count.keys(), key=lambda word: (-count[word], word))
 
 # Step 3: Return the top k words
 return sorted_words[:k]

• Counter: Utilizes Python's collections.Counter to count word occurrences efficiently.
• Sorting: Uses a custom sort function that sorts primarily by frequency and then alphabetically.
• Return Statement: Slices the sorted list to return only the top k words.
• Explanation of the Code:

Tips & Variations

Common Mistakes to Avoid:

• Not Handling Edge Cases: Failing to consider scenarios like k being larger than the number of unique words.
• Inefficient Sorting: Sorting the entire list when only the top k are needed can lead to performance issues.

Alternative Ways to Answer:

• Using a Max-Heap: For larger datasets, consider using a max-heap to efficiently retrieve the top k elements.
• Using a Dictionary: If a manual implementation is preferred, a dictionary can be used for counting, followed by a list for sorting.

Role-Specific Variations:

• Technical Roles: Emphasize algorithmic efficiency and edge cases.
• Creative Roles: Focus on clarity and readability of code; explain your thought process in a narrative manner.
• Managerial Roles: Highlight your approach to team collaboration in solving coding challenges.

Follow-Up Questions:

• What is the time complexity of your solution?
• How would you handle very large datasets?
• Can you explain your choice of data structures?

By following this structured approach, candidates can craft strong, effective responses that not only demonstrate their technical skills but also their problem-solving abilities