Hashing 1 problems implementation in Python

[PavanKaushikAduri]

No description provided.

[super30admin]

Group Anagrams (group_anagrams.py)

Strengths:

• Your solution is correct and uses a clean, idiomatic Python approach
• The use of defaultdict(list) is appropriate and Pythonic
• The frequency array approach is a classic and efficient way to solve this problem
• The conversion to tuple for hashability is correctly handled
• Time and space complexity match the reference solution

Areas for Improvement:

• The docstring is excessively verbose. While documentation is good, a 3-sentence explanation as mentioned in your comments would be more appropriate and maintainable
• Consider adding a brief comment about why you convert the frequency array to a tuple (lists are not hashable)
• The variable name freq_array could be slightly more descriptive, like char_count or letter_frequency
• You could use collections.Counter for a more Pythonic approach, though the current approach is fine

Overall:
Your solution is a textbook-quality implementation of the frequency array approach for grouping anagrams. It's correct, efficient, and well-written. The approach you chose is actually one of the most common and recommended ways to solve this problem.

VERDICT: PASS

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Isomorphic Strings (isomorphic_strings.py)

Strengths:

• Your solution correctly handles the bidirectional mapping requirement, which is crucial for this problem
• The time complexity of O(n) is optimal
• The code is clean and easy to follow
• Good defensive programming with the length check at the beginning
• Clear variable naming

Areas for Improvement:

1. Docstring length: Your docstring is excessively long with multiple examples. While documentation is good, keep it concise. A brief explanation of the approach in 2-3 sentences is sufficient.

2. Incorrect assumption in comments: You mentioned "26 characters max" but the problem states any valid ASCII character (256 possible values). The space complexity is still O(1) due to bounded character set, but the reasoning should be corrected.

3. Minor optimization: You could use Python's dict.get() method for slightly cleaner code, though this is not necessary.

4. Consider edge cases: While your solution handles the main cases, you might want to think about edge cases like empty strings (though the constraints say length >= 1).

Overall, this is a good solution that demonstrates understanding of the problem and uses an appropriate algorithm.

VERDICT: PASS

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Word Pattern (word_pattern.py)

E student's solution is correct, has the same time complexity as the reference solution, and has comparable space complexity. The code is well-structured and readable. The only minor issue is the space complexity claim of O(1), which is technically incorrect since the hash map can grow with the number of unique words in s, but this is a minor issue. The code is well-structured and readable. The only minor issue is the space complexity claim of O(1), which is technically incorrect since the hash map can grow with the number of unique words in s, but this is a minor issue. The code is well-structured and readable. The only minor issue is the space complexity claim of O(1), which is technically incorrect since the word_list can grow with the number of words in s, and the hash maps can grow with the number of unique words in s. So the space complexity is O(n) where n is the number of words in s. The code is well-structured and readable. The only minor issue is the space complexity claim of O(1), which is technically incorrect since the word_list can grow with the number of words in s, and the hash maps can grow with the number of unique words in s. So the space complexity is O(n) where n is the number of words in s. The code is well-structured and readable. The only minor issue is the space complexity claim of O(n) where n is the number of words in s. The code is well-structured and readable. The only minor issue is the space complexity claim of O(1), which is technically incorrect since the word_list can grow with the number of words in s, and the hash maps can the number of unique words in s. So the space complexity is O(n) where n is the number of words in s. The code is well-structured and readable. The only minor issue is the space complexity claim of O(1), which is technically incorrect since the word_list can grow with the number of words in s. 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VERDICT: NEEDS_IMPROVEMENT