Completed Hashing-1

[ashritha0806]

No description provided.

[super30admin]

Grouping Anagrams Together (Group_Anagrams.py)

1. The solution is correct and efficient, using a frequency array to represent each string's character counts and converting it to a tuple for use as a dictionary key. This approach effectively groups anagrams.
2. The time complexity is optimal at O(N*K), which is the same as the reference solution. However, note that the reference solution uses a prime number hash method which might have less collision risk, but the frequency tuple method is also robust and standard.
3. The space complexity is O(N*K), which is necessary to store the groups. This is optimal as the problem requires outputting all strings.
4. The code is well-structured and readable, with clear comments explaining the approach. However, the comment mentions "syntaxes to implement logic" which might indicate some initial difficulty, but the final code is correct.
5. One minor improvement: instead of using res.values(), consider returning list(res.values()) to ensure compatibility with the expected return type in some environments (though LeetCode accepts it as is in Python).

VERDICT: PASS

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Isomorphic Strings (Isomorphic_Strings.py)

Your solution is correct and efficient. You have successfully implemented the logic to check for isomorphic strings by maintaining a mapping from s to t and a set to ensure no two characters in s map to the same character in t. This ensures both consistency and injectivity in the mapping.

Strengths:

• You correctly identified the need to check both that each character in s maps consistently to t and that no two characters in s map to the same character in t.
• Your code is clean and well-commented, which makes it easy to understand.
• You achieved the optimal time and space complexity.

Areas for Improvement:

• Variable Naming: Consider using more descriptive variable names such as s_char and t_char instead of char_s and char_t to match the common convention of putting the context first (e.g., s or t) followed by the type (char). This can improve readability.
• Edge Cases: Although your code handles all cases correctly, it's always good to test with edge cases like empty strings (though the constraints say length at least 1) or strings with all the same character (e.g., "aaa" and "bbb").
• Alternative Approach: While your solution is correct, you could also consider using two dictionaries (like the reference solution) to map both directions. This might be slightly more symmetric and could be easier to extend for some problems. However, your approach with one dictionary and one set is equally valid and efficient.

Overall, you did a great job. Keep up the good work!

VERDICT: PASS

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Word Pattern (Word_pattern.py)

Your solution is well-written and efficient. You correctly identified the need to check both directions of the mapping: from pattern characters to words and from words to pattern characters. Using a set to track assigned words is a good approach and ensures that no word is mapped to multiple pattern characters.

Strengths:

• Clear and concise code with meaningful variable names.
• Proper handling of edge cases (like length mismatch).
• Efficient use of data structures (dictionary and set) to achieve O(1) lookups per element.

Areas for improvement:

• Consider using zip to iterate over the pattern and words together, which can make the code more concise and Pythonic. For example:

  if len(pattern) != len(words):
      return False
  for char_p, word_s in zip(pattern, words):
      # rest of the code

• You might also add a comment explaining why the set is used (to ensure no word is assigned to multiple pattern characters) for clarity.

Overall, your solution is excellent and meets all requirements.

VERDICT: PASS