pystrings/similar_string_groups/__init__.py (1)

67-77: Implementation works but uses different counting approach.

This implementation correctly groups similar strings. However, there's a subtle difference in how it counts groups compared to num_similar_groups:

• This version counts unique roots via set comprehension (line 76)
• num_similar_groups uses DisjointSetUnion.get_count()

Both approaches are correct, but the set-based counting approach is less efficient as it performs O(n) find operations after the union operations complete.

Consider documenting why two implementations exist or consolidating them:

def num_similar_groups_2(strs: List[str]) -> int:
    """
    Alternative implementation that demonstrates counting groups 
    via unique roots instead of maintaining a count during unions.
    """
    n = len(strs)
    uf = DisjointSetUnion(n)
    
    for i in range(n):
        for j in range(i + 1, n):
            if are_similar(strs[i], strs[j]):
                uf.union(i, j)
    
    # Alternative: return uf.get_count() for better performance
    roots = {uf.find(i) for i in range(n)}
    return len(roots)

datastructures/sets/union_find/__init__.py (1)

25-26: Remove debug comments.

Lines 25-26 contain inline comments (# 0, # 1) that appear to be debug artifacts from development. These should be removed for cleaner code.

-        root_i = self.find(i) # 0
-        root_j = self.find(j) # 1
+        root_i = self.find(i)
+        root_j = self.find(j)

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datastructures/sets/union_find/__init__.py (1)

48-74: Consider consolidating or documenting the purpose of having two Union-Find implementations.

Both UnionFind and DisjointSetUnion serve the same purpose but with different optimizations:

• DisjointSetUnion: Uses union by rank + path compression, tracks set count → O(α(n)) amortized per operation
• UnionFind: Uses only path compression → can degrade to O(log n) per operation

This creates potential confusion about which implementation to use. Consider one of the following:

1. Consolidate: Remove UnionFind and use only DisjointSetUnion (recommended for best performance)
2. Document differences: Add clear docstring guidance on when to use each (e.g., "Use UnionFind for simpler cases where minimal memory overhead is preferred; use DisjointSetUnion for optimal performance")
3. Enhance UnionFind: Add union by rank and get_count() to match DisjointSetUnion's API and performance

Based on learnings from the past review comment, option 1 (consolidation) was suggested. If both are needed, consider adding this to the UnionFind docstring:

class UnionFind:
    """
    A minimal Union-Find data structure with path compression.
    
    Note: This is a simpler alternative to DisjointSetUnion without union by rank
    optimization. For performance-critical applications, prefer DisjointSetUnion.
    """

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pystrings/similar_string_groups/__init__.py (1)

17-41: Code duplication between is_similar and are_similar.

The nested is_similar function duplicates the logic of the module-level are_similar function (lines 56-66). This was flagged in previous reviews and remains unaddressed.

Consider removing the nested is_similar and using are_similar directly:

-    # All strings have the same length, per constraints
-    word_len = len(strs[0])
-
     # Initialize Union-Find with n elements, one for each string.
     # The initial count is n (each string is its own group).
     uf = DisjointSetUnion(strs_len)
 
-    def is_similar(s1: str, s2: str) -> bool:
-        """
-        Checks if two strings are similar.
-        Similar means identical (0 diffs) or 1 swap (2 diffs).
-        """
-        diff_count = 0
-        positions_that_differ = []
-        for k in range(word_len):
-            if s1[k] != s2[k]:
-                positions_that_differ.append(k)
-                diff_count += 1
-
-            # Optimization: If more than 2 differences,
-            # they can't be similar.
-            if diff_count > 2:
-                return False
-
-        if diff_count == 2:
-            i = positions_that_differ[0]
-            j = positions_that_differ[1]
-            return s1[i] == s2[j] and s1[j] == s2[i]
-
-        # At this point, diff_count is either 0 or 1
-        # Only 0 differences (identical strings) are similar
-        return diff_count == 0
-
     # Iterate over all unique pairs of strings
     for i in range(strs_len):
         for j in range(i + 1, strs_len):
             # If the strings are similar, merge their groups.
             # The union() method handles decrementing the count
             # only if they were in different groups.
-            if is_similar(strs[i], strs[j]):
+            if are_similar(strs[i], strs[j]):
                 uf.union(i, j)

pystrings/similar_string_groups/__init__.py (3)

2-2: Remove unused import.

UnionFind is imported but never used in this file.

Apply this diff to remove the unused import:

-from datastructures import DisjointSetUnion, UnionFind
+from datastructures import DisjointSetUnion

---

56-56: Add type hints for consistency.

The are_similar function is missing type hints for parameters and return type, which is inconsistent with the rest of the codebase.

Apply this diff to add type hints:

-def are_similar(s1, s2):
+def are_similar(s1: str, s2: str) -> bool:

---

77-78: Consider using get_count() for consistency.

num_similar_groups_2 manually counts unique roots by creating a set, while num_similar_groups uses the more efficient uf.get_count() method. Both approaches are correct, but using get_count() is more efficient (O(1) vs O(n)) and consistent with the DSU API.

Apply this diff to use get_count():

-    roots = {uf.find(i) for i in range(n)}
-    return len(roots)
+    return uf.get_count()

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