Completed BFS-2-1

[ManasviReddy25]

No description provided.

[super30admin]

Rotting Oranges (Problem1.py)

Strengths:

• Excellent documentation with clear, educational comments
• Correct algorithm implementation with proper minute-by-minute BFS
• Good variable naming (fresh, time, queue, directions)
• Proper handling of edge cases (no fresh oranges, unreachable oranges)
• Early return optimization when all oranges become rotten

Areas for Improvement:

• The use of list.pop(0) instead of collections.deque.popleft() is a significant inefficiency. In Python, removing from the front of a list is O(n), while deque popleft is O(1). For a grid of size 10x10 this is negligible, but it's a bad practice that could cause issues with larger inputs.
• Consider using from collections import deque and changing queue = [] to queue = deque() and queue.pop(0) to queue.popleft().

VERDICT: PASS

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Employee Importance (Problem2.py)

Strengths:

• Clear and comprehensive comments explaining the approach and reasoning
• Correct use of a hash map for efficient O(1) lookups
• Proper recursive DFS implementation that handles all subordinates
• Good variable naming and code organization
• Accurate analysis of time and space complexity

Areas for Improvement:

• The use of self.result as a mutable accumulator is functional but not the most Pythonic approach. Consider having dfs() return the importance value instead, making it a pure function that can be composed. This would look like: return emp.importance + sum(self.dfs(subid) for subid in emp.subordinates)
• For very deep hierarchies (up to 2000 levels), the recursive approach could potentially hit Python's recursion limit. An iterative BFS/DFS using a stack or queue (like the reference solution) would be more robust.

VERDICT: PASS