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[freemjstudio] WEEK 14 Solutions #2871

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31 changes: 31 additions & 0 deletions binary-tree-level-order-traversal/freemjstudio.py
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@dalestudy dalestudy Bot Sep 26, 2026

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🏷️ 알고리즘 패턴 분석

binary-tree-level-order-traversal/freemjstudio.py 
from collections import deque

# Definition for a binary tree node.
# class TreeNode:
#     def __init__(self, val=0, left=None, right=None):
#         self.val = val
#         self.left = left
#         self.right = right
class Solution:
    def levelOrder(self, root: TreeNode | None) -> list[list[int]]:
        visits = []

        # bfs
        queue = deque([])
        queue.append(root)

        while queue:
            temp = []
            for _ in range(len(queue)):
                node = queue.popleft()
                if node:
                    temp.append(node.val)

                if node and node.left:
                    queue.append(node.left)
                if node and node.right:
                    queue.append(node.right)
            if temp:
                visits.append(temp)

        return visits
  • 패턴: BFS, Binary Search
  • 설명: 코드가 큐를 이용한 너비 우선 탐색(BFS)로 트리의 각 레벨 값을 순회하는 방식이므로 BFS 패턴에 속합니다. 이웃 노드를 순차적으로 큐에 넣고 레벨 단위로 처리합니다.

📊 시간/공간 복잡도 분석

복잡도
Time O(n)
Space O(n)

피드백: 큐를 사용해 너비 우선 탐색을 수행하고, 각 레벨의 노드를 임시 리스트에 담아 visits에 추가한다. 비트인 노드를 걸러냄으로써 실제 방문하는 노드 수에 비례하는 시간/공간을 사용한다.

개선 제안: 현재 구현이 적절해 보입니다.

💡 풀이에 시간/공간 복잡도를 주석으로 남겨보세요!

Original file line number Diff line number Diff line change
@@ -0,0 +1,31 @@
from collections import deque

# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def levelOrder(self, root: TreeNode | None) -> list[list[int]]:
visits = []

# bfs
queue = deque([])
queue.append(root)

while queue:
temp = []
for _ in range(len(queue)):
node = queue.popleft()
if node:
temp.append(node.val)

if node and node.left:
queue.append(node.left)
if node and node.right:
queue.append(node.right)
if temp:
visits.append(temp)

return visits
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