From 36163bda10edcf1ee2554fc1a1da407d89e518dc Mon Sep 17 00:00:00 2001
From: Kai Chappell <git@kschappell.com>
Date: Fri, 20 Jun 2025 16:05:43 +0100
Subject: [PATCH] remove duplicate question file

---
 .../questions/binary-tree-level-order.yaml    | 135 ------------------
 1 file changed, 135 deletions(-)
 delete mode 100644 backend/data/questions/binary-tree-level-order.yaml

diff --git a/backend/data/questions/binary-tree-level-order.yaml b/backend/data/questions/binary-tree-level-order.yaml
deleted file mode 100644
index 6223c49..0000000
--- a/backend/data/questions/binary-tree-level-order.yaml
+++ /dev/null
@@ -1,135 +0,0 @@
-title: Binary Tree Level Order Traversal
-slug: binary-tree-level-order
-difficulty: medium
-leetcode_id: 102
-leetcode_url: https://leetcode.com/problems/binary-tree-level-order-traversal/
-categories:
-  - trees
-  - queue
-patterns:
-  - bfs
-
-description: |
-  Given the `root` of a binary tree, return the level order traversal of its nodes' values
-  (i.e., from left to right, level by level).
-
-constraints: |
-  - The number of nodes in the tree is in the range [0, 2000].
-  - -1000 <= Node.val <= 1000
-
-examples:
-  - input: "root = [3,9,20,null,null,15,7]"
-    output: "[[3],[9,20],[15,7]]"
-    explanation: "Level 0 has 3, level 1 has 9 and 20, level 2 has 15 and 7."
-  - input: "root = [1]"
-    output: "[[1]]"
-    explanation: "Single node at level 0."
-  - input: "root = []"
-    output: "[]"
-    explanation: "Empty tree returns empty list."
-
-explanation:
-  approach: |
-    1. Use a queue for BFS traversal
-    2. Track the number of nodes at current level
-    3. Process all nodes at current level before moving to next
-    4. Add children to queue as we process each node
-    5. Collect values for each level in a separate list
-
-  intuition: |
-    BFS naturally visits nodes level by level. By tracking how many nodes are in the queue
-    at the start of each level, we know exactly when one level ends and the next begins.
-
-    The key insight is that after processing all nodes of level k, the queue contains
-    exactly all nodes of level k+1.
-
-  common_pitfalls:
-    - title: Not tracking level boundaries
-      description: |
-        Without tracking level size, you can't separate nodes into their levels.
-        Capture queue size at the start of each level iteration.
-      wrong_approach: "Processing queue without counting level size"
-      correct_approach: "level_size = len(queue); process level_size nodes"
-
-    - title: Forgetting null check
-      description: |
-        Empty tree (null root) should return empty list, not cause an error.
-
-    - title: Using list as queue
-      description: |
-        Using list.pop(0) is O(n). Use collections.deque for O(1) popleft.
-
-  key_takeaways:
-    - BFS with queue is the standard for level-order traversal
-    - Track level size to group nodes by level
-    - This pattern extends to many tree problems (zigzag, right side view, etc.)
-    - deque is more efficient than list for queue operations
-
-  time_complexity: "O(n)"
-  space_complexity: "O(n)"
-  complexity_explanation: |
-    Time: Visit each node exactly once.
-    Space: Queue holds at most one level of nodes, which is O(n) in worst case (complete tree).
-
-solutions:
-  - approach_name: BFS with Queue (Optimal)
-    is_optimal: true
-    code: |
-      from collections import deque
-
-      class TreeNode:
-          def __init__(self, val=0, left=None, right=None):
-              self.val = val
-              self.left = left
-              self.right = right
-
-      def level_order(root: TreeNode | None) -> list[list[int]]:
-          if not root:
-              return []
-
-          result = []
-          queue = deque([root])
-
-          while queue:
-              level_size = len(queue)
-              level_values = []
-
-              for _ in range(level_size):
-                  node = queue.popleft()
-                  level_values.append(node.val)
-
-                  if node.left:
-                      queue.append(node.left)
-                  if node.right:
-                      queue.append(node.right)
-
-              result.append(level_values)
-
-          return result
-    explanation: |
-      Process nodes level by level using a queue.
-      Track level size to know when to start a new level list.
-
-  - approach_name: DFS with Level Tracking
-    is_optimal: false
-    code: |
-      def level_order(root: TreeNode | None) -> list[list[int]]:
-          result = []
-
-          def dfs(node: TreeNode | None, level: int) -> None:
-              if not node:
-                  return
-
-              if level == len(result):
-                  result.append([])
-
-              result[level].append(node.val)
-
-              dfs(node.left, level + 1)
-              dfs(node.right, level + 1)
-
-          dfs(root, 0)
-          return result
-    explanation: |
-      DFS alternative: pass level as parameter and append to appropriate list.
-      Same time complexity but uses recursion stack space.