Binary Tree Level Order Traversal in Python (BFS Explained)

Binary Tree Level Order Traversal — Python Solution

Level Order Traversal is one of the fundamental tree traversal techniques where we visit every node of a binary tree level by level, starting from the root and moving left-to-right within each level. It’s widely used in breadth-first search (BFS) patterns and is a staple in algorithm interviews. :contentReference[oaicite:0]{index=0}

💡 What is Level Order Traversal?

Given the root of a binary tree, the goal is to return a nested list where each sublist represents all node values at that specific level of the tree.

For example, for the binary tree:

        Input: root = [3,9,20,null,null,15,7]

                3
               / \
              9  20
                /  \
               15   7
    

The expected traversal output is:

        [
          [3],
          [9,20],
          [15,7]
        ]
    

📌 Idea Behind the Approach

To traverse level by level, we use a queue. A queue processes nodes in FIFO order, enabling breadth-first traversal. At each step:

• We track the number of nodes in the current level.
• We iterate over all of them, pushing their non-null children to the queue for the next level.
• We collect values level by level into the result list.

This ensures we never mix nodes from different depths. :contentReference[oaicite:1]{index=1}

🧠 Python Implementation

# 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

from collections import deque

class Solution:
    def levelOrder(self, root: Optional[TreeNode]) -> List[List[int]]:
        result = []
        q = deque([root])

        while q:
            current_level = []
            for _ in range(len(q)):
                root = q.popleft()
                if root:
                    current_level.append(root.val)
                    q.append(root.left)
                    q.append(root.right)
            if current_level:
                result.append(current_level)
        return result
    

👉 This solution handles empty trees gracefully and guarantees that only non-null nodes are added to the result. Each level is collected independently and appended only if non-empty.

🧪 Time and Space Complexity

• Time Complexity: O(n) — We visit each node exactly once.
• Space Complexity: O(n) — In the worst case, the queue might hold all nodes on the widest level of the tree.

📈 Visual Intuition

Imagine a wave spreading out level by level: you process the current tier of nodes, then enqueue all children for the next tier — just like a BFS ripple spreading outward from the root. :contentReference[oaicite:2]{index=2}

🔚 Final Thoughts

Level Order Traversal is a classic BFS application. With this pattern, you can build many variations like zigzag traversal, level averages, right-side view, and more.

GitHub Solution - Click here
Happy coding! 🚀