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133 changes: 133 additions & 0 deletions dsa-solutions/lc-solutions/0100-0199/0103-zigzag-order-traversal.md
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---
id: binary-tree-zigzag-level-order-traversal
title: Binary Tree Zigzag Level Order Traversal
sidebar_label: 0103-Zigzag Level Order
tags:
- Tree
- Breadth-First-Search
- C++
description: "Given a binary tree, return the zigzag level order traversal of its nodes' values. (i.e., from left to right, then right to left for the next level and alternate between)."
---

## Problem Description

Given a binary tree, return the zigzag level order traversal of its nodes' values. (i.e., from left to right, then right to left for the next level and alternate between).


### Examples

**Example 1:**

![LeetCode Problem - Binary Tree](https://assets.leetcode.com/uploads/2021/02/19/tree1.jpg)
```
Input: root = [3,9,20,null,null,15,7]
Output: [[3],[20,9],[15,7]]
```


### Constraints

- The number of nodes in the tree is in the range $[0, 2000]$.
- $-100 \leq \text{Node.val} \leq 100$

---

## Solution for Binary Tree Problem

### Intuition And Approach

To perform a zigzag level order traversal, we can use a breadth-first search (BFS) with a queue. We'll use a boolean flag to determine the direction of traversal at each level.

1. **Breadth-First Search (BFS):** Traverse the tree level by level.
2. **Direction Toggle:** Use a flag to toggle the direction of traversal for each level (left-to-right or right-to-left).

<Tabs>
<tabItem value="Iterative Simulation" label="Iterative Simulation">


#### Code in Different Languages

<Tabs>
<TabItem value="Java" label="Java" default>
<SolutionAuthor name="@Vipullakum007"/>
```java
class Solution {
public int minDepth(TreeNode root) {
if (root == null) return 0;
Queue<TreeNode> queue = new LinkedList<>();
queue.offer(root);
int depth = 1;
while (!queue.isEmpty()) {
int levelSize = queue.size();
for (int i = 0; i < levelSize; i++) {
TreeNode node = queue.poll();
if (node.left == null && node.right == null) return depth;
if (node.left != null) queue.offer(node.left);
if (node.right != null) queue.offer(node.right);
}
depth++;
}
return depth;
}
}
```

</TabItem>
<TabItem value="Python" label="Python">
<SolutionAuthor name="@Vipullakum007"/>
```python
class Solution:
def minDepth(self, root):
if not root:
return 0
queue = collections.deque([(root, 1)])
while queue:
node, depth = queue.popleft()
if not node.left and not node.right:
return depth
if node.left:
queue.append((node.left, depth + 1))
if node.right:
queue.append((node.right, depth + 1))
```

</TabItem>
<TabItem value="C++" label="C++">
<SolutionAuthor name="@Vipullakum007"/>
```cpp
class Solution {
public:
int minDepth(TreeNode* root) {
if (!root) return 0;
queue<TreeNode*> q;
q.push(root);
int depth = 1;
while (!q.empty()) {
int levelSize = q.size();
for (int i = 0; i < levelSize; ++i) {
TreeNode* node = q.front();
q.pop();
if (!node->left && !node->right) return depth; // Leaf node found
if (node->left) q.push(node->left);
if (node->right) q.push(node->right);
}
++depth;
}
return depth;
}
};
```

</TabItem>
</Tabs>

#### Complexity Analysis

- Time Complexity: $O(n)$ where n is the number of nodes in the binary tree.
- Space Complexity: $O(h)$ where h is the height of the binary tree.

</tabItem>
</Tabs>


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