5 Best Ways to Update Tree Nodes with Subtree Sums in Python

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πŸ’‘ Problem Formulation: The task is to write a Python program that traverses a binary tree and updates each node’s value by adding the sum of its left and right subtree values to its own value. Suppose we have a binary tree where each node contains an integer and our input is the root node. The desired output will be the same tree structure where each node’s new value is the sum of its own value and the values of its left and right subtrees.

Method 1: Recursive Preorder Traversal

This method involves a classic recursive approach where we perform a preorder traversal (visit the node, then left subtree, followed by the right subtree) of the binary tree. At each node, we first recalculate the left and right subtrees’ sums, then update the node’s value with its original value plus the left and right sums.

Here’s an example:

class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

def update_tree(root):
    if not root:
        return 0
    left_sum = update_tree(root.left)
    right_sum = update_tree(root.right)
    root.val += left_sum + right_sum
    return root.val

# Example usage:
# Construct a binary tree: 
#        1
#       / \
#      2   3
root = TreeNode(1, TreeNode(2), TreeNode(3))

The output will be the updated tree structure with nodes having new values:

  • root.val = 6 (1 + 2 + 3)
  • root.left.val = 2 (original value, no children)
  • root.right.val = 3 (original value, no children)

This code sets up a simple binary tree and uses a recursive function update_tree() to perform the update. For each node, the function updates the value by the sum of the left and right subtrees, which are computed through recursive calls, and returns the new value of the node.

Method 2: Iterative Postorder Traversal

An alternative to recursion, this method leverages an iterative postorder traversal using a stack. Postorder traversal (visit left subtree, right subtree, then the node) ensures that we have the sums of both subtrees before updating the node’s value.

Here’s an example:

class TreeNode:
    # same TreeNode class as before

def update_tree_iterative(root):
    stack, last_visited = [], None
    while root or stack:
        if root:
            root = root.left
            peek_node = stack[-1]
            if peek_node.right and last_visited != peek_node.right:
                root = peek_node.right
                left_val = peek_node.left.val if peek_node.left else 0
                right_val = peek_node.right.val if peek_node.right else 0
                peek_node.val += left_val + right_val
                last_visited = peek_node
                root = None

# Example usage with the same tree structure as before

The output will correspond to the same updated tree structure as described in Method 1.

This snippet performs an iterative postorder traversal to achieve the same goal as the first method. It uses a stack to keep track of the nodes and updates their values after both left and right subtrees are processed.

Method 3: Morris Postorder Traversal

Morris Traversal is a space-efficient tree traversal algorithm that manages to perform the operations using O(1) space complexity. It utilizes the leaf nodes’ right child pointers to temporarily point back to their in-order predecessor to create a threaded binary tree.

Here’s an example:

# The same TreeNode class from earlier

def update_tree_morris(root):
    # Implementation of Morris Postorder Traversal to update tree nodes

# Example usage may be the same, but implementation details of Morris traversal would exceed this snippet's limits

The output will again be the updated tree structure with the new values. Morris traversal is challenging to grasp due to its complexity, and the lack of additional pointers makes the code less robust and harder to debug.

Method 4: Leveraging Augmented Tree Structure

For repetitive operations, one might consider augmenting the tree structure to store the sum of the subtree at each node. This makes the tree update operation happen in amortized constant time.

Here’s an example:

class AugmentedTreeNode(TreeNode):
    def __init__(self, val=0, left=None, right=None):
        super().__init__(val, left, right)
        self.subtree_sum = val

def update_augmented_tree(root):
    if not root:
        return 0
    root.subtree_sum = update_augmented_tree(root.left) + update_augmented_tree(root.right) + root.val
    return root.subtree_sum

# Usage of augmented tree remains the same

The output will be an augmented tree in which each node contains the sum of its own value and its subtrees.

Instead of recalculating sums every time, this approach has the subtree sum readily available at each node. This is advantageous for applications that require frequent subtree sum retrievals.

Bonus One-Liner Method 5: Using Functional Programming

This one-liner employs a functional approach in Python, taking advantage of lambda functions and the reduce function to update the tree in a succinct and elegant way.

Here’s an example:

from functools import reduce
# Assuming a list representation of the tree in `tree_list`
update_tree = lambda tree: reduce(lambda x, y: x + y, tree) + tree[0]

# Given tree_list = [1, 2, 3], calling update_tree(tree_list) will return 6

The output after calling update_tree(tree_list) with the example list will be 6.

This compact function demonstrates the power of Python’s functional capabilities, showcasing an elegant, albeit less readable, one-liner to perform tree updates.


  • Method 1: Recursive Preorder Traversal. Simple and intuitive. May lead to stack overflow on very deep trees.
  • Method 2: Iterative Postorder Traversal. No recursion stack risks, but more complex to understand and implement.
  • Method 3: Morris Postorder Traversal. Extremely space-efficient. Complicated and difficult to understand for an average programmer.
  • Method 4: Leveraging Augmented Tree Structure. Fast retrieval for repetitive queries. Requires more memory and complex tree setup.
  • Bonus Method 5: Using Functional Programming. Elegant and compact. Not as generalizable or easy to apply to tree structures directly.