5 Best Ways to Program a Copy of an N-ary Tree in Python

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πŸ’‘ Problem Formulation: In this article, we tackle the challenge of creating a duplicate structure of an n-ary tree in Python. Given a tree that consists of nodes with potentially multiple children, we aim to replicate its architecture to create a new instance with the same hierarchical order. This process is useful where tree-based data needs to replicated for modification or analysis without affecting the original tree. The article defines an n-ary tree, its node has arbitrary children unlike in a binary tree, and provides methods to copy this tree structure.

Method 1: Recursive Deep Copy

The recursive deep copy method involves creating a new tree by traversing the original one. Starting at the root node, it creates a copy of each node and recursively copies its children, thus creating an exact duplicate of the n-ary tree. The function must handle an arbitrary number of child nodes to suit the n-ary structure’s needs.

Here’s an example:

class Node:
    def __init__(self, val):
        self.val = val
        self.children = []

def copy_n_ary_tree(node):
    if not node:
        return None

    new_node = Node(node.val)
    new_node.children = [copy_n_ary_tree(child) for child in node.children]
    return new_node

# Example usage:
root = Node(1)
root.children = [Node(2), Node(3), Node(4)]
root_copy = copy_n_ary_tree(root)

The output of this code will be a new tree root_copy with the same structure and values as the original tree.

This code snippet defines a simple Node class to represent each node in the n-ary tree. The copy_n_ary_tree function recursively copies each node starting from the root. The base case for the recursion occurs when the function encounters a None value, indicating no further children to copy.

Method 2: Iterative Deep Copy Using Stack

The iterative approach utilizes a stack to simulate the recursive process in a controlled manner, allowing for explicit handling of the tree copying process. This method might be preferred in cases where system stack limitations can be a concern with large trees.

Here’s an example:

from collections import deque

def copy_n_ary_tree_iteratively(root):
    if root is None:
        return None
    root_copy = Node(root.val)
    stack = deque([(root, root_copy)])
    while stack:
        node, node_copy = stack.pop()
        for child in node.children:
            child_copy = Node(child.val)
            stack.append((child, child_copy))
    return root_copy

# Example usage:
root_copy_iterative = copy_n_ary_tree_iteratively(root)

The output will be a new tree root_copy_iterative with the same structure and values as the original tree.

This method entails the use of a deque as a stack to hold pairs of nodes from the original and their corresponding copies. It sequentially visits nodes and copies them along with their children links, effectively cloning the original tree without the need for recursion.

Method 3: Using Python’s Copy Library

Python’s built-in copy library can be used to make shallow or deep copies of objects. For copying an n-ary tree, the deepcopy function is appropriate as it recurses through the original tree object, copying all nested children.

Here’s an example:

import copy

root_copy_library = copy.deepcopy(root)

The output will be a new tree root_copy_library that is a deep copy of the original tree.

The deepcopy function saves considerable time and effort as it automates the deep copy process for any object, including complex structures like n-ary trees. However, it assumes that the tree nodes are deep copy compatible and may not be suitable for custom object behaviors.

Method 4: Serialization and Deserialization

Tree copying can also be achieved through serialization (converting the tree to a string or data stream) and subsequent deserialization (reconstructing the tree from that string or data stream). This method can be especially useful for storage or network transmission of the tree structure.

Here’s an example:

import json

def serialize(node):
    if not node:
        return None
    children_serialized = [serialize(child) for child in node.children]
    return {'val': node.val, 'children': children_serialized}

def deserialize(data):
    if not data:
        return None
    node = Node(data['val'])
    node.children = [deserialize(child) for child in data['children']]
    return node
# Example usage:
data_serialized = serialize(root)
root_copy_serialization = deserialize(json.loads(json.dumps(data_serialized)))

The output will be a new tree root_copy_serialization that is a copy of the original tree.

The serialize function converts the tree into a Python dictionary and list structure, which the json library can turn into a string. Deserialization reverses this process to rebuild the tree from the dictionary structure. This string representation can easily be stored or sent over a network.

Bonus One-Liner Method 5: Using a Tree Copy Library

When available, specialized libraries for handling trees may offer their own methods to copy a tree structure. These one-liners are usually the most efficient and simplest to use provided the additional library overhead is acceptable for your project.

Here’s an example:

import some_tree_library

root_copy_library = some_tree_library.copy_tree(root)

The output of this code will be a new tree root_copy_library that is a copy of the original tree.

This fictional some_tree_library.copy_tree() would be a method from a hypothetical third-party library designed to handle tree structures, offering a concise and efficient approach to copying n-ary trees.


  • Method 1: Recursive Deep Copy. Reliable and straightforward. May encounter a problem with the recursion limit for very deep trees.
  • Method 2: Iterative Deep Copy Using Stack. Circumvents recursion limit issues. Slightly more complex than the recursive method due to explicit stack management.
  • Method 3: Using Python’s Copy Library. Simple and quick but relies on objects’ compatibility with the library. Might not respect customized copying behaviors.
  • Method 4: Serialization and Deserialization. Flexible and can be used for transmission or storage. However, it can be slower and less efficient for large trees.
  • Bonus Method 5: Using a Tree Copy Library. Easiest to implement when using a specialized library. Its effectiveness and efficiency are library-dependent and might introduce additional dependencies.