Binarytree: Python Library for Studying Binary Trees

Are you studying binary trees for your next exam, assignment or technical interview?

Binarytree is a Python library which lets you generate, visualize, inspect and manipulate binary trees. Skip the tedious work of setting up test data, and dive straight into practising your algorithms. Heaps and BSTs (binary search trees) are also supported.

New in version 6.0.0: You can now use binarytree with Graphviz and Jupyter Notebooks (documentation):

Requirements

Python 3.6+

Installation

Install via pip:

pip install binarytree

For conda users:

conda install binarytree -c conda-forge

Getting Started

Binarytree uses the following class to represent a node:

class Node:

    def __init__(self, value, left=None, right=None):
        self.value = value  # The node value (integer)
        self.left = left    # Left child
        self.right = right  # Right child

Generate and pretty-print various types of binary trees:

from binarytree import tree, bst, heap

# Generate a random binary tree and return its root node
my_tree = tree(height=3, is_perfect=False)

# Generate a random BST and return its root node
my_bst = bst(height=3, is_perfect=True)

# Generate a random max heap and return its root node
my_heap = heap(height=3, is_max=True, is_perfect=False)

# Pretty-print the trees in stdout
print(my_tree)
#
#        _______1_____
#       /             \
#      4__          ___3
#     /   \        /    \
#    0     9      13     14
#         / \       \
#        7   10      2
#
print(my_bst)
#
#            ______7_______
#           /              \
#        __3__           ___11___
#       /     \         /        \
#      1       5       9         _13
#     / \     / \     / \       /   \
#    0   2   4   6   8   10    12    14
#
print(my_heap)
#
#              _____14__
#             /         \
#        ____13__        9
#       /        \      / \
#      12         7    3   8
#     /  \       /
#    0    10    6
#

Build your own trees:

from binarytree import Node

root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.right = Node(4)

print(root)
#
#      __1
#     /   \
#    2     3
#     \
#      4
#

Inspect tree properties:

from binarytree import Node

root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)

print(root)
#
#        __1
#       /   \
#      2     3
#     / \
#    4   5
#
assert root.height == 2
assert root.is_balanced is True
assert root.is_bst is False
assert root.is_complete is True
assert root.is_max_heap is False
assert root.is_min_heap is True
assert root.is_perfect is False
assert root.is_strict is True
assert root.leaf_count == 3
assert root.max_leaf_depth == 2
assert root.max_node_value == 5
assert root.min_leaf_depth == 1
assert root.min_node_value == 1
assert root.size == 5

# See all properties at once:
assert root.properties == {
    'height': 2,
    'is_balanced': True,
    'is_bst': False,
    'is_complete': True,
    'is_max_heap': False,
    'is_min_heap': True,
    'is_perfect': False,
    'is_strict': True,
    'leaf_count': 3,
    'max_leaf_depth': 2,
    'max_node_value': 5,
    'min_leaf_depth': 1,
    'min_node_value': 1,
    'size': 5
}

print(root.leaves)
# [Node(3), Node(4), Node(5)]

print(root.levels)
# [[Node(1)], [Node(2), Node(3)], [Node(4), Node(5)]]

Use level-order (breadth-first) indexes to manipulate nodes:

from binarytree import Node

root = Node(1)                  # index: 0, value: 1
root.left = Node(2)             # index: 1, value: 2
root.right = Node(3)            # index: 2, value: 3
root.left.right = Node(4)       # index: 4, value: 4
root.left.right.left = Node(5)  # index: 9, value: 5

print(root)
#
#      ____1
#     /     \
#    2__     3
#       \
#        4
#       /
#      5
#
root.pprint(index=True)
#
#       _________0-1_
#      /             \
#    1-2_____        2-3
#            \
#           _4-4
#          /
#        9-5
#
print(root[9])
# Node(5)

# Replace the node/subtree at index 4
root[4] = Node(6, left=Node(7), right=Node(8))
root.pprint(index=True)
#
#       ______________0-1_
#      /                  \
#    1-2_____             2-3
#            \
#           _4-6_
#          /     \
#        9-7     10-8
#

# Delete the node/subtree at index 1
del root[1]
root.pprint(index=True)
#
#    0-1_
#        \
#        2-3

Traverse trees using different algorithms:

from binarytree import Node

root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)

print(root)
#
#        __1
#       /   \
#      2     3
#     / \
#    4   5
#
print(root.inorder)
# [Node(4), Node(2), Node(5), Node(1), Node(3)]

print(root.preorder)
# [Node(1), Node(2), Node(4), Node(5), Node(3)]

print(root.postorder) 
# [Node(4), Node(5), Node(2), Node(3), Node(1)]

print(root.levelorder) 
# [Node(1), Node(2), Node(3), Node(4), Node(5)]

print(list(root)) # Equivalent to root.levelorder
# [Node(1), Node(2), Node(3), Node(4), Node(5)]

Convert to list representations:

from binarytree import build

# Build a tree from list representation
values = [7, 3, 2, 6, 9, None, 1, 5, 8]
root = build(values)
print(root)
#
#            __7
#           /   \
#        __3     2
#       /   \     \
#      6     9     1
#     / \
#    5   8
#

# Go back to list representation
print(root.values) 
# [7, 3, 2, 6, 9, None, 1, 5, 8]

Check out the documentation for more details.