Are you studying binary trees for your next exam, assignment or technical interview?
BinaryTree is a minimal Python library which gives you a simple API to generate, visualize and inspect binary trees so you can skip the tedious work of mocking up test objects and dive right into practising your algorithms! Heaps and BSTs (binary search trees) are also supported.
To install a stable version from PyPi:
~$ pip install binarytreeTo install the latest version directly from GitHub:
~$ pip install -e git+git@github.com:joowani/binarytree.git@master#egg=binarytreeYou may need to use sudo depending on your environment setup.
By default, BinaryTree uses the following class to represent a tree node:
class Node(object):
def __init__(self, value):
self.value = value
self.left = None
self.right = NoneGenerate and pretty-print various types of binary trees:
from binarytree import tree, bst, heap, show
# Generate a random binary tree and return its root
my_tree = tree(height=5, is_balanced=False)
# Generate a random binary search tree (BST) and return its root
my_bst = bst(height=5)
# Generate a random max heap and return its root
my_heap = heap(height=3, is_max=True)
# Pretty-print the trees in stdout
show(my_tree)
show(my_bst)
show(my_heap)List representations are supported as well:
from heapq import heapify
from binarytree import tree, convert, show
my_list = [7, 3, 2, 6, 9, 4, 1, 5, 8]
# Convert the list into a tree and return its root
my_tree = convert(my_list)
# Convert the list into a heap and return its root
heapify(my_list)
my_tree = convert(my_list)
# Convert the tree back to a list
my_list = convert(my_tree)
# Pretty-printing also works on lists
show(my_list)Inspect a tree to quickly see its various properties:
from binarytree import tree, inspect
my_tree = tree(height=10)
result = inspect(my_tree)
print(result['height'])
print(result['node_count'])
print(result['leaf_count'])
print(result['min_value'])
print(result['max_value'])
print(result['min_leaf_depth'])
print(result['max_leaf_depth'])
print(result['is_bst'])
print(result['is_max_heap'])
print(result['is_min_heap'])
print(result['is_height_balanced'])
print(result['is_weight_balanced'])
print(result['is_full'])Use the binarytree.Node class to build your own trees:
from binarytree import Node, show
root = Node(1)
root.left = Node(2)
root.right = Node(3)
root.left.left = Node(4)
root.left.right = Node(5)
show(root)If the default binarytree.Node class does not meet your requirements, you can define and use your own custom node specification:
from binarytree import Node, customize, tree, show
# Define your own null/sentinel value
my_null = -1
# Define your own node class
class MyNode(object):
def __init__(self, data, left, right):
self.data = data
self.l_child = left
self.r_child = right
# Call customize in the beginning of your code to apply your specification
customize(
node_init=lambda val: MyNode(val, my_null, my_null),
node_class=MyNode,
null_value=my_null,
value_attr='data',
left_attr='l_child',
right_attr='r_child'
)
my_custom_tree = tree()
show(my_custom_tree)New in 2.0.0: Utility functions you can play around with:
from binarytree import tree, show_ids, show_all, subtree, prune, leafs
my_tree = tree(height=5, is_balanced=False)
# Show the level-order node IDs instead of values
show_ids(my_tree)
# Show both the node IDs and the values
show_all(my_tree)
# Return the root of the subtree by its level-order ID
subtree(my_tree, node_id=2)
# Prune a node (and its children) by its level-order ID
prune(my_tree, node_id=1)
# Return the leaf nodes of the tree
leafs(my_tree, values_only=True)New in 2.0.0: The default binarytree.Node class comes with additional goodies:
from binarytree import tree
my_tree = tree(height=5, is_balanced=False)
# If you want to use these methods in your own custom class, your class
# will have to inherit from the default binarytree.Node class
my_tree.inspect()
my_tree.show()
my_tree.leafs()
my_tree.subtree(node_id=2).show()
my_tree.subtree(node_id=1).convert()
my_tree.prune(node_id=1).show_all()