techscientist / binarytree

Python Library for Learning Binary Trees

BinaryTree: Python Library for Learning Binary Trees

Introduction

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

BinaryTree is a minimal Python library which provides you with a simple API to generate, visualize and inspect binary trees so you can skip the tedious work of mocking up test trees, and dive right into practising your algorithms! Heaps and BSTs (binary search trees) are also supported.

Installation

To install a stable version from PyPi:

~$ pip install binarytree

To install the latest version directly from GitHub:

~$ git clone https://github.com/joowani/binarytree.git
~$ python binarytree/setup.py install

You may need to use sudo depending on your environment setup.

Getting Started

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 = None

Generate and pretty-print all kinds of binary trees:

from binarytree import tree, bst, heap, pprint

# Generate a random binary tree and return its root
my_tree = tree(height=5, balanced=False)

# Generate a random BST and return its root
my_bst = bst(height=5)

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

# Pretty print the trees in stdout
pprint(my_tree)
pprint(my_bst)
pprint(my_heap)

List representations are supported as well:

from heapq import heapify
from binarytree import tree, convert, pprint

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
pprint(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'])

Import the Node class and build your own trees:

from binarytree import Node, pprint

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

pprint(root)

If the default Node class does not meet your requirements, you can define and use your own custom node specification:

from binarytree import Node, setup, tree, pprint

# 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 setup in the beginning to apply your specification
setup(
    node_init_func=lambda v: MyNode(v, 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()
pprint(my_custom_tree)