Binary Search tree implementation in python
"""
17
/ \
/ \
/ \
4 20
/\ /\
/ \ / \
/ \ / \
1 9 18 23
\
\
\
34
"""
# Binary Search Tree implementation in Python
class BinaryTreeNode():
def __init__(self, data):
self.data = data
self.left = None
self.right = None
def add_child(self, data):
if data == self.data: # check if the of new data exist already in the tree, if yes don't add
return
if data < self.data:
# Add to left subtree
if self.left:
self.left.add_child(data) # Recursively call the add_child method to add the data to an appropriate place
else:
self.left = BinaryTreeNode(data)
else:
# Add to right subtree
if self.right:
self.right.add_child(data) # Recursively call the add_child method to add the data to an appropriate place
else:
self.right = BinaryTreeNode(data)
# Visit Left subtree, then Root node and finaly Right subtree
def in_order_traversal(self): # Left - Root - Right
elements = []
# Getting all elements of the Left Subtree
if self.left:
elements += self.left.in_order_traversal() # Recursively get all the elements of the left subtree and add them into the list
elements.append(self.data) # Adding the root node to the list
# Getting all elements of the Right Subtree
if self.right:
elements += self.right.in_order_traversal() # Recursively get all the elements of the right subtree and add them into the list
return elements
# Get all elements from the Root node then the left subtree and finanally the Right subtree
def pre_order_traversal(self): # Root - Left - Right
elements = []
elements.append(self.data)
if self.left:
elements += self.left.pre_order_traversal() # Recursively get all the elements of the left subtree and add them into the list
if self.right:
elements += self.right.pre_order_traversal() # Recursively get all the elements of the right subtree and add them into the list
return elements # get the Root node element
# Get all elements from the Right subtree then the left subtree and finally the Root node
def post_order_traversal(self):
elements = []
if self.left:
elements += self.left.post_order_traversal() # Recursively get all the elements of the left subtree and add them into the list
if self.right:
elements += self.right.post_order_traversal() # Recursively get all the elements of the right subtree and add them into the list
elements.append(self.data) # Get the Root node element
return elements
def search_element(self, elem): # complexity of log n O(log n)
if self.data == elem:
return True
elif elem < self.data:
# This means if present, element would be on the left
if self.left:
return self.left.search_element(elem)
else:
return False
else:
# This means if present, element would be on the right
if self.right:
return self.right.search_element(elem)
else:
return False
def sum_of_all_elements_in_tree(self):
return sum(self.in_order_traversal())
def max_element_in_tree(self):
return max(self.in_order_traversal())
def min_element_in_tree(self):
return min(self.in_order_traversal())
# Tree Builder helper method
def build_binary_tree(lst_elem: list):
if len(lst_elem) >1:
root_node = BinaryTreeNode(lst_elem[0])
for i in lst_elem[1:]:
root_node.add_child(i)
#root_node.search_element(20)
#print(root_node.in_order_traversal())
return root_node
else:
return print("Insufficient number of elements")
if __name__ == '__main__':
mt = build_binary_tree([17, -5, 4, 1, 20, 9, -1, 23, 18, 0, 34])
print("In Order Traversal", mt.in_order_traversal())
print("Post Order Traversal", mt.post_order_traversal())
print("Pre Order Traversal", mt.pre_order_traversal())
print(mt.search_element(20))
print("Sum of all elemnts in tree", mt.sum_of_all_elements_in_tree())
print("Max element in tree is", mt.max_element_in_tree())
print("Min element in tree is", mt.min_element_in_tree())Source: raw.githubusercontent.com
binary search tree iterator python
"""
17
/ \
/ \
/ \
4 20
/\ /\
/ \ / \
/ \ / \
1 9 18 23
\
\
\
34
"""
# Binary Search Tree implementation in Python
class BinaryTreeNode():
def __init__(self, data):
self.data = data
self.left = None
self.right = None
def add_child(self, data):
if data == self.data: # check if the of new data exist already in the tree, if yes don't add
return
if data < self.data:
# Add to left subtree
if self.left:
self.left.add_child(data) # Recursively call the add_child method to add the data to an appropriate place
else:
self.left = BinaryTreeNode(data)
else:
# Add to right subtree
if self.right:
self.right.add_child(data) # Recursively call the add_child method to add the data to an appropriate place
else:
self.right = BinaryTreeNode(data)
# Visit Left subtree, then Root node and finaly Right subtree
def in_order_traversal(self): # Left - Root - Right
elements = []
# Getting all elements of the Left Subtree
if self.left:
elements += self.left.in_order_traversal() # Recursively get all the elements of the left subtree and add them into the list
elements.append(self.data) # Adding the root node to the list
# Getting all elements of the Right Subtree
if self.right:
elements += self.right.in_order_traversal() # Recursively get all the elements of the right subtree and add them into the list
return elements
# Get all elements from the Root node then the left subtree and finanally the Right subtree
def pre_order_traversal(self): # Root - Left - Right
elements = []
elements.append(self.data)
if self.left:
elements += self.left.pre_order_traversal() # Recursively get all the elements of the left subtree and add them into the list
if self.right:
elements += self.right.pre_order_traversal() # Recursively get all the elements of the right subtree and add them into the list
return elements # get the Root node element
# Get all elements from the Right subtree then the left subtree and finally the Root node
def post_order_traversal(self):
elements = []
if self.left:
elements += self.left.post_order_traversal() # Recursively get all the elements of the left subtree and add them into the list
if self.right:
elements += self.right.post_order_traversal() # Recursively get all the elements of the right subtree and add them into the list
elements.append(self.data) # Get the Root node element
return elements
def search_element(self, elem): # complexity of log n O(log n)
if self.data == elem:
return True
elif elem < self.data:
# This means if present, element would be on the left
if self.left:
return self.left.search_element(elem)
else:
return False
else:
# This means if present, element would be on the right
if self.right:
return self.right.search_element(elem)
else:
return False
def sum_of_all_elements_in_tree(self):
return sum(self.in_order_traversal())
def max_element_in_tree(self):
return max(self.in_order_traversal())
def min_element_in_tree(self):
return min(self.in_order_traversal())
# Tree Builder helper method
def build_binary_tree(lst_elem: list):
if len(lst_elem) >1:
root_node = BinaryTreeNode(lst_elem[0])
for i in lst_elem[1:]:
root_node.add_child(i)
#root_node.search_element(20)
#print(root_node.in_order_traversal())
return root_node
else:
return print("Insufficient number of elements")
if __name__ == '__main__':
mt = build_binary_tree([17, -5, 4, 1, 20, 9, -1, 23, 18, 0, 34])
print("In Order Traversal", mt.in_order_traversal())
print("Post Order Traversal", mt.post_order_traversal())
print("Pre Order Traversal", mt.pre_order_traversal())
print(mt.search_element(20))
print("Sum of all elemnts in tree", mt.sum_of_all_elements_in_tree())
print("Max element in tree is", mt.max_element_in_tree())
print("Min element in tree is", mt.min_element_in_tree())