Develop Reference

Binary search tree lazy deletion Python

I've created a binary search tree class and I'm trying to get a grasp on how "lazy deletion" works.
I've set a flag in order to show if a node has been removed. When I search for a value that I want to remove, self.removed would be marked as True. However, when I use the findValue method, it still says that the removed value is still there.
I've been doing some research on how lazy deletion works and all of them say that you need to set a flag and set it to True if the value is found. Is there anything else I would need to implement in order to get this to work? Or am I missing something?
Any help would be appreciated!
class Node:
def __init__(self, value):
self.value = value
self.left = None
self.right = None
self.removed = False
def setLeft(self, left):
self.left = left
def setRight(self, right):
self.right = right
def getLeft(self):
return self.left
def getRight(self):
return self.right
def getValue(self):
return self.value
class Tree:
def __init__(self):
self.root = None
def insertValue(self, value):
"""add a new node containing value to the tree """
if self.root is None:
temp = Node(value)
self.root = temp
return
self.recInsertValue(value, self.root)
def recInsertValue(self, value, ptr):
if value < ptr.value:
if ptr.getLeft() is None:
temp = Node(value)
ptr.left = temp
else:
self.recInsertValue(value, ptr.getLeft())
else:
if ptr.right is None:
temp = Node(value)
ptr.right = temp
else:
self.recInsertValue(value, ptr.right)
def findValue(self, value):
"""return true if there is a node containing value, false otherwise"""
ptr = self.root
while ptr is not None:
if ptr.value == value:
return True
if value < ptr.value:
ptr = ptr.getLeft()
else:
ptr = ptr.getRight()
return False
def removeValue(self, value):
ptr = self.root
while ptr is not None:
if ptr.value == value:
ptr.removed = True
return True
if value < ptr.value:
ptr = ptr.getLeft()
else:
ptr = ptr.getRight()
return False
def inOrder(self):
return self.recInOrder(self.root)
def recInOrder(self, ptr):
buffer = ""
if ptr is not None:
buffer += self.recInOrder(ptr.getLeft())
buffer += str(ptr.getValue()) + " "
buffer += self.recInOrder(ptr.getRight())
return buffer
return ""

You should also modify the search method, otherwise there is no way to utilize the removed flag.
def findValue(self, value):
"""return true if there is a node containing value, false otherwise"""
ptr = self.root
while ptr is not None:
if ptr.value == value and not ptr.removed:
return True
if value < ptr.value:
ptr = ptr.getLeft()
else:
ptr = ptr.getRight()
return False
Also don't forget recInOrder:
def recInOrder(self, ptr):
buffer = ""
if ptr is not None:
buffer += self.recInOrder(ptr.getLeft())
buffer += str(ptr.getValue()) + " " if not ptr.removed else ""
buffer += self.recInOrder(ptr.getRight())
return buffer
return ""

Recursively Creating a Binary Tree Given a String Expression in Prefix Notation in Python

Representation of LinkedBinaryTree that should be outputted
import LinkedBinaryTree
def create_expression_tree(prefix_exp_str):
def create_expression_tree_helper(prefix_exp, start_pos):
start_pos += 1
op = ['+', '-', '*', '/']
elem = prefix_exp[start_pos]
if elem == ' ':
elem = prefix_exp[start_pos+1]
start_pos += 1
if elem not in op:
return LinkedBinaryTree.LinkedBinaryTree.Node(int(elem))
else:
left = create_expression_tree_helper(prefix_exp, start_pos)
right = create_expression_tree_helper(prefix_exp, start_pos+2)
return LinkedBinaryTree.LinkedBinaryTree.Node(elem, left, right)
tree = LinkedBinaryTree.LinkedBinaryTree(create_expression_tree_helper(prefix_exp_str, -1))
return tree
tree1 = create_expression_tree('* 2 + - 15 6 4')
for i in tree1.preorder():
print(i.data, end='')
I implemented my own binary tree class which is shown below. Preorder() is a generator for my LinkedBinaryTree that gives the values of the tree in prefix order. With this code, I'm outputting
*2+-151
but it should be outputting
*2+-1564 if the binary expression tree has been created correctly.
I'm aware that there is an issue with numbers greater than 1 digit, but I'm not sure how to fix it without compromising the runtime (ie. using slicing). Also I'm not sure why it is omitting some of the tokens. Any ideas? (The implementation must run in linear time and use no additional parameters in the helper function I've defined).
import ArrayQueue
class LinkedBinaryTree:
class Node:
def __init__(self, data, left=None, right=None):
self.data = data
self.parent = None
self.left = left
if (self.left is not None):
self.left.parent = self
self.right = right
if (self.right is not None):
self.right.parent = self
def __init__(self, root=None):
self.root = root
self.size = self.subtree_count(root)
def __len__(self):
return self.size
def is_empty(self):
return len(self) == 0
def subtree_count(self, root):
if (root is None):
return 0
else:
left_count = self.subtree_count(root.left)
right_count = self.subtree_count(root.right)
return 1 + left_count + right_count
def sum(self):
return self.subtree_sum(self.root)
def subtree_sum(self, root):
if (root is None):
return 0
else:
left_sum = self.subtree_sum(root.left)
right_sum = self.subtree_sum(root.right)
return root.data + left_sum + right_sum
def height(self):
return self.subtree_height(self.root)
def subtree_height(self, root):
if (root.left is None and root.right is None):
return 0
elif (root.left is None):
return 1 + self.subtree_height(root.right)
elif (root.right is None):
return 1 + self.subtree_height(root.left)
else:
left_height = self.subtree_height(root.left)
right_height = self.subtree_height(root.right)
return 1 + max(left_height, right_height)
def preorder(self):
yield from self.subtree_preorder(self.root)
def subtree_preorder(self, root):
if(root is None):
return
else:
yield root
yield from self.subtree_preorder(root.left)
yield from self.subtree_preorder(root.right)
def postorder(self):
yield from self.subtree_postorder(self.root)
def subtree_postorder(self, root):
if(root is None):
return
else:
yield from self.subtree_postorder(root.left)
yield from self.subtree_postorder(root.right)
yield root
def inorder(self):
yield from self.subtree_inorder(self.root)
def subtree_inorder(self, root):
if(root is None):
return
else:
yield from self.subtree_inorder(root.left)
yield root
yield from self.subtree_inorder(root.right)
def breadth_first(self):
if (self.is_empty()):
return
line = ArrayQueue.ArrayQueue()
line.enqueue(self.root)
while (line.is_empty() == False):
curr_node = line.dequeue()
yield curr_node
if (curr_node.left is not None):
line.enqueue(curr_node.left)
if (curr_node.right is not None):
line.enqueue(curr_node.right)
def __iter__(self):
for node in self.breadth_first():
yield node.data
I added the code here for LinkedBinaryTree class. The ArrayQueue class that is used in the implementation of the breadth traversal method is just a basic queue using a Python list as the underlying data structure.

The two issues with your code were:
you processed the characters one by one while several-digit numbers could be present (fixed by splitting the expression to a list)
you did not account for the fact that chained operator expressions could be longer that just your standard increment (fixed by adding a size property to Node
So here is the new Node class
class Node:
def __init__(self, data, left=None, right=None):
self.data = data
self.parent = None
self.left = left
if (self.left is not None):
self.left.parent = self
self.right = right
if (self.right is not None):
self.right.parent = self
#property
def size(self):
l = 1
if self.left is not None:
l += self.left.size
if self.right is not None:
l += self.right.size
return l
and here is the new tree creator
def create_expression_tree(prefix_exp_str):
expr_lst = prefix_exp_str.split(" ")
op = {'+': None, '-': None, '*': None, '/': None}
def create_expression_tree_helper(prefix_exp, start_pos):
start_pos += 1
elem = prefix_exp[start_pos]
node = None
if elem not in op:
node = LinkedBinaryTree.Node(int(elem))
else:
left = create_expression_tree_helper(prefix_exp, start_pos)
incr = left.size
right = create_expression_tree_helper(prefix_exp, start_pos+incr)
node = LinkedBinaryTree.Node(elem, left, right)
return node
tree = LinkedBinaryTree(create_expression_tree_helper(expr_lst, -1))
return tree
EDIT here is a version that does not require to modify the Node class
def create_expression_tree(prefix_exp_str):
expr_lst = prefix_exp_str.split(" ")
op = {'+': None, '-': None, '*': None, '/': None}
def create_expression_tree_helper(prefix_exp, start_pos):
start_pos += 1
elem = prefix_exp[start_pos]
node = None
size = 1
if elem not in op:
node = LinkedBinaryTree.Node(int(elem))
else:
left, left_size = create_expression_tree_helper(prefix_exp, start_pos)
right, right_size = create_expression_tree_helper(prefix_exp, start_pos+left_size)
node = LinkedBinaryTree.Node(elem, left, right)
size += left_size + right_size
return node, size
tree = LinkedBinaryTree(create_expression_tree_helper(expr_lst, -1)[0])
return tree

Return a string indicating what type of imbalance exists in a binary search tree

Having added a new node to an AVL. what kind of rotation do I need to fix it? I need to write a fuction that takes the binary tree and returns a string saying what type of imbalaced exist
class Node:
""" A node in a BST. It may have left and right subtrees """
def __init__(self, item, left=None, right=None):
self.item = item
self.left = left
self.right = right
class BST:
""" An implementation of a Binary Search Tree """
def __init__(self, lst=None):
self.root = None
if lst != None:
for x in lst:
self.add(x)
def recurse_add(self, ptr, item):
if ptr == None:
return Node(item)
elif item < ptr.item:
ptr.left = self.recurse_add(ptr.left, item)
elif item > ptr.item:
ptr.right = self.recurse_add(ptr.right, item)
return ptr
def add(self, item):
""" Add this item to its correct position on the tree """
self.root = self.recurse_add(self.root, item)
def r_height(self, ptr):
if ptr == None:
return 0
else:
return 1 + max(self.r_height(ptr.left), self.r_height(ptr.right))
def height(self):
return self.r_height(self.root)

How to count the leaves of a Binary Tree?

class Node:
def __init__(self, item, left = None, right = None):
self.item = item
self.left = left
self.right = right
class BST:
def __init__(self):
self.root = None
def recurse_add(self, ptr, item):
if ptr == None:
return Node(item)
elif item < ptr.item:
ptr.left = self.recurse_add(ptr.left, item)
elif item > ptr.item:
ptr.right = self.recurse_add(ptr.right, item)
return ptr
Here is my attempt:
def count_leaves(self):
ptr = self.root
counter = 0
if ptr.left is None and ptr.right is None:
counter += 1
if ptr.left:
counter += self.count_leaves()
if ptr.right:
counter += self.count_leaves()
return counter
I got a RecursionError, is there anyway that I could fix this?Can anyone explain to me how I would count the leaves of the binary tree?

You are always repeating from the self node when you are counting leaves.
def count_leaves(self):
ptr = self.root # reseting at the root
...
counter += self.count_leaves() # recurses from the top of the tree
Your secondary problem seems to be related to how you are adding nodes.
For example,
def recurse_add(self, ptr, item):
if prt == None:
# say this is false
ptr.left = self.recurse_add(ptr.left, item)
...
return ptr # The recursive call will return 'ptr.left'
So, basically ptr.left = ptr.left in the case that ptr != None
You need to iterate down. I generally implement all recursive methods onto the Node class rather than pass along a pointer in the Tree class.
class Node(object):
def __init__(self, item, left = None, right = None):
self.item = item
self.left = left
self.right = right
def is_leaf(self):
return self.right is None and self.left is None
def add(self, item):
if item <= self.item:
self.left = Node(item) if self.left is None else self.left.add(item)
elif item > self.item:
self.right = Node(item) if self.right is None else self.right.add(item)
return self
def count_leaves(self):
counter = 0
if self.is_leaf():
counter += 1
if self.left is not None:
counter += self.left.count_leaves()
if self.right is not None:
counter += self.right.count_leaves()
return counter
Now, just delegate the methods to the root from the tree, if you have a root.
class BST:
def __init__(self):
self.root = None
def is_empty(self):
return self.root is None
def add(self, item):
if self.is_empty():
self.root = Node(item)
else:
self.root.add(item)
def count_leaves(self):
return 0 if self.is_empty() else self.root.count_leaves()

How to implement a binary search tree in Python?

This is what I've got so far but it is not working:
class Node:
rChild,lChild,data = None,None,None
def __init__(self,key):
self.rChild = None
self.lChild = None
self.data = key
class Tree:
root,size = None,0
def __init__(self):
self.root = None
self.size = 0
def insert(self,node,someNumber):
if node is None:
node = Node(someNumber)
else:
if node.data > someNumber:
self.insert(node.rchild,someNumber)
else:
self.insert(node.rchild, someNumber)
return
def main():
t = Tree()
t.root = Node(4)
t.root.rchild = Node(5)
print t.root.data #this works
print t.root.rchild.data #this works too
t = Tree()
t.insert(t.root,4)
t.insert(t.root,5)
print t.root.data #this fails
print t.root.rchild.data #this fails too
if __name__ == '__main__':
main()

Here is a quick example of a binary insert:
class Node:
def __init__(self, val):
self.l_child = None
self.r_child = None
self.data = val
def binary_insert(root, node):
if root is None:
root = node
else:
if root.data > node.data:
if root.l_child is None:
root.l_child = node
else:
binary_insert(root.l_child, node)
else:
if root.r_child is None:
root.r_child = node
else:
binary_insert(root.r_child, node)
def in_order_print(root):
if not root:
return
in_order_print(root.l_child)
print root.data
in_order_print(root.r_child)
def pre_order_print(root):
if not root:
return
print root.data
pre_order_print(root.l_child)
pre_order_print(root.r_child)
r = Node(3)
binary_insert(r, Node(7))
binary_insert(r, Node(1))
binary_insert(r, Node(5))
3
/ \
1 7
/
5
print "in order:"
in_order_print(r)
print "pre order"
pre_order_print(r)
in order:
1
3
5
7
pre order
3
1
7
5

class Node:
rChild,lChild,data = None,None,None
This is wrong - it makes your variables class variables - that is, every instance of Node uses the same values (changing rChild of any node changes it for all nodes!). This is clearly not what you want; try
class Node:
def __init__(self, key):
self.rChild = None
self.lChild = None
self.data = key
now each node has its own set of variables. The same applies to your definition of Tree,
class Tree:
root,size = None,0 # <- lose this line!
def __init__(self):
self.root = None
self.size = 0
Further, each class should be a "new-style" class derived from the "object" class and should chain back to object.__init__():
class Node(object):
def __init__(self, data, rChild=None, lChild=None):
super(Node,self).__init__()
self.data = data
self.rChild = rChild
self.lChild = lChild
class Tree(object):
def __init__(self):
super(Tree,self).__init__()
self.root = None
self.size = 0
Also, main() is indented too far - as shown, it is a method of Tree which is uncallable because it does not accept a self argument.
Also, you are modifying the object's data directly (t.root = Node(4)) which kind of destroys encapsulation (the whole point of having classes in the first place); you should be doing something more like
def main():
t = Tree()
t.add(4) # <- let the tree create a data Node and insert it
t.add(5)

class Node:
rChild,lChild,parent,data = None,None,None,0
def __init__(self,key):
self.rChild = None
self.lChild = None
self.parent = None
self.data = key
class Tree:
root,size = None,0
def __init__(self):
self.root = None
self.size = 0
def insert(self,someNumber):
self.size = self.size+1
if self.root is None:
self.root = Node(someNumber)
else:
self.insertWithNode(self.root, someNumber)
def insertWithNode(self,node,someNumber):
if node.lChild is None and node.rChild is None:#external node
if someNumber > node.data:
newNode = Node(someNumber)
node.rChild = newNode
newNode.parent = node
else:
newNode = Node(someNumber)
node.lChild = newNode
newNode.parent = node
else: #not external
if someNumber > node.data:
if node.rChild is not None:
self.insertWithNode(node.rChild, someNumber)
else: #if empty node
newNode = Node(someNumber)
node.rChild = newNode
newNode.parent = node
else:
if node.lChild is not None:
self.insertWithNode(node.lChild, someNumber)
else:
newNode = Node(someNumber)
node.lChild = newNode
newNode.parent = node
def printTree(self,someNode):
if someNode is None:
pass
else:
self.printTree(someNode.lChild)
print someNode.data
self.printTree(someNode.rChild)
def main():
t = Tree()
t.insert(5)
t.insert(3)
t.insert(7)
t.insert(4)
t.insert(2)
t.insert(1)
t.insert(6)
t.printTree(t.root)
if __name__ == '__main__':
main()
My solution.

class BST:
def __init__(self, val=None):
self.left = None
self.right = None
self.val = val
def __str__(self):
return "[%s, %s, %s]" % (self.left, str(self.val), self.right)
def isEmpty(self):
return self.left == self.right == self.val == None
def insert(self, val):
if self.isEmpty():
self.val = val
elif val < self.val:
if self.left is None:
self.left = BST(val)
else:
self.left.insert(val)
else:
if self.right is None:
self.right = BST(val)
else:
self.right.insert(val)
a = BST(1)
a.insert(2)
a.insert(3)
a.insert(0)
print a

The Op's Tree.insert method qualifies for the "Gross Misnomer of the Week" award -- it doesn't insert anything. It creates a node which is not attached to any other node (not that there are any nodes to attach it to) and then the created node is trashed when the method returns.
For the edification of #Hugh Bothwell:
>>> class Foo(object):
... bar = None
...
>>> a = Foo()
>>> b = Foo()
>>> a.bar
>>> a.bar = 42
>>> b.bar
>>> b.bar = 666
>>> a.bar
42
>>> b.bar
666
>>>

The accepted answer neglects to set a parent attribute for each node inserted, without which one cannot implement a successor method which finds the successor in an in-order tree walk in O(h) time, where h is the height of the tree (as opposed to the O(n) time needed for the walk).
Here is an implementation based on the pseudocode given in Cormen et al., Introduction to Algorithms, including assignment of a parent attribute and a successor method:
class Node(object):
def __init__(self, key):
self.key = key
self.left = None
self.right = None
self.parent = None
class Tree(object):
def __init__(self, root=None):
self.root = root
def insert(self, z):
y = None
x = self.root
while x is not None:
y = x
if z.key < x.key:
x = x.left
else:
x = x.right
z.parent = y
if y is None:
self.root = z # Tree was empty
elif z.key < y.key:
y.left = z
else:
y.right = z
#staticmethod
def minimum(x):
while x.left is not None:
x = x.left
return x
#staticmethod
def successor(x):
if x.right is not None:
return Tree.minimum(x.right)
y = x.parent
while y is not None and x == y.right:
x = y
y = y.parent
return y
Here are some tests to show that the tree behaves as expected for the example given by DTing:
import pytest
#pytest.fixture
def tree():
t = Tree()
t.insert(Node(3))
t.insert(Node(1))
t.insert(Node(7))
t.insert(Node(5))
return t
def test_tree_insert(tree):
assert tree.root.key == 3
assert tree.root.left.key == 1
assert tree.root.right.key == 7
assert tree.root.right.left.key == 5
def test_tree_successor(tree):
assert Tree.successor(tree.root.left).key == 3
assert Tree.successor(tree.root.right.left).key == 7
if __name__ == "__main__":
pytest.main([__file__])

Just something to help you to start on.
A (simple idea of) binary tree search would be quite likely be implement in python according the lines:
def search(node, key):
if node is None: return None # key not found
if key< node.key: return search(node.left, key)
elif key> node.key: return search(node.right, key)
else: return node.value # found key
Now you just need to implement the scaffolding (tree creation and value inserts) and you are done.

I find the solutions a bit clumsy on the insert part. You could return the root reference and simplify it a bit:
def binary_insert(root, node):
if root is None:
return node
if root.data > node.data:
root.l_child = binary_insert(root.l_child, node)
else:
root.r_child = binary_insert(root.r_child, node)
return root

its easy to implement a BST using two classes, 1. Node and 2. Tree
Tree class will be just for user interface, and actual methods will be implemented in Node class.
class Node():
def __init__(self,val):
self.value = val
self.left = None
self.right = None
def _insert(self,data):
if data == self.value:
return False
elif data < self.value:
if self.left:
return self.left._insert(data)
else:
self.left = Node(data)
return True
else:
if self.right:
return self.right._insert(data)
else:
self.right = Node(data)
return True
def _inorder(self):
if self:
if self.left:
self.left._inorder()
print(self.value)
if self.right:
self.right._inorder()
class Tree():
def __init__(self):
self.root = None
def insert(self,data):
if self.root:
return self.root._insert(data)
else:
self.root = Node(data)
return True
def inorder(self):
if self.root is not None:
return self.root._inorder()
else:
return False
if __name__=="__main__":
a = Tree()
a.insert(16)
a.insert(8)
a.insert(24)
a.insert(6)
a.insert(12)
a.insert(19)
a.insert(29)
a.inorder()
Inorder function for checking whether BST is properly implemented.

Another Python BST with sort key (defaulting to value)
LEFT = 0
RIGHT = 1
VALUE = 2
SORT_KEY = -1
class BinarySearchTree(object):
def __init__(self, sort_key=None):
self._root = []
self._sort_key = sort_key
self._len = 0
def insert(self, val):
if self._sort_key is None:
sort_key = val // if no sort key, sort key is value
else:
sort_key = self._sort_key(val)
node = self._root
while node:
if sort_key < node[_SORT_KEY]:
node = node[LEFT]
else:
node = node[RIGHT]
if sort_key is val:
node[:] = [[], [], val]
else:
node[:] = [[], [], val, sort_key]
self._len += 1
def minimum(self):
return self._extreme_node(LEFT)[VALUE]
def maximum(self):
return self._extreme_node(RIGHT)[VALUE]
def find(self, sort_key):
return self._find(sort_key)[VALUE]
def _extreme_node(self, side):
if not self._root:
raise IndexError('Empty')
node = self._root
while node[side]:
node = node[side]
return node
def _find(self, sort_key):
node = self._root
while node:
node_key = node[SORT_KEY]
if sort_key < node_key:
node = node[LEFT]
elif sort_key > node_key:
node = node[RIGHT]
else:
return node
raise KeyError("%r not found" % sort_key)

Here is a compact, object oriented, recursive implementation:
class BTreeNode(object):
def __init__(self, data):
self.data = data
self.rChild = None
self.lChild = None
def __str__(self):
return (self.lChild.__str__() + '<-' if self.lChild != None else '') + self.data.__str__() + ('->' + self.rChild.__str__() if self.rChild != None else '')
def insert(self, btreeNode):
if self.data > btreeNode.data: #insert left
if self.lChild == None:
self.lChild = btreeNode
else:
self.lChild.insert(btreeNode)
else: #insert right
if self.rChild == None:
self.rChild = btreeNode
else:
self.rChild.insert(btreeNode)
def main():
btreeRoot = BTreeNode(5)
print 'inserted %s:' %5, btreeRoot
btreeRoot.insert(BTreeNode(7))
print 'inserted %s:' %7, btreeRoot
btreeRoot.insert(BTreeNode(3))
print 'inserted %s:' %3, btreeRoot
btreeRoot.insert(BTreeNode(1))
print 'inserted %s:' %1, btreeRoot
btreeRoot.insert(BTreeNode(2))
print 'inserted %s:' %2, btreeRoot
btreeRoot.insert(BTreeNode(4))
print 'inserted %s:' %4, btreeRoot
btreeRoot.insert(BTreeNode(6))
print 'inserted %s:' %6, btreeRoot
The output of the above main() is:
inserted 5: 5
inserted 7: 5->7
inserted 3: 3<-5->7
inserted 1: 1<-3<-5->7
inserted 2: 1->2<-3<-5->7
inserted 4: 1->2<-3->4<-5->7
inserted 6: 1->2<-3->4<-5->6<-7

Here is a working solution.
class BST:
def __init__(self,data):
self.root = data
self.left = None
self.right = None
def insert(self,data):
if self.root == None:
self.root = BST(data)
elif data > self.root:
if self.right == None:
self.right = BST(data)
else:
self.right.insert(data)
elif data < self.root:
if self.left == None:
self.left = BST(data)
else:
self.left.insert(data)
def inordertraversal(self):
if self.left != None:
self.left.inordertraversal()
print (self.root),
if self.right != None:
self.right.inordertraversal()
t = BST(4)
t.insert(1)
t.insert(7)
t.insert(3)
t.insert(6)
t.insert(2)
t.insert(5)
t.inordertraversal()

A simple, recursive method with only 1 function and using an array of values:
class TreeNode(object):
def __init__(self, value: int, left=None, right=None):
super().__init__()
self.value = value
self.left = left
self.right = right
def __str__(self):
return str(self.value)
def create_node(values, lower, upper) -> TreeNode:
if lower > upper:
return None
index = (lower + upper) // 2
value = values[index]
node = TreeNode(value=value)
node.left = create_node(values, lower, index - 1)
node.right = create_node(values, index + 1, upper)
return node
def print_bst(node: TreeNode):
if node:
# Simple pre-order traversal when printing the tree
print("node: {}".format(node))
print_bst(node.left)
print_bst(node.right)
if __name__ == '__main__':
vals = [0, 1, 2, 3, 4, 5, 6]
bst = create_node(vals, lower=0, upper=len(vals) - 1)
print_bst(bst)
As you can see, we really only need 1 method, which is recursive: create_node. We pass in the full values array in each create_node method call, however, we update the lower and upper index values every time that we make the recursive call.
Then, using the lower and upper index values, we calculate the index value of the current node and capture it in value. This value is the value for the current node, which we use to create a node.
From there, we set the values of left and right by recursively calling the function, until we reach the end state of the recursion call when lower is greater than upper.
Important: we update the value of upper when creating the left side of the tree. Conversely, we update the value of lower when creating the right side of the tree.
Hopefully this helps!

The following code is basic on #DTing‘s answer and what I learn from class, which uses a while loop to insert (indicated in the code).
class Node:
def __init__(self, val):
self.l_child = None
self.r_child = None
self.data = val
def binary_insert(root, node):
y = None
x = root
z = node
#while loop here
while x is not None:
y = x
if z.data < x.data:
x = x.l_child
else:
x = x.r_child
z.parent = y
if y == None:
root = z
elif z.data < y.data:
y.l_child = z
else:
y.r_child = z
def in_order_print(root):
if not root:
return
in_order_print(root.l_child)
print(root.data)
in_order_print(root.r_child)
r = Node(3)
binary_insert(r, Node(7))
binary_insert(r, Node(1))
binary_insert(r, Node(5))
in_order_print(r)

The problem, or at least one problem with your code is here:-
def insert(self,node,someNumber):
if node is None:
node = Node(someNumber)
else:
if node.data > someNumber:
self.insert(node.rchild,someNumber)
else:
self.insert(node.rchild, someNumber)
return
You see the statement "if node.data > someNumber:" and the associated "else:" statement both have the same code after them. i.e you do the same thing whether the if statement is true or false.
I'd suggest you probably intended to do different things here, perhaps one of these should say self.insert(node.lchild, someNumber) ?

Another Python BST solution
class Node(object):
def __init__(self, value):
self.left_node = None
self.right_node = None
self.value = value
def __str__(self):
return "[%s, %s, %s]" % (self.left_node, self.value, self.right_node)
def insertValue(self, new_value):
"""
1. if current Node doesnt have value then assign to self
2. new_value lower than current Node's value then go left
2. new_value greater than current Node's value then go right
:return:
"""
if self.value:
if new_value < self.value:
# add to left
if self.left_node is None: # reached start add value to start
self.left_node = Node(new_value)
else:
self.left_node.insertValue(new_value) # search
elif new_value > self.value:
# add to right
if self.right_node is None: # reached end add value to end
self.right_node = Node(new_value)
else:
self.right_node.insertValue(new_value) # search
else:
self.value = new_value
def findValue(self, value_to_find):
"""
1. value_to_find is equal to current Node's value then found
2. if value_to_find is lower than Node's value then go to left
3. if value_to_find is greater than Node's value then go to right
"""
if value_to_find == self.value:
return "Found"
elif value_to_find < self.value and self.left_node:
return self.left_node.findValue(value_to_find)
elif value_to_find > self.value and self.right_node:
return self.right_node.findValue(value_to_find)
return "Not Found"
def printTree(self):
"""
Nodes will be in sequence
1. Print LHS items
2. Print value of node
3. Print RHS items
"""
if self.left_node:
self.left_node.printTree()
print(self.value),
if self.right_node:
self.right_node.printTree()
def isEmpty(self):
return self.left_node == self.right_node == self.value == None
def main():
root_node = Node(12)
root_node.insertValue(6)
root_node.insertValue(3)
root_node.insertValue(7)
# should return 3 6 7 12
root_node.printTree()
# should return found
root_node.findValue(7)
# should return found
root_node.findValue(3)
# should return Not found
root_node.findValue(24)
if __name__ == '__main__':
main()

def BinaryST(list1,key):
start = 0
end = len(list1)
print("Length of List: ",end)
for i in range(end):
for j in range(0, end-i-1):
if(list1[j] > list1[j+1]):
temp = list1[j]
list1[j] = list1[j+1]
list1[j+1] = temp
print("Order List: ",list1)
mid = int((start+end)/2)
print("Mid Index: ",mid)
if(key == list1[mid]):
print(key," is on ",mid," Index")
elif(key > list1[mid]):
for rindex in range(mid+1,end):
if(key == list1[rindex]):
print(key," is on ",rindex," Index")
break
elif(rindex == end-1):
print("Given key: ",key," is not in List")
break
else:
continue
elif(key < list1[mid]):
for lindex in range(0,mid):
if(key == list1[lindex]):
print(key," is on ",lindex," Index")
break
elif(lindex == mid-1):
print("Given key: ",key," is not in List")
break
else:
continue
size = int(input("Enter Size of List: "))
list1 = []
for e in range(size):
ele = int(input("Enter Element in List: "))
list1.append(ele)
key = int(input("\nEnter Key for Search: "))
print("\nUnorder List: ",list1)
BinaryST(list1,key)

class TreeNode:
def __init__(self, value):
self.value = value
self.left = None
self.right = None
class BinaryTree:
def __init__(self, root=None):
self.root = root
def add_node(self, node, value):
"""
Node points to the left of value if node > value; right otherwise,
BST cannot have duplicate values
"""
if node is not None:
if value < node.value:
if node.left is None:
node.left = TreeNode(value)
else:
self.add_node(node.left, value)
else:
if node.right is None:
node.right = TreeNode(value)
else:
self.add_node(node.right, value)
else:
self.root = TreeNode(value)
def search(self, value):
"""
Value will be to the left of node if node > value; right otherwise.
"""
node = self.root
while node is not None:
if node.value == value:
return True # node.value
if node.value > value:
node = node.left
else:
node = node.right
return False
def traverse_inorder(self, node):
"""
Traverse the left subtree of a node as much as possible, then traverse
the right subtree, followed by the parent/root node.
"""
if node is not None:
self.traverse_inorder(node.left)
print(node.value)
self.traverse_inorder(node.right)
def main():
binary_tree = BinaryTree()
binary_tree.add_node(binary_tree.root, 200)
binary_tree.add_node(binary_tree.root, 300)
binary_tree.add_node(binary_tree.root, 100)
binary_tree.add_node(binary_tree.root, 30)
binary_tree.traverse_inorder(binary_tree.root)
print(binary_tree.search(200))
if __name__ == '__main__':
main()