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Writing a Binary Search Tree in Python with Examples

What is a Binary Search Tree?

A binary search tree, or BST for short, is a tree whose nodes store a key that is greater than all of their left child nodes and less than all of their right child nodes.

Binary trees are useful for storing data in an organized manner so that it can be quickly retrieved, inserted, updated, and deleted. This arrangement of nodes lets each comparison skip about half of the rest of the tree, so the entire search is lightning fast.

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To be precise, binary search trees provide an average Big-O complexity of O(log(n)) for search, insert, update, and delete operations. Log(n) is much faster than the linear O(n) time required to find elements in an unsorted array. Many popular production databases such as PostgreSQL and MySQL use binary trees under the hood to speed up CRUD operations.

Binary Search Tree

Pros of a BST

  • When balanced, a BST provides lightning-fast O(log(n)) insertions, deletions, and lookups.
  • Binary search trees are pretty simple. An ordinary BST, unlike a balanced tree like a red-black tree, requires very little code to get running.

Cons of a BST

  • Slow for a brute-force search. If you need to iterate over each node, you might have more success with an array.
  • When the tree becomes unbalanced, all fast O(log(n)) operations quickly degrade to O(n).
  • Since pointers to whole objects are typically involved, a BST can require quite a bit more memory than an array, although this depends on the implementation.

Implementing a BST in Python

Step 1 – BSTNode Class

Our implementation won’t use a Tree class, but instead just a Node class. Binary trees are really just a pointer to a root node that in turn connects to each child node, so we’ll run with that idea.

First, we create a constructor:

class BSTNode:     def __init__(self, val=None):         self.left = None         self.right = None         self.val = val
Code language: Python (python)

We’ll allow a value (key) to be provided, but if one isn’t provided we’ll just set it to None. We’ll also initialize both children of the new node to None.

Step 2 – Insert

We need a way to insert new data into the tree. Inserting a new node should append it as a leaf node in the proper spot.

10 10 / \ Insert 5 / \ 2 60 ---------> 2 60 / \ / \ 1 3 1 3 \ 5

The insert method is as follows:

def insert(self, val): if not self.val:         self.val = val         return     if self.val == val:         return     if val < self.val:         if self.left:             self.left.insert(val)             return         self.left = BSTNode(val)         return     if self.right:         self.right.insert(val)         return     self.right = BSTNode(val)
Code language: Python (python)

If the node doesn’t yet have a value, we can just set the given value and return. If we ever try to insert a value that also exists, we can also simply return as this can be considered a noop. If the given value is less than our node’s value and we already have a left child then we recursively call insert on our left child. If we don’t have a left child yet then we just make the given value our new left child. We can do the same (but inverted) for our right side.

Step 3 – Get Min and Get Max

def get_min(self): current = self     while current.left is not None:         current = current.left     return current.val def get_max(self):     current = self     while current.right is not None:         current = current.right     return current.val
Code language: Python (python)

getMin and getMax are useful helper functions, and they’re easy to write! They are simple recursive functions that traverse the edges of the tree to find the smallest or largest values stored therein.

Step 4 – Delete

def delete(self, val):     if self == None:         return self     if val < self.val:         self.left = self.left.delete(val)         return self     if val > self.val:         self.right = self.right.delete(val)         return self     if self.right == None:         return self.left     if self.left == None:         return self.right     min_larger_node = self.right     while min_larger_node.left:         min_larger_node = min_larger_node.left     self.val = min_larger_node.val     self.right = self.right.delete(min_larger_node.val)     return selfdef delete(self, val):     if self == None:         return self     if val < self.val:         if self.left:             self.left = self.left.delete(val)         return self     if val > self.val:         if self.right:             self.right = self.right.delete(val)         return self     if self.right == None:         return self.left     if self.left == None:         return self.right     min_larger_node = self.right     while min_larger_node.left:         min_larger_node = min_larger_node.left     self.val = min_larger_node.val     self.right = self.right.delete(min_larger_node.val)     return selfdef delete(self, val):     if self == None:         return self     if val < self.val:         self.left = self.left.delete(val)         return self     if val > self.val:         self.right = self.right.delete(val)         return self     if self.right == None:         return self.left     if self.left == None:         return self.right     min_larger_node = self.right     while min_larger_node.left:         min_larger_node = min_larger_node.left     self.val = min_larger_node.val     self.right = self.right.delete(min_larger_node.val)     return self
Code language: Python (python)

The delete operation is one of the more complex ones. It is a recursive function as well, but it also returns the new state of the given node after performing the delete operation. This allows a parent whose child has been deleted to properly set it’s left or right data member to None.

Step 5 – Exists

The exists function is another simple recursive function that returns True or False depending on whether a given value already exists in the tree.

def exists(self, val):     if val == self.val:         return True     if val < self.val:         if self.left == None:             return False         return self.left.exists(val)     if self.right == None:         return False     return self.right.exists(val)
Code language: Python (python)

Step 6 – Inorder

It’s useful to be able to print out the tree in a readable format. The inorder method print’s the values in the tree in the order of their keys.

def inorder(self, vals):     if self.left is not None:         self.left.inorder(vals)     if self.val is not None:         vals.append(self.val)     if self.right is not None:         self.right.inorder(vals)     return vals
Code language: Python (python)

Step 7 – Preorder

def preorder(self, vals):     if self.val is not None:         vals.append(self.val)     if self.left is not None:         self.left.preorder(vals)     if self.right is not None:         self.right.preorder(vals)     return vals
Code language: Python (python)

Step 8 – Postorder

def postorder(self, vals):     if self.left is not None:         self.left.postorder(vals)     if self.right is not None:         self.right.postorder(vals)     if self.val is not None:         vals.append(self.val)     return vals
Code language: Python (python)

Using the BST

def main():     nums = [126181921113542418]     bst = BSTNode()     for num in nums:         bst.insert(num)     print("preorder:")     print(bst.preorder([]))     print("#")     print("postorder:")     print(bst.postorder([]))     print("#")     print("inorder:")     print(bst.inorder([]))     print("#")     nums = [2620]     print("deleting " + str(nums))     for num in nums:         bst.delete(num)     print("#")     print("4 exists:")     print(bst.exists(4))     print("2 exists:")     print(bst.exists(2))     print("12 exists:")     print(bst.exists(12))     print("18 exists:")     print(bst.exists(18))
Code language: Python (python)

Full Binary Search Tree in Python

class BSTNode:     def __init__(self, val=None):         self.left = None         self.right = None         self.val = val     def insert(self, val):         if not self.val:             self.val = val             return         if self.val == val:             return         if val < self.val:             if self.left:                 self.left.insert(val)                 return             self.left = BSTNode(val)             return         if self.right:             self.right.insert(val)             return         self.right = BSTNode(val)     def get_min(self):         current = self         while current.left is not None:             current = current.left         return current.val     def get_max(self):         current = self         while current.right is not None:             current = current.right         return current.val     def delete(self, val):         if self == None:             return self         if val < self.val:             if self.left:                 self.left = self.left.delete(val)             return self         if val > self.val:             if self.right:                 self.right = self.right.delete(val)             return self         if self.right == None:             return self.left         if self.left == None:             return self.right         min_larger_node = self.right         while min_larger_node.left:             min_larger_node = min_larger_node.left         self.val = min_larger_node.val         self.right = self.right.delete(min_larger_node.val)         return self     def exists(self, val):         if val == self.val:             return True         if val < self.val:             if self.left == None:                 return False             return self.left.exists(val)         if self.right == None:             return False         return self.right.exists(val)     def preorder(self, vals):         if self.val is not None:             vals.append(self.val)         if self.left is not None:             self.left.preorder(vals)         if self.right is not None:             self.right.preorder(vals)         return vals     def inorder(self, vals):         if self.left is not None:             self.left.inorder(vals)         if self.val is not None:             vals.append(self.val)         if self.right is not None:             self.right.inorder(vals)         return vals     def postorder(self, vals):         if self.left is not None:             self.left.postorder(vals)         if self.right is not None:             self.right.postorder(vals)         if self.val is not None:             vals.append(self.val)         return vals
Code language: Python (python)

Where would you use a binary search tree in real life?

There are many applications of binary search trees in real life, and one of the most common use-cases is in storing indexes and keys in a database.

For example, in MySQL or PostgresQL when you create a primary key column, what you’re really doing is creating a binary tree where the keys are the values of the column, and those nodes point to database rows. This lets the application easily search database rows by providing a key. For example, getting a user record by the email primary key.

There are many applications of binary search trees in real life, and one of the most common use cases is storing indexes and keys in a database.

For example, when you create a primary key column in MySQL or PostgresQL, you create a binary tree where the keys are the values of the column and the nodes point to database rows. This allows the application to easily search for database rows by specifying a key, for example, to find a user record using the email primary key.

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Other common uses include:

  • Pathfinding algorithms in videogames (A*) use BSTs
  • File compression using a Huffman encoding scheme uses a binary search tree
  • Rendering calculations – Doom (1993) was famously the first game to use a BST
  • Compilers for low-level coding languages parse syntax using a BST
  • Almost every database in existence uses BSTs for key lookups

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