Binary search trees organize data hierarchically, with each node having at most two children. The left child contains a value smaller than the parent and the right child contains a larger value. Searching is efficient as half of remaining nodes can be eliminated at each step by comparing the search value to the current node. Traversals such as inorder give elements in sorted order. Insertion and deletion are flexible but trees can become imbalanced affecting performance.

1. Binary Search Tree In Python
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2. 1
Introduction to Binary Search Tree
• A binary search tree is a data structure that organizes data in a
hierarchical manner.
• It is a type of binary tree where each node has at most two children.
• The left child of a node contains a value smaller than the parent,
while the right child contains a value greater than the parent.

3. 2
Operations on Binary Search Tree
• Insertion: To add a new node to the tree, we compare the value with
the current node and follow the left or right child accordingly until we
find an empty spot.
• Deletion: When deleting a node, we need to consider three cases:
• - If the node has no children, we can simply remove it.
• - If the node has one child, we replace it with its child.
• - If the node has two children, we find the next smallest value (or
next largest) in the tree and replace it with the node to be deleted.
• Your third bullet

4. 3
Traversing a Binary Search Tree
• Inorder Traversal: In this traversal, we visit the left subtree, then the
root, and finally the right subtree. This results in a sorted list of
values.
• Preorder Traversal: In this traversal, we visit the root, then the left
subtree, and finally the right subtree.
• Postorder Traversal: In this traversal, we visit the left subtree, then
the right subtree, and finally the root.

5. 4
Searching in a Binary Search Tree
• To search for a value in a binary search tree, we compare the value
with the current node.
• If the value matches, we have found the node.
• If the value is smaller, we move to the left child.

6. 5
Advantages of Binary Search Tree
• Efficient searching: Due to the hierarchical structure of the tree,
search operations can be performed quickly by eliminating half of
the remaining nodes at each step.
• Sorted storage: Inorder traversal of a binary search tree gives the
elements in sorted order, which can be useful in certain scenarios.
• Insertion and deletion flexibility: Binary search trees allow dynamic
insertion and deletion of nodes, maintaining the tree's balance
through rotation operations.

7. 6
Disadvantages of Binary Search Tree
• Imbalanced trees: If the elements are inserted in a specific order, the
tree can become unbalanced, resulting in poor performance for
search operations.
• Lack of uniqueness: Binary search trees do not inherently enforce
uniqueness of values, which means duplicate values can be
inserted.
• Memory overhead: Binary search trees require additional memory to
store the tree structure, including pointers to left and right children.

8. 7
Conclusion
• Binary search trees are a versatile data structure for organizing and
accessing data efficiently.
• They offer advantages such as efficient searching, sorted storage,
and flexibility in insertion and deletion.
• However, they can suffer from imbalanced trees, lack of uniqueness,
and memory overhead. Careful consideration of the data and
appropriate balancing techniques can help mitigate these issues.