Understanding Trees in Python

Introduction to Trees

• Trees are a fundamental data structure in computer science.
• Visualization helps in understanding trees and graphs.
• Focus will be on binary trees for simplicity.

Binary Tree Structure

• Root: The starting node of a tree.
• Left and Right Child: Each node can have up to two children.
• Nodes less than the parent go to the left.
• Nodes greater than the parent go to the right.

Example Tree Construction

• Start with root node 'G'.
• Insert nodes:
  • 'C' less than 'G', goes left.
  • 'B' less than 'G' and 'C', goes left.
  • 'A' less than 'G', 'C', and 'B', goes left.
  • 'E' less than 'G' but greater than 'C', goes right of 'C'.
  • 'D' follows similar comparisons to determine position.
  • Continue similarly for nodes 'F', 'I', 'H', 'J', 'K'.

Implementing Trees in Python

Creating the Node Structure

• Create a Node class:
  • Attributes: data, left, right.
  • Constructor initializes attributes; left and right set to None.

Inserting Data

• Method insert to add data:
  • Base Case: If root is None, set the root.
  • Recursive Case:
    • If data < root, check left child:
      • If None, insert.
      • Else, recurse on left child.
    • If data > root, check right child:
      • If None, insert.
      • Else, recurse on right child.

Building the Tree in Code

• Instantiate Node class with root as 'G': root = Node('G') root.insert('C') root.insert('B') root.insert('A') ... (continue for other nodes)

Next Steps

• Upcoming tutorials will cover:
  • Breadth-First Search (BFS): Traversing nodes level by level.
  • Depth-First Search (DFS): Traversing as far along a branch before backtracking.

Conclusion

• Review and practice building trees and implementing basic operations in Python.
• More to come in subsequent tutorials on searching within trees.