diff --git a/Python/chapter04/p02_minimal_tree/miguelHx.py b/Python/chapter04/p02_minimal_tree/miguelHx.py
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+"""Python Version 3.9.2
+4.2 - Minimal Tree:
+Given a sorted (increasing order) array with unique integer elements,
+write an algorithm to create a binary search tree with minimal height.
+"""
+import math
+import unittest
+
+from abc import abstractmethod
+from dataclasses import dataclass
+from typing import Generic, TypeVar
+from typing import Optional, Protocol
+from typing import Generator, List, Iterator
+
+
+T = TypeVar('T', bound='Comparable')
+
+class Comparable(Protocol):
+    @abstractmethod
+    def __lt__(self, other: T) -> bool:
+        pass
+
+    @abstractmethod
+    def __gt__(self, other: T) -> bool:
+        pass
+
+    @abstractmethod
+    def __eq__(self, other: object) -> bool:
+        pass
+
+@dataclass
+class BSTNode(Generic[T]):
+    val: T
+    left_child: 'Optional[BSTNode]' = None
+    right_child: 'Optional[BSTNode]' = None
+
+    def __str__(self):
+        return f'Node ({self.id}), Left ID: {self.left_child.id}, Right ID: {self.right_child.id}'
+
+class BSTIterator(Iterator[T]):
+
+    def __init__(self, root: Optional[BSTNode]):
+        self.gen = self.in_order_traversal_generator(root)
+
+    def in_order_traversal_generator(self, node: Optional[BSTNode]) -> Generator:
+        if not node:
+            raise StopIteration
+        if node.left_child:
+            yield from self.in_order_traversal_generator(node.left_child)
+        yield node.val
+        if node.right_child:
+            yield from self.in_order_traversal_generator(node.right_child)
+
+    def __next__(self) -> T:
+        return next(self.gen)
+
+@dataclass
+class BinarySearchTree:
+    root: 'Optional[BSTNode]' = None
+
+    def insert(self, value: T) -> None:
+        if not self.root:
+            self.root = BSTNode(value)
+        else:
+            self._insert(value, self.root)
+
+    def _insert(self, value: T, curr_node: BSTNode) -> None:
+        if value < curr_node.val:
+            if not curr_node.left_child:
+                # insert here
+                curr_node.left_child = BSTNode(value)
+            else:
+                # otherwise, keep searching left subtree
+                self._insert(value, curr_node.left_child)
+        elif value > curr_node.val:
+            if not curr_node.right_child:
+                # insert here
+                curr_node.right_child = BSTNode(value)
+            else:
+                # otherwise, keep searching right subtree
+                self._insert(value, curr_node.right_child)
+        else:
+            raise ValueError(f'Value {value} already exists in tree.')
+
+    def height(self) -> int:
+        return self._height(self.root)
+
+    def _height(self, node: Optional[BSTNode]) -> int:
+        if not node:
+            return 0
+        else:
+            return 1 + max(self._height(node.left_child), self._height(node.right_child))
+
+    def print_tree(self):
+        if self.root:
+            self._print_tree(self.root)
+
+    def _print_tree(self, curr_node: Optional[BSTNode]) -> None:
+        if curr_node:
+            self._print_tree(curr_node.left_child)
+            print(curr_node.val)
+            self._print_tree(curr_node.right_child)
+
+    def __iter__(self) -> BSTIterator:
+        return BSTIterator(self.root)
+
+
+
+def minimal_tree(arr: List[T], bst: Optional[BinarySearchTree] = None) -> BinarySearchTree:
+    """Given a sorted (increasing order) array
+    with unique integer elements, write an algorithm
+    to create a binary search tree with minimal height.
+    Basic steps:
+    1. get midpoint
+    2. insert midpoint into bst
+    3. turn left to get left midpoint until no more values
+    4. turn right to insert right midpoint until no more values
+
+    Time Complexity: O(n) where n is size of arr
+    Space Complexity: O(n)
+
+    Args:
+        arr (List[T]): list of unique numbers sorted, in incr. order.
+
+    Returns:
+        BinarySearchTree: A binary search tree with minimal height.
+    """
+    bst = BinarySearchTree() if not bst else bst
+    if not arr:
+        return bst
+    # middle value gets inserted first before going left or right
+    middle = math.floor(len(arr) / 2)
+    bst.insert(arr[middle])
+    left_subarr = arr[:middle]
+    right_subarr = arr[middle+1:]
+    minimal_tree(left_subarr, bst)
+    minimal_tree(right_subarr, bst)
+    return bst
+
+
+class TestBinarySearchTree(unittest.TestCase):
+
+    def test_binary_search_tree_creation_height_3(self):
+        bst = BinarySearchTree()
+        bst.insert(8)
+        bst.insert(4)
+        bst.insert(10)
+        bst.insert(2)
+        bst.insert(6)
+        bst.insert(20)
+        self.assertEqual(list(bst), [2, 4, 6, 8, 10, 20])
+        self.assertEqual(bst.height(), 3)
+
+    def test_binary_search_tree_creation_height_4(self):
+        bst = BinarySearchTree()
+        bst.insert(8)
+        bst.insert(2)
+        bst.insert(10)
+        bst.insert(4)
+        bst.insert(6)
+        bst.insert(20)
+        self.assertEqual(list(bst), [2, 4, 6, 8, 10, 20])
+        self.assertEqual(bst.height(), 4)
+
+
+class TestMinimalTree(unittest.TestCase):
+
+    def test_minimal_tree(self):
+        # sorted, increasing order array
+        arr = [2, 4, 6, 8, 9, 10, 20]
+        bst = minimal_tree(arr)
+        self.assertEqual(list(bst), [2, 4, 6, 8, 9, 10, 20])
+        self.assertEqual(bst.height(), 3)
+
+
+if __name__ == '__main__':
+    unittest.main()