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CheezItMan
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Jan 14, 2022
CheezItMan
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CheezItMan
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Nice work Abigail, I added some feedback for minor improvements. Let me know if you have questions.
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| # Time Complexity: | ||
| # Space Complexity: | ||
| def add(self, key, value = None): |
| self.root = TreeNode(key,value) | ||
| current = self.root | ||
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| while True: |
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Just a personal note. I dislike while True loops which depend on break statements to control the flow of execution because it makes the loop harder to trace and understand.
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| # Time Complexity: | ||
| # Space Complexity: | ||
| def find(self, key): |
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| # Time Complexity: | ||
| # Space Complexity: | ||
| def inorder(self): |
| left_part= helper(node.left) | ||
| middle_part = [{"key":node.key,"value":node.value}] | ||
| right_part = helper(node.right) | ||
| result = left_part + middle_part + right_part |
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Just a note that doing so much array concatenation is expensive rather than concatenating to the same area each time.
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| # Time Complexity: | ||
| # Space Complexity: | ||
| def preorder(self): |
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| # Time Complexity: | ||
| # Space Complexity: | ||
| def postorder(self): |
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| queue.append(self.root) | ||
| while queue != []: | ||
| temp = queue.pop(0) |
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Just a note that queue.pop(0) is an O(n) operation. Python does have a deque data structure which provides a similar O(1) operation.
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