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CheezItMan
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Nora, nice work, you hit the learning goals here. The only comments I have here for you are about time/space complexity. That's one thing to work on.
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| # Time Complexity: O(log n) | ||
| # Space Complexity: O(log n) | ||
| def add(key, value) |
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👍 , your time/space complexities are correct assuming the tree is balanced.
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| # Time Complexity: O(log n) | ||
| # Space Complexity: O(log n) | ||
| def find(key) |
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👍 Since you are using a loop the space complexity is actually O(1) since you are not building a system stack.
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| def inorder |
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| def preorder |
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| # Time Complexity: O(n) | ||
| # Space Complexity: O(n) | ||
| def postorder |
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| # Time Complexity: O(n) - worst case for unbalanced, best case O(log n) | ||
| # Space Complexity: O(1) | ||
| def height |
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👍 Since you are doing recursion the space complexity is O(log n) assuming the tree is balanced and O(n) otherwise.
The time complexity is O(n) since you visit each node.
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