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Binary trees, trees which have at most two children per node, are a useful data structure for organizing hierarchical data.

It’s helpful to know the depth of a tree, or how many levels make up the tree.

# first level root_of_tree = {"data": 42} # adding a child - second level root_of_tree["left_child"] = {"data": 34} root_of_tree["right_child"] = {"data": 56} # adding a child to a child - third level first_child = root_of_tree["left_child"] first_child["left_child"] = {"data": 27}

Here’s an iterative algorithm for counting the depth of a given tree.

We’re using Python dictionaries to represent each tree node, with the key of `"left_child"`

or `"right_child"`

referencing **another** tree node, or `None`

if no child exists.

def depth(tree): result = 0 # our "queue" will store nodes at each level queue = [tree] # loop as long as there are nodes to explore while queue: # count the number of child nodes level_count = len(queue) for child_count in range(0, level_count): # loop through each child child = queue.pop(0) # add its children if they exist if child["left_child"]: queue.append(child["left_child"]) if child["right_child"]: queue.append(child["right_child"]) # count the level result += 1 return result two_level_tree = { "data": 6, "left_child": {"data": 2} } four_level_tree = { "data": 54, "right_child": {"data": 93, "left_child": {"data": 63, "left_child": {"data": 59} } } } depth(two_level_tree) # 2 depth(four_level_tree) # 4

This algorithm will visit each node in the tree once, which makes it a linear runtime, `O(N)`

, where `N`

is the number of nodes in the tree.

### Instructions

**1.**

Implement your version of `depth()`

which has the same functionality **using recursive calls!**

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