Inorder traversal gives sorted order in Data Structures Theory - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the nodes of a binary search tree in sorted order using inorder traversal.

Data Structures Theory

def inorder_traversal(node):
    if node is not None:
        inorder_traversal(node.left)
        print(node[1])
        inorder_traversal(node.right)

Drag options to blanks, or click blank then click option'

A.value

B.val

C.key

D.data

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect attribute names like .val or .key which may not exist.

Printing the node object directly instead of its value.

The inorder traversal visits nodes in the left subtree, then the current node, then the right subtree. To print the node's value, we access the .value attribute.

2fill in blank

medium

Complete the code to collect the inorder traversal result in a list instead of printing.

Data Structures Theory

def inorder_collect(node, result):
    if node is not None:
        inorder_collect(node.left, result)
        result.append(node[1])
        inorder_collect(node.right, result)

Drag options to blanks, or click blank then click option'

A.val

B.value

C.data

D.key

Attempts:

3 left

💡 Hint

Common Mistakes

Appending the node object instead of its value.

Using wrong attribute names causing errors.

To collect the values in order, append the node's .value attribute to the result list during traversal.

3fill in blank

hard

Fix the error in the inorder traversal code that causes incorrect order by completing the blank.

Data Structures Theory

def inorder_traversal(node):
    if node is not None:
        inorder_traversal(node.right)
        print(node[1])
        inorder_traversal(node.left)

Drag options to blanks, or click blank then click option'

A.value

B.val

C.data

D.key

Attempts:

3 left

💡 Hint

Common Mistakes

Visiting right subtree before left causing descending order output.

Using wrong attribute names for node data.

The inorder traversal must visit the left subtree first, then the node, then the right subtree. The code visits right first, which is incorrect. Fixing the attribute to .value is necessary to print the node's data.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each node's value to its depth during inorder traversal.

Data Structures Theory

depth_map = {node[1]: depth for node, depth in nodes if node[2]

Drag options to blanks, or click blank then click option'

A.value

Bis not None

C!= None

D.val

Attempts:

3 left

💡 Hint

Common Mistakes

Using != None instead of is not None which is less preferred.

Using wrong attribute names for node values.

The dictionary keys should be the node's .value. The condition checks that the node is not None using is not None to avoid errors.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that stores node values as keys and their depths as values, filtering only nodes with values greater than 10.

Data Structures Theory

depth_map = {node[1]: depth for node, depth in nodes if node[2] [3] 10}

Drag options to blanks, or click blank then click option'

A.value

Bvalue

C>

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using inconsistent attribute names in keys and condition.

Using wrong comparison operators like >= when only greater than is needed.

Use .value to access the node's data for keys and in the condition. The comparison operator > filters nodes with values greater than 10.