Memory Layout Comparison Array vs Linked List in DSA Python - Interactive Comparison Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an array with 5 elements initialized to zero.

DSA Python

array = [[1]] * 5
print(array)

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

A1

BNone

C0

D''

Attempts:

3 left

💡 Hint

Common Mistakes

Using None instead of 0

Using a string instead of a number

The array is initialized with zeros using 0. This creates a list of five zeros.

2fill in blank

medium

Complete the code to define a Node class for a linked list with a value and a next pointer.

DSA Python

class Node:
    def __init__(self, value):
        self.value = value
        self.[1] = None

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

Anext

Bpointer

Cprev

Dlink

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'prev' which is for doubly linked lists

Using 'pointer' or 'link' which are not standard attribute names

The next attribute points to the next node in the linked list.

3fill in blank

hard

Fix the error in the code that appends a new node to the end of a linked list.

DSA Python

def append_node(head, value):
    new_node = Node(value)
    current = head
    while current.[1] is not None:
        current = current.next
    current.next = new_node
    return head

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

Anext

Bprev

Cvalue

Dpointer

Attempts:

3 left

💡 Hint

Common Mistakes

Checking 'prev' which is not used here

Checking 'value' which is data, not a pointer

The loop should check current.next to find the last node.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps array indices to their values if the value is even.

DSA Python

{i: arr[i] for i in range(len(arr)) if arr[i] [1] 2 == 0 and i [2] 5}

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

A%

B==

C<

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of '%' for even check

Using '>' instead of '<' for index filtering

The modulo operator % checks if a number is even. The comparison < filters indices less than 5.

5fill in blank

hard

Fill all three blanks to create a linked list from a list of values and print the values.

DSA Python

head = Node([1])
current = head
for val in values[1:]:
    current.[2] = Node(val)
    current = current.[3]

# Print linked list values
while head is not None:
    print(head.value, end=' -> ')
    head = head.next
print('None')

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

Avalues[0]

Bnext

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'value' instead of 'next' for linking

Using wrong index for first value

The first node is created with values[0]. The next attribute links nodes and moves the current pointer forward.

Practice

1. Which of the following best describes the memory layout of an array compared to a linked list?

easy

A. Array stores elements in contiguous memory locations, linked list stores elements scattered with pointers.

B. Array stores elements scattered with pointers, linked list stores elements in contiguous memory.

C. Both array and linked list store elements in contiguous memory locations.

D. Both array and linked list store elements scattered randomly without pointers.

Solution

1. Step 1: Understand array memory layout

   Arrays store elements one after another in a continuous block of memory.

2. Step 2: Understand linked list memory layout

   Linked lists store elements anywhere in memory, each element points to the next.

3. Final Answer:

   Array stores elements in contiguous memory locations, linked list stores elements scattered with pointers. -> Option A

4. Quick Check:

   Array = contiguous, Linked List = scattered with pointers [OK]

Hint: Arrays are continuous blocks; linked lists use scattered nodes with pointers [OK]

Common Mistakes:

• Thinking linked lists store elements contiguously
• Confusing pointers with contiguous storage
• Assuming arrays use pointers between elements

2. Which Python code snippet correctly creates a linked list node with a value and a pointer to the next node?

easy

A. class Node:
def __init__(self, val, next):
self.val = val
self.next = next

B. class Node:
def __init__(self, val):
self.val = val
self.next = None

C. class Node:
def __init__(self):
self.val = None
self.next = None

D. class Node:
def __init__(self, val):
self.val = val
self.next = val

Solution

1. Step 1: Check constructor parameters

   class Node: def __init__(self, val, next): self.val = val self.next = next takes both value and next node pointer, which is correct for linked list nodes.

2. Step 2: Verify pointer assignment

   class Node: def __init__(self, val, next): self.val = val self.next = next assigns self.next to the passed next node, correctly linking nodes.

3. Final Answer:

   class Node: def __init__(self, val, next): self.val = val self.next = next -> Option A

4. Quick Check:

   Node needs value and next pointer parameters [OK]

Hint: Linked list nodes need value and next pointer in constructor [OK]

Common Mistakes:

• Not passing next pointer in constructor
• Assigning next pointer incorrectly
• Using value for next pointer

3. Consider this Python code creating an array and a linked list with 3 elements each. What is the output of printing their sizes?

import sys

arr = [10, 20, 30]

class Node:
    def __init__(self, val, next=None):
        self.val = val
        self.next = next

node3 = Node(30)
node2 = Node(20, node3)
node1 = Node(10, node2)

print(sys.getsizeof(arr))
print(sys.getsizeof(node1))

medium

A. Code will raise an error due to sys.getsizeof usage

B. Array size is smaller than single node size

C. Array and node sizes are equal

D. Array size is larger than single node size

Solution

1. Step 1: Understand sys.getsizeof on array

   sys.getsizeof(arr) returns size of list object plus references to elements, usually larger than single node.

2. Step 2: Understand sys.getsizeof on node

   sys.getsizeof(node1) returns size of one Node object, smaller than whole array size.

3. Final Answer:

   Array size is larger than single node size -> Option D

4. Quick Check:

   Array holds multiple elements, node is single object [OK]

Hint: Array holds multiple items, so size > single node size [OK]

Common Mistakes:

• Assuming node size includes whole list
• Thinking array size is smaller than node
• Believing sys.getsizeof causes error

4. The following code attempts to traverse a linked list but causes an error. What is the bug?

class Node:
    def __init__(self, val, next=None):
        self.val = val
        self.next = next

node3 = Node(30)
node2 = Node(20, node3)
node1 = Node(10, node2)

current = node1
while current.next is not None:
    print(current.val)
    current = current.next

medium

A. The code raises AttributeError on current.next

B. The loop causes infinite iteration

C. The loop misses printing the last node's value

D. The code prints values in reverse order

Solution

1. Step 1: Analyze loop condition

   The loop runs while current.next is not None, so it stops before last node.

2. Step 2: Check printed values

   Last node's value is never printed because loop ends before current becomes last node.

3. Final Answer:

   The loop misses printing the last node's value -> Option C

4. Quick Check:

   Loop condition excludes last node printing [OK]

Hint: Use 'while current is not None' to print all nodes [OK]

Common Mistakes:

• Assuming last node prints automatically
• Thinking loop causes error
• Believing loop prints in reverse

5. You want to store 1 million integers and frequently insert elements in the middle. Considering memory layout and performance, which data structure is better and why?

hard

A. Array, because contiguous memory improves cache performance despite costly middle insertions

B. Linked list, because scattered nodes allow fast middle insertions without shifting elements

C. Array, because it uses pointers to link elements making insertions fast

D. Linked list, because it stores elements contiguously for faster access

Solution

1. Step 1: Consider insertion cost in arrays

   Arrays require shifting elements for middle insertions, which is slow for large data.

2. Step 2: Consider linked list insertion

   Linked lists insert by changing pointers, no shifting needed, so faster for middle insertions.

3. Step 3: Consider memory layout tradeoff

   Linked lists use scattered memory but benefit insertion speed; arrays have better cache but slower insertions.

4. Final Answer:

   Linked list, because scattered nodes allow fast middle insertions without shifting elements -> Option B

5. Quick Check:

   Linked list = fast middle insertions [OK]

Hint: Linked lists excel at middle insertions; arrays need shifting [OK]

Common Mistakes:

• Choosing array for fast middle insertions
• Confusing memory layout benefits
• Assuming arrays use pointers like linked lists