Hash Map vs Array vs Linked List for Lookup in DSA Python - Visual Comparison

Concept Flow - Hash Map vs Array vs Linked List for Lookup

Start Lookup

↓

Choose Data Structure

↓

Array

↓

Check index

↓

Compare value

↓

Found?

↓

Stop

Lookup starts by choosing the data structure, then follows its specific steps to find the value or stop if not found.

Execution Sample

DSA Python

array = [10, 20, 30, 40]
linked_list = 10 -> 20 -> 30 -> 40
hash_map = {10: 'a', 20: 'b', 30: 'c', 40: 'd'}

def lookup(value):
  # Array: check index
  # Linked List: traverse nodes
  # Hash Map: compute hash and access bucket

This code shows lookup in array, linked list, and hash map for a given value.

Execution Table

Step	Data Structure	Operation	Details	Visual State
1	Array	Check index 0	Compare array[0] == 30? (10 == 30)	[10, 20, 30, 40]
2	Array	Check index 1	Compare array[1] == 30? (20 == 30)	[10, 20, 30, 40]
3	Array	Check index 2	Compare array[2] == 30? (30 == 30) Found	[10, 20, 30, 40]
4	Linked List	Start at head	Current node value = 10	10 -> 20 -> 30 -> 40 -> null
5	Linked List	Move to next	Current node value = 20	10 -> 20 -> 30 -> 40 -> null
6	Linked List	Move to next	Current node value = 30 Found	10 -> 20 -> 30 -> 40 -> null
7	Hash Map	Compute hash	Hash(30) = bucket 3	{10:'a',20:'b',30:'c',40:'d'}
8	Hash Map	Access bucket	Bucket 3 contains key 30 Found	{10:'a',20:'b',30:'c',40:'d'}
9	Array	Check index 3	Compare array[3] == 50? (40 == 50)	[10, 20, 30, 40]
10	Array	End	Value 50 not found in array	[10, 20, 30, 40]
11	Linked List	Move to next	Current node value = 40	10 -> 20 -> 30 -> 40 -> null
12	Linked List	Move to next	Current node is null End	10 -> 20 -> 30 -> 40 -> null
13	Linked List	End	Value 50 not found in linked list	10 -> 20 -> 30 -> 40 -> null
14	Hash Map	Compute hash	Hash(50) = bucket 5	{10:'a',20:'b',30:'c',40:'d'}
15	Hash Map	Access bucket	Bucket 5 empty End	{10:'a',20:'b',30:'c',40:'d'}
16	Hash Map	End	Value 50 not found in hash map	{10:'a',20:'b',30:'c',40:'d'}

💡 Lookup ends when value is found or all elements/buckets checked without success.

Variable Tracker

Variable	Start	After Step 3	After Step 6	After Step 8	After Step 10	After Step 13	After Step 16
Array Index	0	2 (found 30)	2	2	3 (checked 40)	3	3
Linked List Current Node	Head (10)	10	30 (found)	30	40	null (end)	null
Hash Map Bucket	None	None	3 (found 30)	3	5 (empty)	5	5

Key Moments - 3 Insights

Why does array lookup stop early at index 2?

Because at step 3 in execution_table, array[2] equals the target value 30, so lookup returns immediately without checking further.

Why does linked list traversal continue even after checking some nodes?

As shown in steps 4-6, linked list checks nodes one by one until it finds the value or reaches the end (null). It cannot jump directly to a node.

How does hash map find the value so quickly?

At steps 7-8, hash map computes a hash to directly access the bucket where the value should be, avoiding traversal of all elements.

Visual Quiz - 3 Questions

Look at the execution_table, at which step does the array find the value 30?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Check the 'Array' rows and see where 'Found' is mentioned in the Details column.

According to variable_tracker, what is the linked list current node after step 6?

A 30

B null

C 20

D 40

💡 Hint

Look at the 'Linked List Current Node' row under 'After Step 6' column.

If the hash map had a bucket collision at step 8, what would change in the execution_table?

A Array steps increase

B Lookup ends immediately

C More steps to traverse bucket list

D Linked list traversal starts

💡 Hint

Consider how hash maps handle collisions by chaining or probing, requiring extra steps.

Concept Snapshot

Lookup in Array: check each index until found or end.
Lookup in Linked List: traverse nodes one by one.
Lookup in Hash Map: compute hash, access bucket directly.
Hash Map is fastest on average, Array is simple, Linked List is slowest for lookup.
Use Hash Map for fast key-based lookup.
Array good for index-based access.
Linked List good for dynamic insertions, but slow lookup.

Full Transcript

This visual execution compares lookup operations in three data structures: array, linked list, and hash map. Lookup in array checks each index sequentially until the value is found or the end is reached. Linked list lookup traverses nodes one by one from head to tail. Hash map lookup computes a hash to directly access the bucket where the value should be, enabling faster access. The execution table shows step-by-step operations and visual states. Variable tracker records pointer positions and indices after key steps. Key moments clarify why array stops early when found, why linked list must traverse nodes, and how hash map achieves fast lookup. The visual quiz tests understanding of these steps and behaviors. The snapshot summarizes the lookup behavior and use cases for each data structure.

Practice

1. Which data structure provides the fastest average lookup time for finding an element by key?

easy

A. Hash Map

B. Array

C. Linked List

D. Stack

Solution

1. Step 1: Understand lookup times for each data structure

   Hash Map uses a hash function to find keys quickly, usually in constant time O(1). Arrays require scanning or index access, O(1) if index known but O(n) if searching by value. Linked Lists require scanning nodes, O(n).

2. Step 2: Compare average lookup times

   Hash Map is fastest on average for key lookup due to direct hashing. Arrays and Linked Lists are slower for searching by value.

3. Final Answer:

   Hash Map -> Option A

4. Quick Check:

   Fastest lookup = Hash Map [OK]

Hint: Hash Map uses keys for O(1) lookup, arrays and lists do not [OK]

Common Mistakes:

• Confusing array index access with value lookup
• Thinking linked list lookup is fast
• Assuming stack is a lookup structure

2. Which of the following is the correct syntax to create an empty hash map (dictionary) in Python?

easy

A. hash_map = {}

B. hash_map = ()

C. hash_map = []

D. hash_map = set()

Solution

1. Step 1: Recall Python syntax for empty data structures

   Empty list uses [], empty tuple uses (), empty dictionary uses {}, and empty set uses set().

2. Step 2: Identify the hash map syntax

   Hash map in Python is a dictionary, created with {}.

3. Final Answer:

   hash_map = {} -> Option A

4. Quick Check:

   Empty dict = {} [OK]

Hint: Use curly braces {} for empty dictionary in Python [OK]

Common Mistakes:

• Using [] which creates a list, not a dictionary
• Using () which creates a tuple
• Using set() which creates a set, not a dictionary

3. What will be the output of the following Python code?

arr = [10, 20, 30, 40]
lookup = {val: idx for idx, val in enumerate(arr)}
print(lookup.get(30))
print(lookup.get(50, -1))

medium

A. 30\n50

B. 3\n-1

C. 2\n-1

D. null\n-1

Solution

1. Step 1: Understand dictionary comprehension and get method

   The dictionary 'lookup' maps values to their indices: {10:0, 20:1, 30:2, 40:3}. lookup.get(30) returns the index for 30, which is 2. lookup.get(50, -1) looks for 50, not found, returns default -1.

2. Step 2: Determine printed output

   First print outputs 2. Second print outputs -1.

3. Final Answer:

   2 -1 -> Option C

4. Quick Check:

   lookup[30] = 2, missing key returns -1 [OK]

Hint: Dictionary get returns default if key missing [OK]

Common Mistakes:

• Confusing keys and values in dictionary
• Expecting get to raise error if key missing
• Mixing index and value in output

4. The following code tries to find if a value exists in a linked list. What is the bug?

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

def find(head, target):
    current = head
    while current.next is not None:
        if current.val == target:
            return True
        current = current.next
    return False

medium

A. The function should use recursion instead of a loop

B. The loop condition should be 'while current is not None' to check last node

C. The Node class is missing a previous pointer

D. The function should return false inside the loop

Solution

1. Step 1: Analyze loop condition in linked list traversal

   The loop uses 'while current.next is not None', so it stops before last node. This means the last node's value is never checked.

2. Step 2: Correct loop condition to include last node

   Changing to 'while current is not None' ensures all nodes, including last, are checked for target value.

3. Final Answer:

   The loop condition should be 'while current is not None' to check last node -> Option B

4. Quick Check:

   Loop must check all nodes including last [OK]

Hint: Check loop condition includes last node (current is not None) [OK]

Common Mistakes:

• Stopping loop early missing last node
• Adding unnecessary previous pointer
• Thinking recursion is required

5. You have a large dataset of unique user IDs and want to check membership quickly. Which data structure is best and why? A) Array because it stores elements sequentially B) Linked List because it allows easy insertion C) Hash Map because it provides average O(1) lookup D) Stack because it supports last-in-first-out access

hard

A. Array because it stores elements sequentially

B. Linked List because it allows easy insertion

C. Stack because it supports last-in-first-out access

D. Hash Map because it provides average O(1) lookup

Solution

1. Step 1: Understand lookup needs for large dataset

   Checking membership means quickly finding if an element exists. Arrays and linked lists require scanning, O(n) time. Stacks are not designed for membership lookup.

2. Step 2: Identify best data structure for fast membership

   Hash Map uses hashing to find elements in average O(1) time, ideal for large datasets and membership checks.

3. Final Answer:

   Hash Map because it provides average O(1) lookup -> Option D

4. Quick Check:

   Fast membership = Hash Map [OK]

Hint: Use hash map for fast membership checks in large data [OK]

Common Mistakes:

• Choosing array or list for fast lookup
• Confusing insertion speed with lookup speed
• Thinking stack supports membership lookup