Hash Table Concept and Hash Functions in DSA Python - Time & Space Complexity

Time Complexity: Hash Table Concept and Hash Functions

O(1)

Understanding Time Complexity

We want to understand how fast a hash table can find or store data using hash functions.

The question is: how does the time to do these operations change as we add more items?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

class SimpleHashTable:
    def __init__(self, size):
        self.size = size
        self.table = [None] * size

    def hash_function(self, key):
        return hash(key) % self.size

    def insert(self, key, value):
        index = self.hash_function(key)
        self.table[index] = value

    def get(self, key):
        index = self.hash_function(key)
        return self.table[index]

This code creates a simple hash table with a basic hash function and supports insert and get operations.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Calculating the hash and accessing the array index.
• How many times: Each insert or get does this once, no loops inside these methods.

How Execution Grows With Input

Each operation does a quick calculation and direct access, so time stays about the same even if the table grows.

Input Size (n)	Approx. Operations
10	1 operation per insert/get
100	1 operation per insert/get
1000	1 operation per insert/get

Pattern observation: The time does not increase with more items, it stays constant.

Final Time Complexity

Time Complexity: O(1)

This means each insert or get takes about the same time no matter how many items are stored.

Common Mistake

[X] Wrong: "Hash table operations always take longer as more items are added because the array gets bigger."

[OK] Correct: The hash function directly finds the spot, so time stays constant unless many items collide in the same spot.

Interview Connect

Understanding hash tables and their time complexity shows you know how to store and find data quickly, a key skill in many coding problems.

Self-Check

"What if many keys hash to the same index and we handle collisions by searching a list? How would the time complexity change?"

Practice

1. What is the main purpose of a hash function in a hash table?

easy

A. To encrypt the data stored in the table

B. To sort the keys in ascending order

C. To convert a key into an index for storing data

D. To count the number of elements in the table

Solution

1. Step 1: Understand the role of a hash function

   A hash function takes a key and converts it into a number that can be used as an index in the hash table.

2. Step 2: Compare with other options

   Sorting keys, encrypting data, or counting elements are not the main roles of a hash function in a hash table.

3. Final Answer:

   To convert a key into an index for storing data -> Option C

4. Quick Check:

   Hash function = key to index [OK]

Hint: Hash function maps keys to indexes fast [OK]

Common Mistakes:

• Thinking hash function sorts keys
• Confusing hash function with encryption
• Assuming hash function counts elements

2. Which of the following is the correct syntax to define a simple hash function in Python that returns the remainder of a key divided by 10?

easy

A. def hash_func(key): return key % 10

B. def hash_func(key): return key / 10

C. def hash_func(key): return key * 10

D. def hash_func(key): return key + 10

Solution

1. Step 1: Identify the correct modulus operation

   The hash function should return the remainder when key is divided by 10, which is done using the modulus operator '%'.

2. Step 2: Check syntax correctness

   def hash_func(key): return key % 10 uses 'return key % 10' which is correct syntax and logic. Other options use division, multiplication, or addition which do not produce the remainder.

3. Final Answer:

   def hash_func(key): return key % 10 -> Option A

4. Quick Check:

   Use % for remainder in hash function [OK]

Hint: Use % operator for remainder in hash functions [OK]

Common Mistakes:

• Using division instead of modulus
• Multiplying or adding instead of modulus
• Incorrect function syntax

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

def simple_hash(key):
    return key % 5

hash_table = [None] * 5
keys = [12, 22, 17]
for key in keys:
    index = simple_hash(key)
    hash_table[index] = key
print(hash_table)

medium

A. [None, None, 12, 17, 22]

B. [None, None, 17, None, None]

C. [None, 22, 12, None, 17]

D. [None, 12, 22, 17, None]

Solution

1. Step 1: Calculate hash indexes for each key

   12 % 5 = 2, 22 % 5 = 2, 17 % 5 = 2. All three keys map to the same index 2.

2. Step 2: Trace the assignments

   The code assigns directly to hash_table[index] with no collision handling, so later keys overwrite earlier ones at index 2: 12 -> 22 -> 17. Final state: [None, None, 17, None, None].

3. Final Answer:

   [None, None, 17, None, None] -> Option B

4. Quick Check:

   All keys hash to index 2, last key stored [OK]

Hint: Check hash index for each key carefully [OK]

Common Mistakes:

• Assuming keys store at different indexes without collision
• Ignoring overwriting in hash table
• Miscomputing modulus

4. Identify the error in the following hash function code:

def hash_func(key):
    return key // 10

hash_table = [None] * 10
index = hash_func(25)
hash_table[index] = 25
print(hash_table)

medium

A. No error, code runs correctly

B. hash_table size is too small for keys

C. Function should return key * 10 instead

D. Using integer division instead of modulus causes wrong indexing

Solution

1. Step 1: Understand the hash function logic

   The function uses integer division '//' which divides key by 10 and returns the quotient, not the remainder.

2. Step 2: Check impact on indexing

   For key=25, 25 // 10 = 2, which is a valid index but this is not a typical hash function behavior. Usually, modulus '%' is used to get remainder for index within table size.

3. Step 3: Identify the error

   Using '//' can cause indexes outside the hash table size if keys are large. Modulus '%' should be used to ensure index is within table size.

4. Final Answer:

   Using integer division instead of modulus causes wrong indexing -> Option D

5. Quick Check:

   Use % for index, not // [OK]

Hint: Use % to keep index within table size [OK]

Common Mistakes:

• Confusing integer division with modulus
• Ignoring table size limits
• Assuming code runs without error

5. You want to design a hash function for strings that returns an index between 0 and 9 for a hash table of size 10. Which of the following functions correctly implements this?

hard

A. def hash_func(s): return sum(ord(c) for c in s) % 10

B. def hash_func(s): return len(s) * 10

C. def hash_func(s): return ord(s[0])

D. def hash_func(s): return s.count('a')

Solution

1. Step 1: Understand the requirement

   The hash function must return an integer index between 0 and 9 for any string input.

2. Step 2: Analyze each option

   def hash_func(s): return sum(ord(c) for c in s) % 10 sums ASCII values of all characters and uses modulus 10 to keep index in range 0-9. def hash_func(s): return len(s) * 10 multiplies length by 10, which can exceed 9. def hash_func(s): return ord(s[0]) returns ASCII of first char, which can be larger than 9. def hash_func(s): return s.count('a') counts 'a's, which can be any number, possibly larger than 9.

3. Step 3: Choose correct function

   Only def hash_func(s): return sum(ord(c) for c in s) % 10 guarantees index in 0-9 range for any string.

4. Final Answer:

   def hash_func(s): return sum(ord(c) for c in s) % 10 -> Option A

5. Quick Check:

   Sum ASCII % table size for string hash [OK]

Hint: Sum ASCII codes and mod by table size [OK]

Common Mistakes:

• Using string length times 10 as index
• Using ASCII code directly without modulus
• Using character count without range check