Introduction to Hashing in Python

introduction to hashing in python

Introduction to Hashing in Python

 Hashing is a fundamental concept used to efficiently store and retrieve data. Hashing algorithms play a crucial role in various applications, including data retrieval, encryption, and security.

Let’s jump into the article to know more about Hashing in Python and also you will get to know about  the world of hashing, explaining its importance, different types, and practical applications.

Understanding Hashing in Python

Hashing is a process of mapping data of arbitrary size to a fixed-size value, known as a hash code or simply a “hash.” The primary objective of hashing is to expedite data retrieval by converting complex data into a compact representation, making it easier to manage and access.

Hashing in Python Examples

introduction to hashing in python 3


Why Use Hashing in Python?

Hashing in Python is widely used for various reasons, including data integrity, password storage, and efficient data retrieval. It allows for quick data comparison and indexing, making it a crucial tool for programmers.

Hash Function in Python

A hash function is a mathematical function that takes an input (or ‘message’) and returns a fixed-size string of characters, which is typically a hexadecimal number. The output, often called the ‘hash value’ or ‘hash code,’ is unique to the input data. Hash functions are designed to efficiently map data of arbitrary size to a fixed-size value.

Hashing in Python

  • Python provides built-in hash functions, making it easy to compute hash values for data. Python’s hash() function is often used for this purpose. However, in some cases, you may need to create custom hash functions tailored to your specific needs.

Implementing Hash Functions in Python

Python offers a built-in hash() function that can quickly compute hash values for a variety of data types.

Code :

# Hashing a string
data = "Hello, World!"
hash_value = hash(data)
print("Hash value for the string:", hash_value)

# Hashing an integer
number = 42
hash_value = hash(number)
print("Hash value for the integer:", hash_value)

Explanation :

  • The hash() function is used to generate hash values for both a string and an integer.
  • It’s important to note that the hash values produced by the hash() function are platform-specific and may differ between different Python installations.
  • These hash values are suitable for data retrieval and data integrity checks, but they should not be used in security-sensitive applications like password hashing.

Components of Hashing in Python

introduction to hashing in python

Hashing Algorithms in Python

Python supports several hashing algorithms, such as MD5, SHA-1, and SHA-256. These algorithms produce hash values of varying lengths and security levels.

Types of Hashing Algorithms:

  1. MD5
  2. SHA-1
  3. SHA-256


MD5, which stands for Message Digest Algorithm 5, is a widely used cryptographic hash function. It was designed by Ronald Rivest in 1991 and is commonly employed in various applications for data verification and fingerprinting. MD5 produces a fixed-size 128-bit (16-byte) hash value, typically represented as a 32-character hexadecimal number.

Common Uses of MD5

1. Data Integrity

MD5 is often used to verify the integrity of data during transmission. By computing the MD5 hash of the original data and comparing it with the received data’s hash, you can quickly detect any changes or errors.

2. Password Storage

MD5 was used to store passwords. When a user creates or updates a password, the system hashes it using MD5 and stores the hash. When the user logs in, the system hashes the entered password and compares it to the stored hash.

3. Digital Signatures

MD5 is employed in digital signatures and certificates to ensure the authenticity of documents and software. By hashing the content and providing a hash value, you can verify that the document has not been altered.


SHA-1, which stands for Secure Hash Algorithm 1, is a cryptographic hash function designed to take an input message and produce a fixed-size 160-bit (20-byte) hash value. While SHA-1 was once considered secure and widely used, it is now considered vulnerable to collision attacks, and its use is discouraged for security-critical applications.

Example :

import hashlib

# Create a sample message to hash
message = "Hello, World!"

# Create a SHA-1 hash object
sha1_hash = hashlib.sha1()

# Update the hash object with the message

# Get the hexadecimal representation of the hash
hashed_message = sha1_hash.hexdigest()

# Print the SHA-1 hash
print("SHA-1 Hash:", hashed_message)


SHA-256, part of the SHA-2 family of cryptographic hash functions, is widely used for various security and integrity purposes. It produces a 256-bit (32-byte) hash value, which is considered highly secure.


import hashlib

# Create a sample message to hash
message = "Hello, World!"

# Create a SHA-256 hash object
sha256_hash = hashlib.sha256()

# Update the hash object with the message

# Get the hexadecimal representation of the hash
hashed_message = sha256_hash.hexdigest()

# Print the SHA-256 hash
print("SHA-256 Hash:", hashed_message)

Implementing Hashing in Python

Example: Hashing a Password

import hashlib

def hash_password(password):
    salt = "somerandomsalt"
    password = password + salt
    hashed_password = hashlib.sha256(password.encode()).hexdigest()
    return hashed_password

password = "mysecretpassword"
hashed_password = hash_password(password)
print("Hashed Password:", hashed_password)

What is collision?

A collision in hashing refers to a situation where two different inputs produce the same hash value or hash code. In other words, it occurs when two distinct sets of data or messages result in identical hash values after undergoing the hashing process.

For Example :

Introduction to hashing in python

How to handle collision

Handling collisions is a critical aspect of working with hashing algorithms, particularly in data structures and cryptographic applications. There are mainly two methods to handle collision:

  1. Separate Chaining:
  2. Open Addressing:

Separate Chaining:

  • In this approach, each hash bucket (a location where values are stored) in a hash table contains a linked list or another data structure to hold multiple values that hash to the same location.
  • When a collision occurs, the new value is simply added to the list at that location. To retrieve a specific value, you search within the list. This method ensures that all values with the same hash can be stored and retrieved efficiently.

Open Addressing:

  • Instead of using separate data structures, open addressing aims to find the next available slot when a collision happens
  • There are various open addressing techniques, such as linear probing (checking the next available slot), quadratic probing (using a quadratic function to find the next slot), and double hashing (using a second hash function to calculate the next slot).
  • Open addressing can lead to better cache performance compared to separate chaining.

Applications of Hashing


In conclusion, Introduction to Hashing in Python provide the important informationabout hash functions and their applications in data structures and algorithms. With a deep understanding of hashing, you are better equipped to tackle complex problems and optimize your software, ensuring your place at the forefront of technology and innovation.

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Question 1.

What is hashing used for in Python?

Hashing in Python is used for data retrieval, password storage, and cryptographic applications.

Question 2.

How do you handle collisions in hash functions?

Collisions in hash functions are handled using techniques like chaining and open addressing, ensuring that data retrieval remains efficient.

Question 3.

What are some practical use cases of hash functions in Python?

Practical use cases include hashing passwords for security, caching data for faster retrieval, and ensuring data integrity during transmission.

Question 4.

Can I create my own custom hash function in Python?

Yes, you can create custom hash functions in Python, tailored to your specific data and requirements.

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