Hashing is a data structure that is used to store a large amount of data, which can be accessed in
O(1) time by operations such as search, insert and delete. Various Applications of Hashing are:
- Indexing in database
- Symbol Tables in Compiler/Interpreter
- Dictionaries, caches, etc.
Concept of Hashing, Hash Table and Hash Function
Hashing is an important Data Structure which is designed to use a special function called the Hash function which is used to map a given value with a particular key for faster access of elements. The efficiency of mapping depends of the efficiency of the hash function used.
h(large_value) = large_value % m
h() is the required hash function and ‘m’ is the size of the hash table. For large values, hash functions produce value in a given range.
How Hash Function Works?
- It should always map large keys to small keys.
- It should always generate values between 0 to m-1 where m is the size of the hash table.
- It should uniformly distribute large keys into hash table slots.
- Open Addressing (Linear Probing, Quadratic Probing, Double Hashing)
If we know the keys beforehand, then we have can have perfect hashing. In perfect hashing, we do not have any collisions. However, If we do not know the keys, then we can use the following methods to avoid collisions:
While hashing, the hashing function may lead to a collision that is two or more keys are mapped to the same value. Chain hashing avoids collision. The idea is to make each cell of hash table point to a linked list of records that have same hash function value.
Note: In Linear Probing, whenever a collision occurs, we probe to the next empty slot. While in Quadratic Probing, whenever a collision occurs, we probe for
i^2th slot in the ith iteration and we keep probing until an empty slot in the hashtable is found.
Performance of Hashing
The performance of hashing is evaluated on the basis that each key is equally likely to be hashed for any slot of the hash table.
m = Length of Hash Table n = Total keys to be inserted in the hash table Load factor lf = n/m Expected time to search = O(1 +lf ) Expected time to insert/delete = O(1 + lf) The time complexity of search insert and delete is O(1) if lf is O(1)
Python Implementation of Hashing
0 --> Allahabad --> Mathura 1 --> Punjab --> Noida 2 3 4 5 --> Mumbai 6 7 8 9 --> Delhi
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.
- Chaining comparison operators in Python
- NLP | Chunk Tree to Text and Chaining Chunk Transformation
- How to Filter rows using Pandas Chaining?
- Full domain Hashing with variable Hash size in Python
- hmac - Keyed-Hashing for Message Authentication
- Hashing in Distributed Systems
- Mid-Square hashing
- Address Calculation Sort using Hashing
- String hashing using Polynomial rolling hash function
- Linear Regression (Python Implementation)
- Random Walk (Implementation in Python)
- Python implementation of automatic Tic Tac Toe game using random number
- Implementation of Dynamic Array in Python
- Interesting Python Implementation for Next Greater Elements
- Python | Implementation of Polynomial Regression
- Python | Implementation of Movie Recommender System
- ML | Reinforcement Learning Algorithm : Python Implementation using Q-learning
- Python 3.6 dictionary implementation using hash tables
- Hamming Code implementation in Python
- ML | Naive Bayes Scratch Implementation using Python
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to firstname.lastname@example.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.