Python - Hash Table

Python is a high-level, interpreted programming language acknowledged for its simplicity and clarity, which makes it a first-rate preference for beginners and skilled developers alike. It supports multiple programming paradigms, together with procedural, item-oriented, and useful programming. Python's layout emphasizes code clarity and ease, permitting developers to write down clean and logical code for small and huge-scale tasks.

Python capabilities are a dynamic type of system and automatic memory control, with a widespread preferred library that supports many commonplace programming tasks, such as internet development, facts analysis, artificial intelligence, medical computing, and more. Its giant surroundings of third-party applications, accessible via the Python Package Index (PyPI), similarly extend its abilities. Python's versatility and ease of use have made it one of the most famous programming languages in the global.

Hash table

A hash table, additionally referred to as a hash map, is a statistics structure that efficiently stores and retrieves information through key-value pairs. In Python, hash tables are implemented using dictionaries, which permit average-case consistent time complexity (O(1)) for insertions, deletions, and lookups.

Key Concepts

• Hash Function:
  • Converts a key into an integer (hash value).
  • This integer is then mapped to an index in an array (the hash desk).
  • Python's integrated `hash()` feature may be used to generate hash values.
• Buckets:
  • The array within the hash desk consists of buckets, in which every bucket can save more than one key-value pair.
  • Each bucket can handle collisions (conditions wherein one-of-a-kind keys produce an equal hash value).
• Collision Handling:
  • Separate Chaining: Each bucket incorporates a list (or another data structure) of key-value pairs.
  • Open Addressing: Finds the following available bucket the use of strategies like linear probing or quadratic probing.

Features

• Insertion, Deletion, and Lookup: Python dictionaries offer (O(1)) common time complexity for these operations due to efficient hashing and collision decision mechanisms.
• Dynamic Resizing: Python dictionaries automatically resize themselves to hold efficient operation as the range of key-value pairs grows. This resizing helps keep constant time complexity by maintaining the burden thing (ratio of variety of elements to the scale of the hash desk) within a reasonable variety.
• Arbitrary Key Types: Keys in a Python dictionary can be of any immutable kind (e.g., strings, numbers, tuples). The immutability guarantees that the hash value of the key stays regular.
• Ordered Dictionaries (Python 3.7+): Starting from Python 3.7, dictionaries preserve the insertion order of keys. This method states that after iterating over a dictionary, items may be back within the order they had been introduced.
• Hashing and Collision Resolution:
  • Efficient Hashing: Python makes use of an internal hash function to map keys to indices in the underlying array.
  • Collision Resolution: Collisions (while two keys hash to the identical index) are handled using open addressing with quadratic probing.
• High-Level Interface: The dictionary API is easy and intuitive, with trustworthy syntax for not unusual operations, which include insertion (`dict[key] = value`), retrieval (`value= dict[key]`), and deletion (`del dict[key]`).
• Iterability: You can iterate over keys, values, and key-value pairs the use of strategies like `dict.keys()`, `dict.values()`, and `dict.items()`. This makes it easy to traverse and manage the contents of a dictionary.
• Comprehensions: Python helps dictionary comprehension, permitting the introduction of dictionaries in a concise and readable manner.
• Built-in Methods: Python dictionaries come with a wealthy set of strategies for not unusual duties, which include:
  • `get(key, default)`: Retrieve a value with a default if the key isn't a gift.
  • `update(other_dict)`: Update the dictionary with key-price pairs from every other dictionary.
  • `pop(key, default)`: Remove a key and go back to its value, or return a default if the important thing isn't always observed.
  • `clear()`: Remove all gadgets from the dictionary.
• Safety and Error Handling: When trying to access a non-existent key, a `KeyError` exception is raised, which can be handled gracefully with the use of `try-except` blocks.

Example

Output:

Name: Alina
Age: 30
City: Egypt
Updated Age: 31
City after deletion: Not Found
Name exists in hash table
Iterating over hash table:
name: Alina
age: 31
Squares dictionary: {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}