search
HomeBackend DevelopmentPython TutorialPython's underlying technology revealed: how to implement a hash table

Pythons underlying technology revealed: how to implement a hash table

Revealing the underlying technology of Python: How to implement a hash table

The hash table is a very common and important data structure in the computer field. It can efficiently store and Find a large number of key-value pairs. In Python, we can use hash tables using dictionaries, but few people understand its implementation details in depth. This article will reveal the underlying implementation technology of hash tables in Python and give specific code examples.

The core idea of ​​a hash table is to map keys into a fixed-size array through a hash function, rather than simply storing them in order. This can greatly speed up searches. Below we will introduce the implementation of hash table step by step.

  1. Hash function
    The hash function is a very critical part of the hash table, which maps keys to index positions in the array. A good hash function should be able to map keys evenly to different positions in the array to reduce the probability of collisions. In Python, we can use the hash() function to generate a hash value, but because the value it generates is too long, we generally need to perform a modulo operation on it to adapt it to the size of the array.

The following is an example of a simple hash function:

def hash_func(key, size):
    return hash(key) % size
  1. Implementation of hash table
    In Python, a hash table is created through a dictionary (dict ) object to achieve. The dictionary object uses a hash table internally to store key-value pairs. A simplest hash table can be implemented using arrays and linked lists.

First we define a hash table object, which contains an array and a linked list:

class HashTable:
    def __init__(self, size):
        self.size = size
        self.table = [[] for _ in range(size)]

Then we define the insertion and search methods:

    def insert(self, key, value):
        index = hash_func(key, self.size)
        for item in self.table[index]:
            if item[0] == key:
                item[1] = value
                return
        self.table[index].append([key, value])

    def get(self, key):
        index = hash_func(key, self.size)
        for item in self.table[index]:
            if item[0] == key:
                return item[1]
        raise KeyError(key)

In When inserting, we first obtain the index of the key through the hash function, and then find whether the key already exists in the linked list at the index position. If it exists, update the value; otherwise, insert a new key-value pair at the end of the linked list.

When searching, we also obtain the index of the key through the hash function, and then perform a linear search in the linked list at the index position. If the corresponding key-value pair is found, the value is returned; otherwise, a KeyError exception is thrown.

  1. Using Hash Table
    Now we can use the hash table we implemented. The following is a simple example:
hash_table = HashTable(10)
hash_table.insert("name", "Tom")
hash_table.insert("age", 20)
hash_table.insert("gender", "male")

print(hash_table.get("name"))  # 输出:Tom
print(hash_table.get("age"))  # 输出:20
print(hash_table.get("gender"))  # 输出:male
  1. Summary
    This article introduces the underlying implementation technology of hash tables in Python and gives specific code examples. A hash table is an efficient data structure that allows insertion and lookup operations in constant time. Mastering the implementation principles and related technologies of hash tables can help us better understand and use dictionary objects in Python.

I hope this article will help you understand the underlying implementation of hash tables. If you have any questions or suggestions, please feel free to communicate with us.

The above is the detailed content of Python's underlying technology revealed: how to implement a hash table. For more information, please follow other related articles on the PHP Chinese website!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
Python vs. C  : Understanding the Key DifferencesPython vs. C : Understanding the Key DifferencesApr 21, 2025 am 12:18 AM

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Python vs. C  : Which Language to Choose for Your Project?Python vs. C : Which Language to Choose for Your Project?Apr 21, 2025 am 12:17 AM

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

Reaching Your Python Goals: The Power of 2 Hours DailyReaching Your Python Goals: The Power of 2 Hours DailyApr 20, 2025 am 12:21 AM

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Maximizing 2 Hours: Effective Python Learning StrategiesMaximizing 2 Hours: Effective Python Learning StrategiesApr 20, 2025 am 12:20 AM

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Choosing Between Python and C  : The Right Language for YouChoosing Between Python and C : The Right Language for YouApr 20, 2025 am 12:20 AM

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python vs. C  : A Comparative Analysis of Programming LanguagesPython vs. C : A Comparative Analysis of Programming LanguagesApr 20, 2025 am 12:14 AM

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

2 Hours a Day: The Potential of Python Learning2 Hours a Day: The Potential of Python LearningApr 20, 2025 am 12:14 AM

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python vs. C  : Learning Curves and Ease of UsePython vs. C : Learning Curves and Ease of UseApr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SAP NetWeaver Server Adapter for Eclipse

SAP NetWeaver Server Adapter for Eclipse

Integrate Eclipse with SAP NetWeaver application server.

MantisBT

MantisBT

Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

PhpStorm Mac version

PhpStorm Mac version

The latest (2018.2.1) professional PHP integrated development tool