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Hash Table Implementation in Python: Explained Effortlessly

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Building a Hash Table Implementation in Python

Introduction

In this tutorial, we will explore the implementation of a hash table in Python. A hash table is a classic data structure that is widely used in programming to solve various problems, such as indexing database tables and implementing sets. Although Python has its own built-in hash table called dict, it is beneficial to understand how hash tables work behind the scenes. Additionally, knowing how to build a hash table can be useful for coding assessments and job interviews.

Throughout this tutorial, we will take a step-by-step approach to building a hash table from scratch. Along the way, we will encounter various challenges that will introduce important concepts and demonstrate why hash tables are known for their fast performance. To ensure code quality and reliability, we will also follow a test-driven development (TDD) approach.

Table of Contents

  1. Get to Know the Hash Table Data Structure
    • Hash Table vs Dictionary
    • Hash Table: An Array With a Hash Function
  2. Understand the Hash Function
    • Examine Python’s Built-in hash()
    • Dive Deeper Into Python’s hash()
    • Identify Hash Function Properties
    • Compare an Object’s Identity With Its Hash
    • Make Your Own Hash Function
  3. Build a Hash Table Prototype in Python With TDD
    • Take a Crash Course in Test-Driven Development
    • Define a Custom HashTable Class
    • Insert a Key-Value Pair
    • Find a Value by Key
    • Delete a Key-Value Pair
    • Update the Value of an Existing Pair
    • Get the Key-Value Pairs
    • Use Defensive Copying
    • Get the Keys and Values
    • Report the Hash Table’s Length
    • Make the Hash Table Iterable
    • Represent the Hash Table in Text
    • Test the Equality of Hash Tables
  4. Resolve Hash Code Collisions
    • Find Collided Keys Through Linear Probing
    • Use Linear Probing in the HashTable Class
    • Let the Hash Table Resize Automatically
    • Calculate the Load Factor
    • Isolate Collided Keys With Separate Chaining
  5. Retain Insertion Order in a Hash Table
  6. Use Hashable Keys
    • Hashability vs Immutability
    • The Hash-Equal Contract
  7. Conclusion

Get to Know the Hash Table Data Structure

Hash Table vs Dictionary

The first step in understanding hash tables is to differentiate them from dictionaries. In Python, dict is a built-in hash table implementation that allows us to store and retrieve key-value pairs efficiently. While both hash tables and dictionaries have similar usage patterns, they differ in terms of implementation details.

Hash Table: An Array With a Hash Function

A hash table can be viewed as an underlying array coupled with a hash function. The hash function maps keys to indices in the array, allowing fast access to stored values. We will dive deeper into the hash function and its properties in the subsequent sections.

Understand the Hash Function

To build a hash table, it is essential to understand the hash function and its role in the structure. In this section, we will examine Python’s built-in hash() function, explore its inner workings, identify important properties of hash functions, and even create our own hash function.

Examine Python’s Built-in hash()

Python provides a built-in hash() function that returns a hash value for an object. This hash value can then be used as an index in a hash table. We will analyze how hash() generates these hash values and what factors affect its output.

Dive Deeper Into Python’s hash()

This subsection will explore the implementation details of Python’s hash() function and how it handles various data types. Understanding these details will provide insights into how hash values are generated for different objects.

Identify Hash Function Properties

A good hash function exhibits several desirable properties that contribute to the efficiency of a hash table. We will identify and discuss these properties in order to build an optimized hash function.

Compare an Object’s Identity With Its Hash

In this section, we will compare the identity of objects with their respective hash values to understand how hash codes are used in practice.

Make Your Own Hash Function

Building upon the knowledge gained so far, we will create our own hash function from scratch. This exercise will give us a deeper understanding of how a hash table operates.

Build a Hash Table Prototype in Python With TDD

At this point, we will transition to implementing a working hash table in Python. To ensure the reliability and correctness of our implementation, we will follow a test-driven development approach. We will cover various functionalities of a hash table and write tests for each feature before proceeding to the implementation.

The functionalities we will cover include inserting a key-value pair, finding a value by key, deleting a key-value pair, updating the value of an existing pair, retrieving key-value pairs, defensive copying, getting keys and values, reporting the hash table’s length, making the hash table iterable, representing the hash table in text, and testing the equality of hash tables.

Resolve Hash Code Collisions

Hash collisions occur when different keys generate the same hash code. Handling collisions is an important aspect of implementing a hash table. In this section, we will explore two common techniques for resolving collisions: linear probing and separate chaining.

Find Collided Keys Through Linear Probing

When a collision occurs, linear probing involves searching for an available slot in the hash table by incrementing the index. We will learn how to identify collided keys using this method.

Use Linear Probing in the HashTable Class

Building upon our existing prototype, we will modify our hash table implementation to incorporate linear probing for handling collisions.

Let the Hash Table Resize Automatically

As more key-value pairs are added, a hash table can become full, resulting in decreased performance. To mitigate this, we will implement an automatic resizing mechanism for our hash table to accommodate additional entries efficiently.

Calculate the Load Factor

The load factor is a measure of how full a hash table is. It helps determine when to resize the hash table. We will calculate the load factor and utilize it in our automatic resizing implementation.

Isolate Collided Keys With Separate Chaining

In separate chaining, each bucket of the hash table can store multiple key-value pairs. We will explore how this technique allows us to isolate collided keys and handle them independently.

Retain Insertion Order in a Hash Table

In some scenarios, it is important to retain the order in which key-value pairs are inserted into a hash table. We will discuss the techniques and modifications required to achieve this behavior.

Use Hashable Keys

To be a valid key in a hash table, an object must be hashable. We will explore the concepts of hashability and immutability and discuss the hash-equal contract that defines the hashing behavior of objects.

Conclusion

In conclusion, this tutorial has provided a comprehensive guide to implementing a hash table in Python. We have discussed the differences between a hash table and a dictionary, covered the workings of the hash function, built a hash table prototype using a test-driven development approach, resolved hash code collisions, addressed the issue of resizing, retained insertion order, and explored the use of hashable keys.

By understanding the inner workings of a hash table, you will be better equipped to use dictionaries effectively and tackle coding challenges and job interviews.

References