Effortlessly Master Python Hash Tables: A Beginner's Guide

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Build a Hash Table in Python With TDD

Invented over half a century ago, the hash table is a classic data structure that has been fundamental to programming. To this day, it helps solve many real-life problems, such as indexing database tables, caching computed values, or implementing sets. It often comes up in job interviews, and Python uses hash tables all over the place to make name lookups almost instantaneous.

Even though Python comes with its own hash table called dict, it can be helpful to understand how hash tables work behind the curtain. A coding assessment may even task you with building one. This tutorial will walk you through the steps of implementing a hash table from scratch as if there were none in Python. Along the way, you’ll face a few challenges that’ll introduce important concepts and give you an idea of why hash tables are so fast.

In addition to this, you’ll get a hands-on crash course in test-driven development (TDD) and will actively practice it while building your hash table in a step-by-step fashion. You’re not required to have any prior experience with TDD, but at the same time, you won’t get bored even if you do!

Get to Know the Hash Table Data Structure

Hash Table vs Dictionary

Python’s built-in dict is essentially a hash table. It uses a hashing function to convert keys into slots in an underlying array, making lookups almost instantaneous and providing a constant-time average complexity for operations such as access, insertion, and deletion. So, what is the difference between a hash table and a dictionary in Python? In practical terms, there is little to no difference. However, this tutorial will help you understand how a hash table works and how to implement one yourself.

Hash Table: An Array With a Hash Function

A hash table is essentially an array of n slots that can hold key-value pairs. The main idea is to use a hash function to convert the key into an index in the array. The hash function takes in the key as input and returns a hash code, which is an integer representation of the key. The hash code is then used to calculate the index in the array where the value should be stored. In this tutorial, you will learn how to implement a hash table in Python.

Understand the Hash Function

The hash function is a crucial component of a hash table. It determines how the keys are mapped to unique indices in the underlying array. Python provides a built-in hash() function, which returns a hash code for objects. In this section, you’ll examine Python’s built-in hash() function, dive deeper into its implementation, and explore the properties of a hash function.

Examine Python’s Built-in hash()

Python’s built-in hash() function is a powerful tool that generates a hash code for any object. It takes an object as input and returns an integer. The hash code is based on the object’s contents and properties. In this section, you will explore the hash() function in more detail and understand how it works behind the scenes.

Dive Deeper Into Python’s hash()

The hash() function in Python can generate hash codes for various types of objects, including integers, floats, strings, tuples, and even custom objects. It is important to understand how the hash() function behaves for different types of objects, as this knowledge will be useful when implementing your own hash function for a hash table.

Identify Hash Function Properties

An ideal hash function should have several properties. It should generate unique hash codes for different objects whenever possible, minimize collisions, produce consistent results for the same input, and distribute the hash codes evenly across the range of possible indices in the hash table. Understanding these properties will help you design an efficient hash function for your hash table implementation.

Compare an Object’s Identity With Its Hash

In Python, an object’s identity is determined by its memory address, which you can obtain using the id() function. The identity of an object remains constant throughout its lifetime, even if its contents change. On the other hand, the hash code of an object can change if its contents change. In this section, you will compare an object’s identity with its hash to understand how they relate to each other.

Make Your Own Hash Function

Building your own hash function can be a challenging but rewarding task. In this section, you will learn about some common techniques used to design hash functions and implement a simple hash function from scratch. You will then use this hash function to map keys to indices in your hash table.

Build a Hash Table Prototype in Python With TDD

In this section, you will take a crash course in test-driven development (TDD) and actively practice it while building a prototype of a hash table in Python. TDD is a software development process that emphasizes writing automated tests before writing the actual code. By following this approach, you can ensure that your code is correct and maintainable from the start.

Take a Crash Course in Test-Driven Development

Before diving into building your hash table, it’s important to understand the basics of test-driven development (TDD). This section will introduce you to the main concepts of TDD and explain why it is a valuable approach to software development.

Define a Custom HashTable Class

To begin building your hash table, you will define a custom HashTable class. This class will be responsible for managing the underlying array of slots, handling collisions, and supporting various hash table operations. You will write the tests for this class first and then implement the functionality step by step.

Insert a Key-Value Pair

The first operation you will implement in your hash table is inserting a key-value pair. Given a key and a value, the hash table should store the value at the appropriate index in the underlying array. If there is already a value stored at that index, you will need to handle collisions appropriately.

Find a Value by Key

Once you have inserted key-value pairs into the hash table, you will implement the functionality to retrieve a value by its key. This operation should return the corresponding value if the key exists in the hash table, or an appropriate error message if the key is not found.

Delete a Key-Value Pair

The ability to delete a key-value pair from the hash table is an essential operation. In this section, you will implement the functionality to remove a key-value pair from the hash table given its key. If the key is not found, an appropriate error message should be returned.

Update the Value of an Existing Pair

In addition to inserting new key-value pairs and deleting existing ones, it is important to be able to update the value of an existing pair in the hash table. This operation will allow you to change the value associated with a specific key without altering the other pairs in the hash table.

Get the Key-Value Pairs

To provide a way to access all the key-value pairs stored in the hash table, you will implement the functionality to retrieve them all at once. This operation should return a list of tuples, where each tuple consists of a key-value pair.

Use Defensive Copying

To ensure that the key-value pairs returned by the get_pairs() method cannot be modified externally, you will implement defensive copying. This technique creates a copy of each key-value pair before adding it to the list, preventing unintended modifications to the original pairs.

Get the Keys and Values

In addition to retrieving all the key-value pairs, it can be useful to access just the keys or just the values stored in the hash table. In this section, you will implement the functionality to retrieve all the keys and all the values separately.

Report the Hash Table’s Length

Knowing the number of key-value pairs stored in the hash table can be helpful in various situations. In this section, you will implement the functionality to report the length of the hash table, which represents the number of key-value pairs currently stored.

Make the Hash Table Iterable

To make your hash table more convenient to work with, you will implement the functionality to make it iterable. This will allow you to iterate over the key-value pairs stored in the hash table using a for loop or other iterable operations.

Represent the Hash Table in Text

Sometimes it’s useful to visualize a complex data structure like a hash table in a human-readable format. In this section, you will implement the functionality to represent your hash table as a string, providing a clear visual representation of its contents.

Test the Equality of Hash Tables

To compare two hash tables for equality, you will implement the functionality to check if two hash tables contain the same key-value pairs. This will allow you to verify if two hash tables are equal, even if they have a different internal structure.

Resolve Hash Code Collisions

When using a hash function to map keys to indices in the underlying array, there is a possibility of two different keys producing the same hash code. This is known as a hash code collision. In this section, you will learn about different techniques to resolve hash code collisions and ensure that all your keys are mapped correctly.

Find Collided Keys Through Linear Probing

One way to handle hash code collisions is to use a technique called linear probing. In this approach, if a slot in the underlying array is already occupied, you search for the next available slot in a linear manner. This section will explain how linear probing works and how to implement it in your hash table.

Use Linear Probing in the HashTable Class

To resolve hash code collisions using linear probing, you will modify your HashTable class to handle this technique. This section will guide you through the necessary changes and explain how to integrate linear probing into your hash table implementation.

Let the Hash Table Resize Automatically

As the number of key-value pairs in a hash table increases, the likelihood of hash code collisions also increases. To avoid excessive collisions and maintain a good load factor, it’s important to resize the hash table when necessary. In this section, you will implement the functionality to automatically resize your hash table when it reaches a certain load factor.

Calculate the Load Factor

The load factor is a metric used to determine the level of fullness of a hash table. It is calculated by dividing the number of key-value pairs in the hash table by the number of slots in the underlying array. In this section, you will implement the functionality to calculate and report the load factor of your hash table.

Isolate Collided Keys With Separate Chaining

Another technique to resolve hash code collisions is separate chaining. In this approach, if two keys produce the same hash code, they are stored in a linked list in the same slot. This section will explain how separate chaining works and how to implement it in your hash table.

Retain Insertion Order in a Hash Table

By default, the dict class in Python does not retain the order in which key-value pairs are inserted. However, in certain scenarios, it can be useful to preserve the insertion order. In this section, you will learn how to modify your hash table implementation to retain the insertion order of key-value pairs.

Use Hashable Keys

In a hash table, the keys must be hashable, which means they must have an associated hash code and support equality comparisons. In this section, you will explore the concepts of hashability and immutability and understand the importance of these properties in hash table keys.

Hashability vs Immutability

In Python, hashability and immutability are closely related concepts. An object is considered hashable if it has a hash code and supports equality comparisons. Immutability refers to the inability of an object to be changed after it is created. Understanding the difference between hashability and immutability is important when dealing with hash table keys.

The Hash-Equal Contract

The hash-equal contract is a fundamental principle of hash table implementations. It establishes a relationship between the hash code and the equality of objects. In this section, you will learn about the hash-equal contract and its implications in Python.

Conclusion

In this tutorial, you’ve learned about the hash table data structure and how to build your own hash table in Python using test-driven development (TDD). You’ve explored various concepts related to hash functions, hash code collisions, and hashability. By completing the step-by-step implementation of the hash table, you now have a solid understanding of how hash tables work behind the scenes in Python.