An Introduction to Design Patterns in Python

Design patterns are program templates used to prevent subtle issues and recurring design problems that cause major complications in the software development process. This article will explore design patterns, their different categories, their examples, and how to use these resources.

What is a design pattern?

Design patterns describe how a program is designed, acting as a repeatable solution to common problems encountered during software development. These patterns provide a standardized approach to design, enabling developers to solve complex design challenges more efficiently and effectively. Design patterns help do the following: 

• Facilitate code reuse

• Improve code readability

• Enhance the flexibility of solutions

• Enhance communication throughout the reuse process

• Make the software development process more manageable and less prone to errors

3 types of design patterns

You can group design patterns into three main areas: creational, structural, and behavioral. While individual patterns will vary, the three categories divide different types of programs and functions. When learning about design patterns, you’ll often come across terms like object and class. Within a program, a class is the “blueprint” for object creation and function, while objects are class instances, meaning they are an entity within the class. The three categories of design patterns are as follows.

• Creational patterns focus on creating objects and classes within a program. This type of design pattern is used for functions like creating an object, family of objects, class of object, subclass of objects, and so on.

• Structural patterns focus on object design, storage, and interfacing. With structural patterns, you can find guidance on defining and implementing an object, details about object structure and composition, storage and access information, and how to move between objects within systems.

• Behavioral patterns include various functions focused on how a program operates. You can use this type of design pattern for aspects of your program, such as when the program fulfills a request, how it accesses data, dependencies between objects, and an algorithm model itself.

Design patterns in Python

Python is an object-oriented programming language used by developers in many different areas. This language is dynamic and reusable, making it a great choice for professionals working with high-level data structures in collaborative fields. Because of its wide applications, using design patterns is essential for Python program development. 

Read more: What Is Python Used For? A Beginner’s Guide

Creational patterns in Python

You can use creational patterns to define how a program instantiates objects. This keeps your system independent of object and class creation, simplifying the process and allowing it to change dynamically.

Builder pattern

The builder pattern defines how to build complex objects from simple objects step-by-step. This pattern is useful for building objects with various compositions of the same elements. It allows for flexibility, so the same construction process can meet several specifications you might need.

Singleton

The singleton pattern ensures that you only have one instance of a class, meaning that instead of creating a new class, the program will reference the original. It’s useful for controlling access to resources such as database connections or configuration settings where you should have exactly one object.

Factory method pattern

The factory method pattern offers an interface for creating objects without explicitly defining the type of object needed. Instead, you provide input about the type of object you need, and classes within the program determine how to create it best. This creates a more dynamic environment so that you don’t need to update classes whenever you need to modify an object. This pattern is popularly used for database connection objects, as you can provide information about the type of database you need rather than the actual database, making it easier to switch between them.

Read more: 5 Types of Programming Languages

Structural patterns in Python

Your chosen structural pattern will dictate how objects build together to form a more comprehensive structure. You can find many to choose from, including:

Adapter

The adapter pattern converts objects or classes of a specific interface to be compatible with another interface. It’s especially useful when integrating new features or libraries into existing systems without altering them.

Decorator

The decorator pattern allows you to add new functionality to objects flexibly. This pattern is particularly beneficial for adding or extending features to objects dynamically, as it focuses on objects specifically without altering the entire class. For example, you might use a decorator to extend the functionality of a website, such as adding specific user-friendly features without altering the rest of the website's behavior.

Proxy

The proxy pattern provides a placeholder object representing the original object, similar to a middleman. You use this pattern when you don’t want the user to have direct access to the object, such as when operating on a large object that’s resource-intensive to create or when you need to implement security measures. It can also control the object’s access, adding a layer of protection, for instance, checking if the current user has access rights for the object. Proxy objects are also useful if you are accessing data from a remote location and when loading the full object may take a long time.

Behavioral patterns in Python

When you use behavioral patterns, you are building the structure for how objects communicate with one another in your program. Common ones you might use include:

Chain of responsibility

The chain of responsibility pattern determines a chain of receivers for a particular request. This pattern decouples senders and receivers, allowing several different objects to handle the request as needed by passing it along the chain. It’s useful for scenarios where you can handle a request in various ways but don’t know the handler in advance. 

Command

The command pattern converts requests to objects, decoupling the action and receiver so the program can store a request until needed. This allows the program to queue or log requests and is valuable in scenarios requiring direct operations, scheduled tasks, or commands to execute in response to specific events. For example, a program could store a request until activated by a particular scenario.

Interpreter

The interpreter pattern interprets grammar so language text can be processed. It’s used for designing specialized language interpreters, often applied in the domain of compilers and parsers.

Iterator

The iterator pattern designs a way for you to find objects within your program without exposing the internal mechanisms of the program. You can choose different mechanisms to jump to particular objects, such as finding the next, first, and last elements.

How to start learning design patterns

Learning design patterns is an important step in becoming a proficient software developer. Design patterns help avoid common problems in software design and architecture, enhancing your coding efficiency. 

• Understand the basics: Start with a strong understanding of basic concepts such as classes, objects, methods, attributes, inheritance, and encapsulation.

• Familiarize yourself with the core patterns: Learn about the core creational, structural, and behavioral patterns. 

• Use online resources: Look at practical examples of each design pattern, such as those available on online coding resources. Seeing how professionals implement a pattern in a real-world scenario can help answer questions and solidify your understanding. 

• Implement patterns yourself: After studying examples, try to implement design patterns in your own code. 

Learn on Coursera.

You can enhance your Python and design patterns knowledge on Coursera with exciting courses, Specializations, and Professional Certificates available. To start learning, consider the Programming for Everybody Specialization, offered by the University of Michigan. You can choose the Design Patterns course by the University of Alberta on Coursera for a more specialized introduction to design patterns. Upon completing either program, gain a shareable Professional Certificate to include in your resume, CV, or LinkedIn profile.