Python OOP Hands-on Practice: Development of a Simple Combat Game
Python Game Development with OOP principles
Tech Stack
Python 3, OOP
VsCode, Jupyter, GitHub
Project
Development of a text-based combat game using the Python OOP principles and basic game design principles
Benefits
Object Oriented Programming helps Data specialists write cleaner, more maintainable, and scalable analysis pipelines.
Goals
Enhancing my Python coding skills.
This is an article which topic, OOP in Python, is related to a notebook project. To find this notebook and much more code and explanations of it go to the Github repo link here.
Object-Oriented Programming (OOP) is a fundamental paradigm in Python that allows developers to structure their code efficiently. For data analysts like me, understanding OOP is essential because it enables us to write modular, reusable, and maintainable code. When working with complex datasets, OOP helps organize operations logically and streamlines the implementation of analytics workflows, preprocessing pipelines, and custom visualizations.
The four pillars of OOP—Encapsulation, Abstraction, Inheritance, and Polymorphism—provide a structured way to manage data and behavior in a scalable manner. Here's a quick analogy to grasp these concepts:
Encapsulation: Think of a car. You don't need to know the intricate details of how the engine works to drive it. The functionalities are encapsulated under the hood, and you interact with it using the steering wheel and pedals.
Abstraction: A TV remote abstracts the complexity of circuit boards and signals. You press a button to change the channel without needing to understand the electronics behind it.
Inheritance: A child inherits traits from their parents, much like a class in Python can inherit behavior from another class.
Polymorphism: A single function, like ‘run,’ can be used for both humans and animals, but their actual implementation differs.
In this notebook project, I applied these principles in a simple game development context. By implementing an OOP structure, I learned how different aspects of OOP contribute to cleaner, more efficient code.
Class Objects and Inheritance
In Python, a class is a blueprint for creating objects, and an object is an instance of a class. When we define a class, we describe attributes (data) and methods (functions) that the object will have.
Generic Example:
In my project, I created different classes for Player, Enemy, Dragon, and Soldier. Instead of rewriting code for every new enemy type, I used inheritance to define a general Enemy class and then created specialized versions for specific enemies.
Why is inheritance important? It helps in refactoring code, reducing redundancy, and promoting reusability. Instead of duplicating common attributes and methods, we define them once in a base class and extend them when necessary.
Encapsulation
Encapsulation is the practice of hiding the internal workings of an object while exposing only what’s necessary. It prevents direct access to certain properties, reducing the risk of unintended modifications.
How It Complements Other OOP Pillars
Encapsulation vs. Inheritance: Inheritance allows sharing attributes/methods across classes, but encapsulation keeps certain details private.
Encapsulation vs. Abstraction: Abstraction hides complexity at a high level, while encapsulation enforces strict data protection within a class.
Encapsulation can be abstract to grasp at first. Here’s a simple analogy: A coffee machine encapsulates the brewing process. You only press a button to get coffee, but the inner workings remain hidden.
Example from the Notebook:
Here, __health is private, preventing external modifications. Instead, we use a method (take_damage()) to update it safely.
Abstraction
Abstraction focuses on hiding complex details and exposing only necessary functionalities.
Example:
From my project’s Refactoring: Abstraction section, I moved redundant logic into a base class to simplify combat mechanics. Instead of handling different attack methods separately, I created an abstraction layer in the Enemy class to unify their behavior.
Analogy:
A restaurant menu abstracts the complexity of food preparation. You select a dish without worrying about how it’s made.
3 Tips for Effective Abstraction:
Identify common behavior: Find repetitive logic and move it into a superclass (a parent class).
Use abstract base classes (ABC module in Python) when designing base classes.
Keep interfaces clean: Expose only necessary methods and hide implementation details.
Polymorphism
Polymorphism wasn’t explicitly covered in this project, but it’s a crucial OOP concept. It allows different objects to be treated the same way, even if they behave differently.
Analogy:
A USB port is polymorphic—you can plug in a mouse, a keyboard, or a flash drive, and it will work because they share a common interface.
Example:
Here, speak() works on different objects without knowing their exact types.
OOP Use Cases in Data Analysis
OOP is highly useful in data analysis, especially in:
Exploratory Data Analysis (EDA): Creating reusable classes for data preprocessing (e.g., DataCleaner class to handle missing values and transformations).
Visualization: Abstracting complex matplotlib/seaborn plots into a Plotter class.
Machine Learning Pipelines: Structuring models with OOP (e.g., creating a ModelTrainer class that handles training and evaluation across different algorithms).
For any data specialist, mastering OOP means writing cleaner, more maintainable, and scalable analysis pipelines.
Conclusion
This project, though centered on a simple game, provided a practical way to understand three pillars of OOP—Inheritance, Encapsulation, and Abstraction—by applying them to a real problem.
Looking ahead, I plan to explore the next big topic: Inheritance vs. Composition. While inheritance allows extending functionality from a base class, composition lets us build objects from different components dynamically. Understanding when to use one over the other is crucial for writing better Python applications.
Stay tuned for my next notebooks where I dive into this topic further!
💡 If you want to explore other topics like SQL, check my full SQLAlchemy journey in my GitHub repository.