Today we will discuss what objects are, how objects relate to classes, and when you should use objects and classes in your Python applications. This guide provides practical examples to help you master these essential programming concepts.
In Python, a class is a blueprint and an object is a thing built from that blueprint. You define a class once with the class keyword to describe what data an entity holds (its attributes) and what it can do (its methods). You then create as many objects (also called instances) from that class as you need, each with its own independent data but sharing the same methods. The __init__ method runs automatically on creation to set up each object's starting attributes, and self inside every method refers to the specific object being acted on.
That's the one-paragraph summary an AI gives you. What it can't show you is the shape of the relationship: how one class fans out into many independent objects, where self actually points, and which of the many "attribute" concepts you should reach for. The animated diagram, the class-vs-object table, and the class-attribute gotcha below make those concrete.
One Class, Many Objects (at a Glance)
The single most important mental model in object-oriented programming is that one class produces many independent objects. The class is written once; each object carries its own copy of the instance attributes.
Notice that all three objects share the same cut_hair method (defined once in the class) but each holds its own hairlength. Cutting Max's hair leaves Bella and Rex untouched. That independence is the whole point.
Class vs. Object: The Distinction That Trips People Up
Beginners constantly blur these two words. Here is the precise difference:
| Class | Object (Instance) | |
|---|---|---|
| What it is | A blueprint / template | A concrete thing built from the blueprint |
| How many | Written once | Created as many times as you need |
| Example | class Dog: | mydog = Dog("Max") |
| Holds | Method definitions + class attributes | Its own instance attribute values |
| Lives | In your source code | In memory at runtime |
| Analogy | The architectural drawing | An actual house built from the drawing |
| When you use it | Define the shape and behavior once | Every time you need a new, independent entity |
If you can answer "am I describing the shape, or am I making one of the things?" you can always tell which you need.
What are Objects?
Objects are containers that hold a collection of attributes and functions. Think of objects as real-world entities that have properties (attributes) and can perform actions (methods). For example, you might create an application that tracks dogs.
Object Attributes (Properties)
For each dog you are tracking, you might create an object. Each dog object would have a collection of attributes like:
- Color: The dog's fur color
- Age: How old the dog is
- Breed: What type of dog it is
Object Methods (Actions)
Each dog object also has actions associated with it. For example, you might:
- Take the dog for a walk
- Cut the dog's hair
- Give the dog a bath
Working with Objects – Example
Let's assume you have imported a library that gives you access to an object called Dog. Here's how you would work with it:
# Create a new dog object named Max
mydog = Dog("Max")
# Set Max's breed to Chihuahua
mydog.breed = "Chihuahua"
# Shorten Max's hair length
mydog.cut_hair(2)
# Look at all of the attributes
print(mydog.name)
print(mydog.breed)
print(mydog.hairlength)
What are Classes?
As we discussed in the previous section, an object is a container that holds various attributes and functions. A class is the code that you use to create an object. Think of a class as a blueprint or template for creating objects.
Naming convention: Python's official style guide (PEP 8) recommends
CapWords(also called PascalCase) for class names, soDog,BankAccount,HttpClient. The lowercasedogyou may see in older tutorials works, but it makes classes harder to tell apart from variables and functions. We useDogthroughout.
Creating a Basic Class
To create a new class, we use the keyword class. Let's create a new class called Dog with two attributes: breed and name:
class Dog:
name = ""
breed = ""
# Create a new instance of this class
mydog = Dog()
# Set the name and breed
mydog.name = "Max"
mydog.breed = "Chihuahua"
# Print the dog's name
print(mydog.name)
Adding an Initialization Function
We probably want to treat the name of the dog as a unique attribute for each of our dogs and ensure that all dogs have names. To do this, we need to add an __init__ function that will be called every time we create a new dog object:
class Dog:
def __init__(self, name):
self.name = name
self.breed = ""
# Create a new dog object named Max
mydog = Dog("Max")
print(mydog.name) # Output: Max
Key Concept: The self.name assignment tells Python to store the name we passed in on this specific object. self always refers to the particular instance the method is running on, which is how Dog("Max") and Dog("Bella") end up with different names even though they share one class.
What Actually Happens When You Write Dog("Max")
Understanding the order of events removes most of the confusion around __init__ and self. Here is the sequence:
You never call __init__ yourself and you never pass self yourself. Python wires both up for you the moment you call the class.
Adding Methods to Classes
There are various things we will do with our dog. Perhaps we need to cut our dog's hair on occasion. Let's add a new variable called hairlength and a function called cut_hair:
class Dog:
breed = ""
# Add hairlength variable to the init function
def __init__(self, name):
self.name = name
self.hairlength = 10
# Declare the hair-cutting method
def cut_hair(self, howmuch):
self.hairlength = self.hairlength - howmuch
# Create a new Dog object named Max
mydog = Dog("Max")
# Call the method to cut the dog's hair
mydog.cut_hair(2)
# Print how long the hair length is now
print(mydog.hairlength) # Output: 8
As you can see above, we start by creating a new dog object, then we call the cut_hair method. The default value set in __init__ is a hairlength of 10. After cutting 2, the output from the print statement is 8.
The Class-Attribute Gotcha
Notice breed = "" sits directly in the class body, while self.name and self.hairlength are set inside __init__. That difference matters more than it looks:
| Instance attribute | Class attribute | |
|---|---|---|
| Where defined | self.x = ... inside a method | x = ... in the class body |
| Shared? | Unique to each object | Shared by all instances |
| Good for | Per-object state (name, hair length) | Shared constants (species, wheel count) |
| Common bug | — | A mutable default like tricks = [] is shared, so appending on one dog appends to all |
The safe rule: keep per-object data as instance attributes set in __init__, and reserve class attributes for values that genuinely should be the same for every instance. If you have ever been baffled by "why did adding a trick to one dog add it to all of them," a mutable class attribute is almost always the cause. This is closely related to how mutable vs. immutable objects behave in Python generally.
When Should You Use Objects and Classes?
Objects and classes allow you to break up your application into smaller, manageable pieces. These smaller pieces can be independently modified and tested. If you have done things right, you can modify one class without worrying about breaking another class.
But classes are not always the right tool. Python is deliberately multi-paradigm, and forcing a class where a plain function would do just adds ceremony. Use this quick decision guide:
| Situation | Reach for | Why |
|---|---|---|
| A short script or one-off transform | Plain functions | No lasting state to model; a class adds noise |
| Data plus the behavior that operates on it, bound together | A class | Encapsulation keeps related code in one place |
| Many independent things of the same shape | A class | One blueprint, many instances is exactly the class pattern |
| Just grouping related constants | A module or dataclass | Lighter weight than a full behavioral class |
| Modeling a real-world entity with changing state | A class | State (attributes) + actions (methods) map cleanly |
Key Benefits of Object-Oriented Programming
- Modularity: Break complex programs into smaller, manageable pieces
- Reusability: Create classes once and use them multiple times
- Maintainability: Easier to modify and debug isolated components
- Scalability: Better organization for larger teams and bigger projects
Best Practices
As your programs get bigger, and you work on larger teams, organization becomes increasingly important. Generally, the rule is that classes should only do one thing, and do that one thing really well. Think of each class as a mini-program within your main program.
Pro Tip: Single Responsibility Principle
For more advanced concepts, consider learning about the SOLID principles of object-oriented design, which provide guidelines for writing clean, maintainable object-oriented code. The first, the Single Responsibility Principle, is simply the "one class, one job" rule stated formally: a class should have only one reason to change.
Summary
Today we have discussed what objects and classes are in Python. We covered how to use objects, how to create classes, and how the two are inter-related. Understanding these concepts is fundamental to writing effective Python code that can scale as your applications grow.
Key Takeaways
- Objects are containers that hold attributes and methods
- Classes are blueprints for creating objects; one class produces many independent objects
__init__runs automatically on creation and sets up each object's starting attributesselfrefers to the specific object a method is acting on- Keep per-object data as instance attributes; reserve class attributes for genuinely shared values
- Object-oriented programming improves code organization and maintainability, but plain functions are often the better fit for short scripts
Keep Learning
- Python Mutable vs. Immutable Objects — why some objects can change in place and others can't
- How to Create Functions in Python 3 — methods are functions that live on a class
- Python Basics: Beginner's Guide to Programming Fundamentals — the groundwork under classes
- Error Handling in Python: try/except/else/finally — writing robust methods
- How to Understand Python Data Types — what your attributes actually hold