Creating objects in Python - Mechanics & Internals

Overview - Creating objects

What is it?

Creating objects means making individual things in a program that have their own data and behavior. In Python, objects are made from blueprints called classes. Each object can hold information and do actions defined by its class. This lets programs organize and manage complex data easily.

Why it matters

Without objects, programs would be flat and hard to manage when dealing with many related pieces of data. Objects let us group data and actions together, making code easier to understand, reuse, and change. This is like having many small machines each doing a specific job instead of one big confusing machine.

Where it fits

Before learning to create objects, you should know about variables, data types, and functions in Python. After this, you can learn about more advanced object concepts like inheritance, polymorphism, and design patterns.

Mental Model

Core Idea

An object is a self-contained bundle of data and actions created from a class blueprint.

Think of it like...

Creating an object is like building a specific car from a factory blueprint. The blueprint (class) defines the car's design and features, but each car (object) is a separate, real thing you can drive and customize.

Class (Blueprint)
┌───────────────┐
│  Attributes   │
│  Methods      │
└──────┬────────┘
       │
       ▼
Object (Car)
┌───────────────┐
│  Data values  │
│  Can do tasks │
└───────────────┘

Build-Up - 7 Steps

1

Foundation

Understanding classes as blueprints

Concept: Classes define the structure and behavior for objects.

In Python, a class is created using the 'class' keyword. It groups variables (called attributes) and functions (called methods) that describe what an object will have and do. Example: class Dog: def bark(self): print('Woof!')

Result

You have a blueprint named Dog that can make dogs that bark.

Knowing classes are blueprints helps you see objects as specific examples made from these plans.

2

Foundation

Making objects from classes

Concept: Objects are created by calling the class like a function.

To create an object, you write the class name followed by parentheses. This calls the class's constructor to make a new object. Example: my_dog = Dog() my_dog.bark() # Calls the bark method

Result

Output: Woof!

Understanding that calling a class creates a new object clarifies how programs make many individual things from one blueprint.

3

Intermediate

Using attributes to store data

Concept: Objects can hold their own data in attributes.

Attributes are variables tied to an object. They store information unique to that object. Example: class Dog: def __init__(self, name): self.name = name def bark(self): print(f'{self.name} says Woof!') my_dog = Dog('Buddy') my_dog.bark()

Result

Output: Buddy says Woof!

Knowing objects hold their own data explains how each object can be different even if made from the same class.

4

Intermediate

The __init__ method as constructor

Concept: __init__ sets up new objects with initial data.

The __init__ method runs automatically when an object is created. It initializes attributes. Example: class Cat: def __init__(self, color): self.color = color def meow(self): print(f'A {self.color} cat says Meow!') my_cat = Cat('black') my_cat.meow()

Result

Output: A black cat says Meow!

Understanding __init__ as the setup step helps you control what data every new object starts with.

5

Intermediate

Objects have unique identities

🤔Before reading on: Do you think two objects with the same data are the same object? Commit to your answer.

Concept: Each object is a distinct entity, even if data matches another object.

Two objects can have identical attributes but are still separate in memory. Example: class Point: def __init__(self, x, y): self.x = x self.y = y p1 = Point(1, 2) p2 = Point(1, 2) print(p1 == p2) # False by default print(p1 is p2) # False They are different objects with the same data.

Result

Output: False False

Knowing objects have unique identities prevents confusion when comparing objects and helps understand object references.

6

Advanced

Customizing object creation with __new__

🤔Before reading on: Do you think __new__ is commonly used to create objects? Commit to your answer.

Concept: __new__ controls the actual creation of an object before __init__ runs.

Python calls __new__ to create a new object instance. It returns the new object, then __init__ initializes it. Example: class MyClass: def __new__(cls): print('Creating instance') return super().__new__(cls) def __init__(self): print('Initializing instance') obj = MyClass()

Result

Output: Creating instance Initializing instance

Understanding __new__ reveals the two-step process of object creation and initialization, useful for advanced customization.

7

Expert

Memory and references behind objects

🤔Before reading on: Do you think objects are copied when assigned to new variables? Commit to your answer.

Concept: Variables hold references to objects in memory, not copies of the objects themselves.

When you assign an object to a new variable, both variables point to the same object. Example: a = Dog('Rex') b = a b.name = 'Max' print(a.name) # Output: Max Changing b changes a because both refer to the same object.

Result

Output: Max

Knowing variables are references prevents bugs from unexpected shared changes and clarifies how Python manages objects.

Under the Hood

When you create an object, Python allocates memory for it and stores its attributes there. The variable you assign the object to holds a reference (like an address) to that memory. Methods are functions stored in the class, and when called on an object, Python passes the object itself as the first argument (self). The __new__ method creates the object in memory, then __init__ sets up its data.

Why designed this way?

Python separates object creation (__new__) and initialization (__init__) to allow advanced control over how objects are made. Using references instead of copying objects saves memory and improves performance. This design balances flexibility, efficiency, and simplicity.

Create object call
   │
   ▼
┌───────────┐
│  __new__  │  ← Allocates memory
└────┬──────┘
     │
     ▼
┌───────────┐
│  __init__ │  ← Sets attributes
└────┬──────┘
     │
     ▼
┌───────────┐
│  Object   │  ← Stored in memory
└───────────┘
     ▲
     │
Variable holds reference

Myth Busters - 4 Common Misconceptions

Quick: Does creating two objects with the same data make them the same object? Commit yes or no.

Common Belief: Two objects with the same data are identical and interchangeable.

Reality: Each object is unique in memory, even if data matches. They are separate entities.

Why it matters: Assuming objects are the same can cause bugs when modifying one unexpectedly affects the other.

Quick: When assigning an object to a new variable, is a copy made? Commit yes or no.

Common Belief: Assigning an object to another variable creates a new, independent copy.

Reality: Assignment copies the reference, so both variables point to the same object.

Why it matters: Misunderstanding this leads to confusing bugs where changing one variable changes another unexpectedly.

Quick: Does __init__ create the object in memory? Commit yes or no.

Common Belief: __init__ is responsible for creating the object in memory.

Reality: __init__ only initializes the object after __new__ creates it.

Why it matters: Confusing these methods can cause errors when customizing object creation.

Quick: Can you create an object without a class in Python? Commit yes or no.

Common Belief: Objects can be created without defining a class.

Reality: In Python, all user-defined objects come from classes; classes are required.

Why it matters: Thinking objects exist without classes can confuse the core concept of object-oriented programming.

Expert Zone

1

Mutable objects can be changed after creation, but immutable objects cannot; understanding this affects how you design classes.

2

The __slots__ attribute can optimize memory by restricting allowed attributes, but it limits flexibility.

3

Metaclasses control class creation itself, allowing deep customization beyond normal object creation.

When NOT to use

Creating objects is not ideal for very simple data storage where tuples or dictionaries suffice. For performance-critical code, using lightweight data structures or libraries like namedtuple or dataclasses may be better.

Production Patterns

In real systems, objects represent entities like users or products. Factories or builders create objects with complex setup. Dependency injection manages object creation for testing and flexibility. Immutable objects improve safety in concurrent programs.

Connections

Functional Programming

Contrasts with object creation by emphasizing pure functions and immutable data instead of mutable objects.

Knowing object creation helps understand when to use mutable state versus when to prefer immutable data and functions.

Database Records

Objects often map to database rows, linking programming data with stored data.

Understanding objects clarifies how programs represent and manipulate real-world data stored in databases.

Manufacturing Processes

Object creation parallels making physical products from blueprints in factories.

Seeing object creation as manufacturing helps grasp the separation of design (class) and product (object).

Common Pitfalls

#1 Changing one object affects another unexpectedly.

Wrong approach:

a = Dog('Rex')
b = a
b.name = 'Max'
print(a.name) # Output: Max

Correct approach:

a = Dog('Rex')
b = Dog('Rex')
b.name = 'Max'
print(a.name) # Output: Rex

Root cause:

Assigning one object to another copies the reference, not the object itself.

#2 Forgetting to use self to access attributes inside methods.

Wrong approach:

class Dog:
def __init__(name):
name = name
def bark():
print(name + ' says Woof!')

Correct approach:

class Dog:
def __init__(self, name):
self.name = name
def bark(self):
print(self.name + ' says Woof!')

Root cause:

Not using self means attributes are not stored on the object, causing errors.

#3 Trying to create an object without calling the class.

Wrong approach:

my_dog = Dog # Missing parentheses
my_dog.bark()

Correct approach:

my_dog = Dog()
my_dog.bark()

Root cause:

Classes must be called with parentheses to create objects; missing them assigns the class itself.

Key Takeaways

Objects are created from classes, which act as blueprints defining their data and behavior.

Creating an object involves calling the class, which runs __new__ to allocate memory and __init__ to set up data.

Each object is unique in memory, even if it has the same data as another object.

Variables hold references to objects, so assigning one variable to another does not copy the object.

Understanding object creation is essential for organizing complex programs and managing data effectively.