Object initialization flow in Python - Deep Dive

Overview - Object initialization flow

What is it?

Object initialization flow is the process Python follows when creating a new object from a class. It involves calling special methods that set up the object's initial state and prepare it for use. This flow ensures that every new object starts with the right values and behaviors. Understanding this helps you control how objects are built and customized.

Why it matters

Without a clear object initialization flow, objects might start with missing or wrong information, causing bugs and unpredictable behavior. This concept solves the problem of setting up objects consistently and safely. It lets programmers create objects that are ready to work immediately, making programs more reliable and easier to maintain.

Where it fits

Before learning object initialization flow, you should understand basic classes and objects in Python. After mastering this, you can explore advanced topics like inheritance, custom constructors, and object lifecycle management.

Mental Model

Core Idea

Object initialization flow is the step-by-step process Python uses to create and prepare a new object by calling special methods in a specific order.

Think of it like...

It's like baking a cake: first, you prepare the batter (allocate the object), then you bake it (initialize it with ingredients), and finally, you decorate it (customize the object). Each step must happen in order for the cake to be ready to serve.

┌───────────────┐
│ Class called  │
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ __new__ method│  (creates the empty object)
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ __init__ method│ (fills object with data)
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ Ready object  │
└───────────────┘

Build-Up - 7 Steps

1

Foundation

What is object creation in Python

Concept: Introducing how Python creates objects from classes.

When you write code like obj = MyClass(), Python creates a new object of type MyClass. This process involves two main steps: allocating memory for the object and setting up its initial state.

Result

A new object of MyClass is created and stored in obj.

Understanding that object creation is more than just calling a class name helps you see why special methods control this process.

2

Foundation

Role of __init__ method

Concept: Learning about the __init__ method that sets initial values.

The __init__ method runs right after the object is created. It receives the new object as self and any extra arguments you pass. Inside __init__, you assign values to the object's attributes to prepare it for use.

Result

The object has its attributes set as defined in __init__.

Knowing __init__ is not creating the object but initializing it clarifies common confusion.

3

Intermediate

Understanding the __new__ method

🤔Before reading on: do you think __init__ creates the object or just initializes it? Commit to your answer.

Concept: Introducing __new__, the method that actually creates the object before __init__ runs.

Python calls __new__ first to create a new instance. It returns the new object, which __init__ then initializes. By default, __new__ is inherited from the base object class, but you can override it to customize object creation.

Result

The object is created by __new__, then passed to __init__ for setup.

Understanding __new__ separates creation from initialization, unlocking advanced customization.

4

Intermediate

Order of calls in initialization flow

🤔Before reading on: do you think __init__ runs before or after __new__? Commit to your answer.

Concept: Clarifying the exact order Python calls __new__ and __init__ during object creation.

When you create an object, Python first calls __new__ with the class and arguments. __new__ returns a new object. Then Python calls __init__ on that object to initialize it. If __new__ returns an existing object, __init__ still runs on it.

Result

The flow is: __new__ creates → __init__ initializes → object ready.

Knowing the call order helps debug tricky object creation issues and customize behavior.

5

Intermediate

Customizing object creation with __new__

🤔Before reading on: do you think overriding __new__ is common or rare? Commit to your answer.

Concept: Showing how to override __new__ to control how objects are created, not just initialized.

You can override __new__ to return a different object, implement singletons, or control memory allocation. For example, returning an existing object instead of creating a new one can save resources.

Result

Object creation can be customized beyond just setting attributes.

Understanding __new__ customization opens doors to advanced design patterns.

6

Advanced

Handling immutable objects in initialization

🤔Before reading on: do you think __init__ can change immutable objects like tuples? Commit to your answer.

Concept: Explaining why immutable objects require __new__ for initialization instead of __init__.

Immutable objects like tuples or strings cannot be changed after creation. So, their initialization must happen in __new__, because __init__ runs after the object is created and cannot modify it. This is why classes inheriting from immutable types override __new__.

Result

Immutable objects are properly initialized during creation, not after.

Knowing this prevents bugs when subclassing immutable types.

7

Expert

Surprising behavior with multiple inheritance

🤔Before reading on: do you think __init__ methods from all parent classes run automatically? Commit to your answer.

Concept: Exploring how Python's method resolution order affects initialization in multiple inheritance.

In multiple inheritance, Python uses a method resolution order (MRO) to decide which __init__ runs. If you don't call super().__init__(), some parent initializations may be skipped, causing incomplete object setup. Proper use of super() ensures all __init__ methods run in order.

Result

Objects with multiple parents are fully initialized when super() is used correctly.

Understanding MRO and super() is critical to avoid subtle bugs in complex class hierarchies.

Under the Hood

When you create an object, Python first calls the class's __new__ method, which allocates memory and returns a new instance. Then Python calls __init__ on that instance to set up attributes. __new__ is a static method that receives the class as its first argument and must return an instance. __init__ receives the instance as self and initializes it. If __new__ returns an instance of a different class, __init__ still runs on it. This two-step process separates creation from initialization, allowing fine control.

Why designed this way?

Python separates object creation (__new__) from initialization (__init__) to support immutable types and advanced patterns like singletons. Early languages combined these steps, limiting flexibility. This design allows overriding creation without changing initialization and vice versa. It also fits Python's dynamic nature, letting programmers customize object lifecycle precisely.

┌───────────────┐
│ Call MyClass()│
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ __new__(cls)  │
│ allocates obj │
└──────┬────────┘
       │ returns new obj
       ▼
┌───────────────┐
│ __init__(self)│
│ sets attributes│
└──────┬────────┘
       │
       ▼
┌───────────────┐
│ Initialized   │
│ object ready  │
└───────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does __init__ create the object or just initialize it? Commit to your answer.

Common Belief: Many think __init__ creates the object because it runs first and sets attributes.

Reality: __init__ only initializes an already created object; __new__ creates it.

Why it matters: Confusing these leads to errors when overriding __new__ or subclassing immutable types.

Quick: Will __init__ run automatically for all parent classes in multiple inheritance? Commit to your answer.

Common Belief: People often believe all parent __init__ methods run automatically.

Reality: __init__ methods of parent classes run only if explicitly called, usually via super().

Why it matters: Missing super() calls cause incomplete initialization and subtle bugs.

Quick: Can __init__ modify immutable objects like tuples? Commit to your answer.

Common Belief: Some think __init__ can set attributes on immutable objects.

Reality: Immutable objects must be initialized in __new__, as __init__ cannot modify them.

Why it matters: Trying to initialize immutable objects in __init__ causes errors or ignored changes.

Quick: Does overriding __new__ always require returning a new object? Commit to your answer.

Common Belief: Many believe __new__ must always create and return a new object.

Reality: __new__ can return an existing object, enabling patterns like singletons.

Why it matters: Not knowing this limits design options and leads to inefficient object creation.

Expert Zone

1

Overriding __new__ without returning an instance of the class or a subclass can break object creation silently.

2

Using super() in __init__ is essential in multiple inheritance to ensure all parent classes initialize properly, but the order depends on the method resolution order (MRO).

3

Immutable types require careful handling in __new__, as __init__ cannot modify their state, which can confuse even experienced developers.

When NOT to use

Avoid overriding __new__ unless you need to control object creation itself, such as for immutable types or singletons. For most cases, customizing __init__ is simpler and safer. If you need to manage object lifecycle beyond creation and initialization, consider using factory functions or design patterns like builders.

Production Patterns

In production, __init__ is commonly used to set up objects with configuration data. __new__ is overridden in advanced cases like implementing singletons, caching instances, or subclassing immutable types. Proper use of super() in __init__ ensures robust multiple inheritance. Factories often wrap object creation to hide complexity.

Connections

Factory design pattern

Builds-on

Understanding object initialization flow helps grasp how factories create and configure objects behind the scenes.

Immutable data structures

Opposite/Complement

Knowing why immutable objects require __new__ for initialization clarifies their design and usage in Python.

Manufacturing assembly line

Similar process flow

The stepwise creation and setup of objects mirrors how products are assembled in stages, highlighting the importance of order and completeness.

Common Pitfalls

#1 Overriding __init__ without calling super() in multiple inheritance.

Wrong approach:

class Child(Parent1, Parent2):
def __init__(self):
# missing super()
self.value = 10

Correct approach:

class Child(Parent1, Parent2):
def __init__(self):
super().__init__()
self.value = 10

Root cause:

Misunderstanding that parent __init__ methods run automatically leads to incomplete initialization.

#2 Trying to initialize immutable objects in __init__ instead of __new__.

Wrong approach:

class MyTuple(tuple):
def __init__(self, data):
self.data = data # ineffective for immutable tuple

Correct approach:

class MyTuple(tuple):
def __new__(cls, data):
return super().__new__(cls, data)

Root cause:

Not knowing __init__ cannot modify immutable objects causes ineffective initialization.

#3 Overriding __new__ but forgetting to return the new object.

Wrong approach:

class MyClass:
def __new__(cls):
obj = super().__new__(cls)
# missing return statement

Correct approach:

class MyClass:
def __new__(cls):
obj = super().__new__(cls)
return obj

Root cause:

Forgetting to return the created object breaks object creation silently.

Key Takeaways

Object initialization flow in Python separates object creation (__new__) from initialization (__init__).

The __new__ method creates and returns the new object, while __init__ sets up its attributes.

Immutable objects must be initialized in __new__ because __init__ cannot modify them after creation.

In multiple inheritance, calling super().__init__() ensures all parent classes initialize properly.

Understanding this flow helps avoid common bugs and enables advanced customization of object creation.

Practice

1. What is the purpose of the __init__ method in a Python class?

easy

A. To delete an object when it is no longer needed

B. To define a class-level variable

C. To initialize a new object when it is created

D. To print the object details

Solution

1. Step 1: Understand the role of __init__

   The __init__ method runs automatically when a new object is created from a class.

2. Step 2: Identify what __init__ does

   It sets up the initial state of the object by assigning values to its attributes.

3. Final Answer:

   To initialize a new object when it is created -> Option C

4. Quick Check:

   __init__ initializes objects [OK]

Hint: Remember: __init__ sets up new objects automatically [OK]

Common Mistakes:

• Confusing __init__ with __del__
• Thinking __init__ is for printing
• Believing __init__ defines class variables

2. Which of the following is the correct syntax to define an __init__ method that takes a parameter name in a Python class?

easy

A. def __init__(self, name):

B. def __init__(name):

C. def init(self, name):

D. def __init__(self):

Solution

1. Step 1: Recall __init__ method signature

   The first parameter must be self to refer to the new object.

2. Step 2: Check parameter list

   To accept a name argument, it must be added after self.

3. Final Answer:

   def __init__(self, name): -> Option A

4. Quick Check:

   First param is self, then others [OK]

Hint: Always put self first in method parameters [OK]

Common Mistakes:

• Omitting self parameter
• Using init instead of __init__
• Missing parameters after self

3. What will be the output of this code?

class Car:
    def __init__(self, brand):
        self.brand = brand

my_car = Car('Toyota')
print(my_car.brand)

medium

A. Car

B. Toyota

C. brand

D. Error

Solution

1. Step 1: Understand object creation

   Creating my_car = Car('Toyota') calls __init__ with 'Toyota' as brand.

2. Step 2: Check attribute assignment and print

   The brand attribute of my_car is set to 'Toyota', so printing my_car.brand outputs 'Toyota'.

3. Final Answer:

   Toyota -> Option B

4. Quick Check:

   Attribute value prints 'Toyota' [OK]

Hint: Print attribute after init to see assigned value [OK]

Common Mistakes:

• Expecting class name instead of attribute value
• Confusing attribute name with value
• Thinking print causes error

4. What is wrong with this class definition?

class Person:
    def __init__(self, age):
        age = age

p = Person(30)
print(p.age)

medium

A. The attribute age is not assigned to self

B. The __init__ method is missing self parameter

C. The print statement syntax is incorrect

D. The class name should be lowercase

Solution

1. Step 1: Check attribute assignment inside __init__

   The code assigns age = age, which only reassigns the local variable, not the object's attribute.

2. Step 2: Understand how to assign attributes

   To store the value in the object, it should be self.age = age.

3. Final Answer:

   The attribute age is not assigned to self -> Option A

4. Quick Check:

   Use self.attribute = value to save data [OK]

Hint: Always assign attributes with self.attribute = value [OK]

Common Mistakes:

• Forgetting self in attribute assignment
• Thinking local variable sets object attribute
• Ignoring error messages about missing attributes

5. Given this class:

class Book:
    def __init__(self, title, author='Unknown'):
        self.title = title
        self.author = author

b1 = Book('Python 101')
b2 = Book('Learn AI', 'Alice')

What are the values of b1.author and b2.author?

hard

A. Both b1.author and b2.author are 'Unknown'

B. b1.author is 'Python 101', b2.author is 'Learn AI'

C. b1.author is None, b2.author is 'Alice'

D. b1.author is 'Unknown', b2.author is 'Alice'

Solution

1. Step 1: Understand default parameter usage

   The author parameter has a default value 'Unknown', used if no argument is given.

2. Step 2: Check object creation

   b1 is created with only title, so author defaults to 'Unknown'. b2 provides 'Alice' explicitly.

3. Final Answer:

   b1.author is 'Unknown', b2.author is 'Alice' -> Option D

4. Quick Check:

   Default params fill missing arguments [OK]

Hint: Default values apply when argument is missing [OK]

Common Mistakes:

• Assuming missing argument becomes None
• Mixing title and author values
• Forgetting default parameter behavior