🔍 Python Object Internals – Understand How Python Objects Work Under the Hood

🧲 Introduction – Why Learn Python Object Internals?

Every value in Python—whether it’s an integer, string, function, or class—is an object. Understanding the internal structure of Python objects helps you:

• Write efficient and memory-safe code
• Understand attribute lookups and method resolution
• Debug complex issues in OOP, metaclasses, and descriptors
• Master Python at a deeper, more professional level

Whether you’re working on performance, design patterns, or frameworks—object internals are the foundation of Python programming.

🎯 In this guide, you’ll learn:

• What Python objects are made of
• How attributes are stored and accessed
• Special internal attributes (__dict__, __slots__, __class__)
• Memory layout and object identity
• Inheritance and method resolution order (MRO)

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✅ Everything Is an Object in Python

print(type(42))            # <class 'int'>
print(type("Hello"))       # <class 'str'>
print(type(len))           # <class 'builtin_function_or_method'>
print(type(object))        # <class 'type'>

🧠 Even types and classes are themselves objects in Python. Python follows a consistent object model, with everything derived from object.

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🧱 Internal Structure of Python Objects

Each Python object has a fixed internal layout. At a high level, this includes:

Component	Description
type (__class__)	Points to the object’s class
refcount	Number of references (used by garbage collector)
__dict__	A dictionary storing instance attributes
__slots__	Optional memory-saving layout for fixed attrs

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🔎 Using __dict__ to Inspect Attributes

class Person:
    def __init__(self, name):
        self.name = name
        self.age = 30

p = Person("Alice")
print(p.__dict__)

✅ Output:

{'name': 'Alice', 'age': 30}

💡 All instance attributes are stored in the __dict__ unless __slots__ is defined.

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💼 Optimize with __slots__

class User:
    __slots__ = ('username', 'email')

    def __init__(self, username, email):
        self.username = username
        self.email = email

✅ Benefits:

• Saves memory (no __dict__)
• Slight speed improvement
• Prevents creation of new attributes

⚠️ __slots__ is optional and used in performance-critical code.

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🧠 Understanding __class__

Every object has a reference to its class via __class__:

x = 10
print(x.__class__)  # <class 'int'>

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🔁 Method Resolution Order (MRO)

MRO defines how Python looks up methods in case of inheritance.

✅ Example:

class A: pass
class B(A): pass
class C(B): pass

print(C.__mro__)

✅ Output:

(<class '__main__.C'>, <class '__main__.B'>, <class '__main__.A'>, <class 'object'>)

💡 Python uses the C3 Linearization Algorithm for MRO.

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🧬 Identity and Memory with id()

x = "hello"
print(id(x))  # Memory address of object

✅ id() returns the unique identity of an object, used for object comparison.

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🧠 Interning of Small Objects

Python caches small immutable objects for performance.

a = 100
b = 100
print(a is b)  # ✅ True – same object (cached)

a = 1000
b = 1000
print(a is b)  # ❌ Might be False – not interned

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🧰 Built-in Tools to Inspect Objects

Function	Use Case
type(obj)	Get the object’s class
dir(obj)	List all available attributes/methods
hasattr(obj, x)	Check if object has an attribute
getattr(obj, x)	Dynamically get an attribute value
setattr(obj, x, v)	Dynamically set an attribute
isinstance(obj, T)	Check if object is an instance of a class

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🧰 Real-World Use Case – Dynamic Object Introspection

def dump_object(obj):
    print(f"Class: {obj.__class__.__name__}")
    print("Attributes:", obj.__dict__)
    print("Methods:", [m for m in dir(obj) if callable(getattr(obj, m)) and not m.startswith("__")])

class Product:
    def __init__(self, name):
        self.name = name
    def display(self):
        print("Product:", self.name)

p = Product("Laptop")
dump_object(p)

✅ Useful for debuggers, serializers, and frameworks.

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📘 Best Practices

✅ Do This	❌ Avoid This
Use __slots__ for performance-critical classes	Adding too many dynamic attributes
Use __dict__ and __class__ only for debugging	Relying on them in production logic
Use isinstance() for safe type checking	Comparing types directly with ==
Understand id() and is for object identity	Confuse with == (value comparison)

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📌 Summary – Recap & Next Steps

Python object internals give you deep insight into how objects store attributes, manage memory, and resolve methods.

🔍 Key Takeaways:

• ✅ All Python values are objects with class, attributes, and identity
• ✅ Instance variables live in __dict__
• ✅ Use __slots__ to reduce memory for fixed-attribute classes
• ✅ MRO controls how methods are resolved in inheritance
• ✅ id(), __class__, and dir() reveal internal structure

⚙️ Real-World Relevance:
Essential for writing frameworks, building ORMs, debugging tools, and performance optimization.

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❓ FAQ – Python Object Internals

❓ What is __dict__?

✅ A dictionary holding an object’s writable attributes.

❓ What is __slots__ used for?

✅ To restrict attribute creation and save memory by removing __dict__.

❓ What’s the difference between is and ==?

• is: checks object identity
• ==: checks value equality

❓ How can I find an object’s class?

✅ Use obj.__class__ or type(obj).

❓ What is the MRO?

✅ MRO (Method Resolution Order) is the order Python uses to search for methods in a class hierarchy.

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