Perfect.
This is Module 5 — OOP Mastery, taught at senior / staff / principal Python interview level.

Most candidates use classes.
Very few understand object model, MRO, descriptors, dunder methods, and design trade-offs.
After this module, OOP interviews become predictable.

🧠 MODULE 5 — PYTHON OOP MASTERY (VERY DEEP)

5.1 EVERYTHING IS AN OBJECT (FOUNDATIONAL TRUTH)

In Python:

x = 10

10 → object
x → reference
int itself → object
class → object
function → object

print(type(type))

✔ <class 'type'>

📌 Interview line

Python is a pure object-oriented language with some procedural syntax.

5.2 CLASS CREATION — WHAT REALLY HAPPENS

class A:
    x = 10

Internally:

Class body executed
Namespace dict created
type(name, bases, dict) called
Class object returned

Equivalent to:

A = type("A", (), {"x": 10})

🔥 Interview gold

5.3 INSTANCE CREATION — `new` vs `init`

class A:
    def __new__(cls):
        print("new")
        return super().__new__(cls)

    def __init__(self):
        print("init")

A()

Output:

new
init

Key difference

Method	Purpose
`__new__`	Creates object
`__init__`	Initializes object

📌 __new__ is rarely overridden (immutables, singletons).

5.4 INSTANCE vs CLASS VARIABLES (INTERVIEW FAVORITE)

class A:
    x = 10

a1 = A()
a2 = A()

a1.x = 20

Results:

a1.x = 20 → instance attribute
a2.x = 10
A.x = 10

📌 Attribute lookup order

instance → class → parent classes

5.5 MUTABLE CLASS VARIABLE TRAP (VERY COMMON)

class A:
    data = []

a = A()
b = A()

a.data.append(1)
print(b.data)

Output:

[1]

WHY?

Shared class-level list

✅ Fix:

class A:
    def __init__(self):
        self.data = []

5.6 METHOD TYPES (CRITICAL)

class A:
    def inst(self): pass

    @classmethod
    def cls(cls): pass

    @staticmethod
    def stat(): pass

Method	Receives
Instance	`self`
Class	`cls`
Static	nothing

📌 Interview rule

@classmethod → factory methods
@staticmethod → utility logic

5.7 INHERITANCE — SINGLE & MULTIPLE

class A: pass
class B(A): pass

Multiple inheritance:

class C(A, B): pass

📌 Python supports multiple inheritance, unlike Java.

5.8 METHOD RESOLUTION ORDER (MRO) — CORE TOPIC

MRO defines:

Order in which Python searches for methods

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

print(D.mro())

Output:

[D, B, C, A, object]

5.9 C3 LINEARIZATION (INTERVIEW THEORY)

Python uses C3 algorithm:

Preserves local precedence
Ensures monotonicity
Avoids ambiguity

📌 If MRO cannot be resolved → TypeError

5.10 `super()` — NOT PARENT CALL

class A:
    def show(self):
        print("A")

class B(A):
    def show(self):
        super().show()
        print("B")

📌 super():

Calls next method in MRO
NOT necessarily direct parent

🔥 Interview killer concept

5.11 DIAMOND PROBLEM (SOLVED BY MRO)

class A:
    def f(self): print("A")

class B(A):
    def f(self): print("B")

class C(A):
    def f(self): print("C")

class D(B, C): pass

D().f()

Output:

WHY?

MRO order → B before C

5.12 ENCAPSULATION & NAME MANGLING

class A:
    def __init__(self):
        self.__x = 10

Accessed as:

a._A__x

📌 __x → name-mangled, not truly private

5.13 DUNDER METHODS — OBJECT BEHAVIOR CONTROL

Common dunders

Method	Purpose
`__str__`	User-friendly
`__repr__`	Debug
`__len__`	`len(obj)`
`__eq__`	`==`
`__lt__`	`<`
`__hash__`	Hashing
`__call__`	Callable object

5.14 `str` vs `repr` (INTERVIEW CLASSIC)

class A:
    def __repr__(self):
        return "A_repr"
    def __str__(self):
        return "A_str"

print(a)        # A_str
repr(a)         # A_repr

📌 __repr__ should be unambiguous.

5.15 OPERATOR OVERLOADING

class Vec:
    def __init__(self, x):
        self.x = x

    def __add__(self, other):
        return Vec(self.x + other.x)

v1 + v2

📌 Used in numeric & domain models.

5.16 EQUALITY & HASHING (VERY IMPORTANT)

class A:
    def __eq__(self, other):
        return self.x == other.x

⚠️ If you override __eq__, you must handle __hash__.

__hash__ = None

📌 Otherwise object becomes unhashable.

5.17 `call` — OBJECT AS FUNCTION

class Counter:
    def __init__(self):
        self.c = 0

    def __call__(self):
        self.c += 1
        return self.c

c = Counter()
c()  # 1
c()  # 2

5.18 ABSTRACT BASE CLASSES (ABC)

from abc import ABC, abstractmethod

class Shape(ABC):
    @abstractmethod
    def area(self):
        pass

Cannot instantiate unless implemented.

📌 Used for interfaces.

5.19 COMPOSITION vs INHERITANCE (DESIGN)

❌ Overusing inheritance

class Car(Engine): pass

✅ Prefer composition

class Car:
    def __init__(self):
        self.engine = Engine()

📌 Interview rule

Prefer composition unless there is a true “is-a” relationship.

5.20 `slots` — MEMORY OPTIMIZATION (ADVANCED)

class A:
    __slots__ = ("x", "y")

Removes __dict__
Saves memory
Faster attribute access

⚠️ Restricts dynamic attributes.

🧪 PRACTICE (INTERVIEW LEVEL)

Q1

Why does this break?

class A:
    data = []

Q2

Explain super() in one sentence.

Q3

Predict output:

class A:
    def f(self): print("A")

class B(A):
    def f(self):
        super().f()
        print("B")

B().f()

Q4

Why is overriding __eq__ dangerous without __hash__?

Q5

When would you override __new__?

🎯 INTERVIEW CHECKPOINT (CRITICAL)

You must now confidently explain:

✅ How classes are created (type)
✅ __new__ vs __init__
✅ Instance vs class attributes
✅ Multiple inheritance & MRO
✅ super() internals
✅ Diamond problem
✅ Encapsulation & name mangling
✅ Dunder methods behavior
✅ Equality & hashing rules
✅ Composition vs inheritance

If yes → you are now strong senior-level Python OOP.

🚀 NEXT STEP (CHOOSE ONE)

Reply with ONE number:

1 → 30-question OOP interview drill (very tricky)
2 → Advanced Python internals: descriptors, metaclasses
3 → Build an OOP-heavy real project (framework-style)

You’re now playing at top-tier Python depth 🔥

Excellent.
We’ll do this in the exact order you asked — no shortcuts, no dilution.

You’ll get:

1️⃣ 30 brutal OOP interview questions (with crisp answers)
2️⃣ Advanced internals: descriptors & metaclasses (deep but interview-safe)
3️⃣ A real OOP-heavy framework-style project (impressive, explainable)

This is staff-level Python mastery.

🧠 PART 1 — 30 OOP INTERVIEW QUESTIONS (TRICKY & REAL)

Answer fast. Explain cleanly. That’s how interviews are evaluated.

1️⃣ How is a Python class created internally?

✅ Via type(name, bases, namespace) after executing class body.

2️⃣ Difference between `new` and `init`?

✅ __new__ creates object, __init__ initializes it.

3️⃣ When would you override `new`?

✅ Immutables, singletons, object pooling.

4️⃣ Instance variable vs class variable?

✅ Instance → object-specific
✅ Class → shared across instances

5️⃣ Why is this dangerous?

class A:
    data = []

✅ Shared mutable state.

6️⃣ Attribute lookup order?

✅ instance → class → base classes

7️⃣ What is MRO?

✅ Order Python follows to resolve methods.

8️⃣ Which algorithm does Python use for MRO?

✅ C3 Linearization.

9️⃣ What problem does C3 solve?

✅ Diamond inheritance ambiguity.

🔟 Is `super()` calling the parent?

❌ No.
✅ Calls next method in MRO.

1️⃣1️⃣ Output?

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

print(D.mro())

✅ [D, B, C, A, object]

1️⃣2️⃣ Why multiple inheritance works safely in Python?

✅ Because of deterministic MRO.

1️⃣3️⃣ `@classmethod` vs `@staticmethod`?

✅ classmethod → receives class
✅ staticmethod → receives nothing

1️⃣4️⃣ Typical use of `@classmethod`?

✅ Factory methods.

1️⃣5️⃣ Is Python truly encapsulated?

❌ No. Name mangling only.

1️⃣6️⃣ What is name mangling?

✅ __x → _ClassName__x

1️⃣7️⃣ Difference: `str` vs `repr`?

✅ str → user-friendly
✅ repr → unambiguous/debug

1️⃣8️⃣ Why override `repr`?

✅ Debugging, logging, REPL clarity.

1️⃣9️⃣ What happens if `eq` is overridden?

✅ Must handle __hash__.

2️⃣0️⃣ Why?

✅ Hash consistency for dict/set safety.

2️⃣1️⃣ What makes an object hashable?

✅ Immutable + stable hash.

2️⃣2️⃣ Can a tuple be unhashable?

✅ Yes, if it contains mutable elements.

2️⃣3️⃣ What does `call` do?

✅ Makes object callable like a function.

2️⃣4️⃣ Why use `slots`?

✅ Memory optimization, faster attribute access.

2️⃣5️⃣ Drawback of `slots`?

✅ No dynamic attributes, harder inheritance.

2️⃣6️⃣ ABC vs duck typing?

✅ ABC → enforced contract
✅ Duck typing → behavior-based

2️⃣7️⃣ When to prefer composition?

✅ When relationship is “has-a”.

2️⃣8️⃣ Why inheritance is often overused?

✅ Leads to tight coupling.

2️⃣9️⃣ What is polymorphism in Python?

✅ Same method name, different behavior.

3️⃣0️⃣ One-line OOP interview killer?

“In Python, behavior matters more than hierarchy.”

🧠 PART 2 — ADVANCED INTERNALS

🔥 DESCRIPTORS & METACLASSES (INTERVIEW-SAFE DEEP)

2.1 DESCRIPTORS — HOW ATTRIBUTE ACCESS REALLY WORKS

A descriptor is any object with:

__get__
__set__
__delete__

Example: Custom Attribute Validation

class Positive:
    def __get__(self, obj, objtype=None):
        return obj._x

    def __set__(self, obj, value):
        if value <= 0:
            raise ValueError("Must be positive")
        obj._x = value

Usage:

class Account:
    balance = Positive()

    def __init__(self, balance):
        self.balance = balance

🎯 Used in:

Properties
ORM fields
Framework internals

2.2 PROPERTY IS A DESCRIPTOR

class A:
    @property
    def x(self): ...

📌 property = descriptor wrapper.

2.3 DATA vs NON-DATA DESCRIPTORS

Type	Methods
Data	`__get__ + __set__`
Non-data	`__get__` only

Data descriptors take precedence over instance attributes.

2.4 METACLASSES — CLASSES THAT CREATE CLASSES

class MyMeta(type):
    pass

Used when:

Modifying class creation
Enforcing rules
Auto-registering classes

Metaclass Example: Enforcing Method Presence

class InterfaceMeta(type):
    def __new__(cls, name, bases, dct):
        if "execute" not in dct:
            raise TypeError("execute() required")
        return super().__new__(cls, name, bases, dct)

Usage:

class Task(metaclass=InterfaceMeta):
    def execute(self):
        pass

🎯 Interview-safe explanation:

Metaclasses customize class creation, not object creation.

2.5 Why Metaclasses Are Rare

Hard to read
Debugging complexity
Usually solved with decorators / ABCs

🧠 PART 3 — OOP-HEAVY REAL PROJECT

🔥 MINI FRAMEWORK (INTERVIEW-IMPRESSIVE)

🎯 GOAL

Build a Task Execution Framework with:

Abstract base classes
Descriptor validation
Class registry
Polymorphism
Clean OOP design

3.1 CORE INTERFACE (ABC)

from abc import ABC, abstractmethod

class Task(ABC):
    @abstractmethod
    def execute(self):
        pass

3.2 DESCRIPTOR-BASED VALIDATION

class PositiveInt:
    def __get__(self, obj, objtype=None):
        return obj._value

    def __set__(self, obj, value):
        if value <= 0:
            raise ValueError("Must be positive")
        obj._value = value

3.3 TASK REGISTRY (CLASS-LEVEL)

class TaskRegistry:
    _tasks = {}

    @classmethod
    def register(cls, task_cls):
        cls._tasks[task_cls.__name__] = task_cls

3.4 BASE TASK IMPLEMENTATION

class BaseTask(Task):
    retries = PositiveInt()

    def __init__(self, retries):
        self.retries = retries

3.5 CONCRETE TASK (POLYMORPHISM)

class EmailTask(BaseTask):
    def execute(self):
        print("Sending email...")

TaskRegistry.register(EmailTask)

3.6 EXECUTION ENGINE

def run_task(name, *args):
    task_cls = TaskRegistry._tasks[name]
    task = task_cls(*args)
    task.execute()

Usage:

run_task("EmailTask", 3)

🔥 WHY THIS PROJECT IS INTERVIEW GOLD

You can clearly explain:

✅ Abstract base classes
✅ Descriptor validation
✅ Class vs instance attributes
✅ Polymorphism
✅ Composition over inheritance
✅ Clean extension via registry

This sounds like real engineering, not tutorial code.

🏁 FINAL VERDICT — YOUR LEVEL NOW

If you can:

Explain MRO confidently
Write __eq__ + __hash__ safely
Explain descriptors without panic
Justify composition over inheritance
Walk through this mini framework

👉 You are solid senior-level Python, approaching staff-level depth.

🚀 WHAT NEXT? (CHOOSE ONE)

1 → Advanced concurrency: threading, multiprocessing, async/await
2 → Python internals & performance tuning (GC, memory, bytecode)
3 → End-to-end real project (CLI / ETL / backend service) combining everything

You’ve now built true Python mastery 🔥

Excellent.
We’ll do this in the exact order you asked — no shortcuts, no dilution.

You’ll get:

This is staff-level Python mastery.

🧠 PART 1 — 30 OOP INTERVIEW QUESTIONS (TRICKY & REAL)

Answer fast. Explain cleanly. That’s how interviews are evaluated.

1️⃣ How is a Python class created internally?

✅ Via type(name, bases, namespace) after executing class body.

2️⃣ Difference between `new` and `init`?

✅ __new__ creates object, __init__ initializes it.

3️⃣ When would you override `new`?

✅ Immutables, singletons, object pooling.

4️⃣ Instance variable vs class variable?

✅ Instance → object-specific
✅ Class → shared across instances

5️⃣ Why is this dangerous?

class A:
    data = []

✅ Shared mutable state.

6️⃣ Attribute lookup order?

✅ instance → class → base classes

7️⃣ What is MRO?

✅ Order Python follows to resolve methods.

8️⃣ Which algorithm does Python use for MRO?

✅ C3 Linearization.

9️⃣ What problem does C3 solve?

✅ Diamond inheritance ambiguity.

🔟 Is `super()` calling the parent?

❌ No.
✅ Calls next method in MRO.

1️⃣1️⃣ Output?

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

print(D.mro())

✅ [D, B, C, A, object]

1️⃣2️⃣ Why multiple inheritance works safely in Python?

✅ Because of deterministic MRO.

1️⃣3️⃣ `@classmethod` vs `@staticmethod`?

✅ classmethod → receives class
✅ staticmethod → receives nothing

1️⃣4️⃣ Typical use of `@classmethod`?

✅ Factory methods.

1️⃣5️⃣ Is Python truly encapsulated?

❌ No. Name mangling only.

1️⃣6️⃣ What is name mangling?

✅ __x → _ClassName__x

1️⃣7️⃣ Difference: `str` vs `repr`?

✅ str → user-friendly
✅ repr → unambiguous/debug

1️⃣8️⃣ Why override `repr`?

✅ Debugging, logging, REPL clarity.

1️⃣9️⃣ What happens if `eq` is overridden?

✅ Must handle __hash__.

2️⃣0️⃣ Why?

✅ Hash consistency for dict/set safety.

2️⃣1️⃣ What makes an object hashable?

✅ Immutable + stable hash.

2️⃣2️⃣ Can a tuple be unhashable?

✅ Yes, if it contains mutable elements.

2️⃣3️⃣ What does `call` do?

✅ Makes object callable like a function.

2️⃣4️⃣ Why use `slots`?

✅ Memory optimization, faster attribute access.

2️⃣5️⃣ Drawback of `slots`?

✅ No dynamic attributes, harder inheritance.

2️⃣6️⃣ ABC vs duck typing?

✅ ABC → enforced contract
✅ Duck typing → behavior-based

2️⃣7️⃣ When to prefer composition?

✅ When relationship is “has-a”.

2️⃣8️⃣ Why inheritance is often overused?

✅ Leads to tight coupling.

2️⃣9️⃣ What is polymorphism in Python?

✅ Same method name, different behavior.

3️⃣0️⃣ One-line OOP interview killer?

“In Python, behavior matters more than hierarchy.”

🧠 PART 2 — ADVANCED INTERNALS

🔥 DESCRIPTORS & METACLASSES (INTERVIEW-SAFE DEEP)

2.1 DESCRIPTORS — HOW ATTRIBUTE ACCESS REALLY WORKS

A descriptor is any object with:

__get__
__set__
__delete__

Example: Custom Attribute Validation

class Positive:
    def __get__(self, obj, objtype=None):
        return obj._x

    def __set__(self, obj, value):
        if value <= 0:
            raise ValueError("Must be positive")
        obj._x = value

Usage:

class Account:
    balance = Positive()

    def __init__(self, balance):
        self.balance = balance

🎯 Used in:

Properties
ORM fields
Framework internals

2.2 PROPERTY IS A DESCRIPTOR

class A:
    @property
    def x(self): ...

📌 property = descriptor wrapper.

2.3 DATA vs NON-DATA DESCRIPTORS

Type	Methods
Data	`__get__ + __set__`
Non-data	`__get__` only

Data descriptors take precedence over instance attributes.

2.4 METACLASSES — CLASSES THAT CREATE CLASSES

class MyMeta(type):
    pass

Used when:

Modifying class creation
Enforcing rules
Auto-registering classes

Metaclass Example: Enforcing Method Presence

class InterfaceMeta(type):
    def __new__(cls, name, bases, dct):
        if "execute" not in dct:
            raise TypeError("execute() required")
        return super().__new__(cls, name, bases, dct)

Usage:

class Task(metaclass=InterfaceMeta):
    def execute(self):
        pass

🎯 Interview-safe explanation:

Metaclasses customize class creation, not object creation.

2.5 Why Metaclasses Are Rare

Hard to read
Debugging complexity
Usually solved with decorators / ABCs

🧠 PART 3 — OOP-HEAVY REAL PROJECT

🔥 MINI FRAMEWORK (INTERVIEW-IMPRESSIVE)

🎯 GOAL

Build a Task Execution Framework with:

Abstract base classes
Descriptor validation
Class registry
Polymorphism
Clean OOP design

3.1 CORE INTERFACE (ABC)

from abc import ABC, abstractmethod

class Task(ABC):
    @abstractmethod
    def execute(self):
        pass

3.2 DESCRIPTOR-BASED VALIDATION

class PositiveInt:
    def __get__(self, obj, objtype=None):
        return obj._value

    def __set__(self, obj, value):
        if value <= 0:
            raise ValueError("Must be positive")
        obj._value = value

3.3 TASK REGISTRY (CLASS-LEVEL)

class TaskRegistry:
    _tasks = {}

    @classmethod
    def register(cls, task_cls):
        cls._tasks[task_cls.__name__] = task_cls

3.4 BASE TASK IMPLEMENTATION

class BaseTask(Task):
    retries = PositiveInt()

    def __init__(self, retries):
        self.retries = retries

3.5 CONCRETE TASK (POLYMORPHISM)

class EmailTask(BaseTask):
    def execute(self):
        print("Sending email...")

TaskRegistry.register(EmailTask)

3.6 EXECUTION ENGINE

def run_task(name, *args):
    task_cls = TaskRegistry._tasks[name]
    task = task_cls(*args)
    task.execute()

Usage:

run_task("EmailTask", 3)

🔥 WHY THIS PROJECT IS INTERVIEW GOLD

You can clearly explain:

✅ Abstract base classes
✅ Descriptor validation
✅ Class vs instance attributes
✅ Polymorphism
✅ Composition over inheritance
✅ Clean extension via registry

This sounds like real engineering, not tutorial code.

🏁 FINAL VERDICT — YOUR LEVEL NOW

If you can:

Explain MRO confidently
Write __eq__ + __hash__ safely
Explain descriptors without panic
Justify composition over inheritance
Walk through this mini framework

👉 You are solid senior-level Python, approaching staff-level depth.

🚀 WHAT NEXT? (CHOOSE ONE)

You’ve now built true Python mastery 🔥

Perfect.
We’ll do this exactly in the order you asked, at senior → staff interview depth:

1️⃣ Advanced Concurrency (threading, multiprocessing, async/await)
2️⃣ Python Internals & Performance Tuning (GC, memory, bytecode)
3️⃣ End-to-End Real Project (CLI / ETL / service) combining EVERYTHING

No shortcuts. This is elite Python engineering.

🧠 PART 1 — ADVANCED CONCURRENCY (INTERVIEW-HEAVY)

Most candidates use concurrency.
Very few understand when, why, and how it actually works in Python.

1. THREADING — THE TRUTH (GIL REALITY)

Key Fact (Interview Must-Say)

Threads do NOT run Python bytecode in parallel in CPython due to the GIL.

When threading IS useful

✔ IO-bound tasks
✔ Network calls
✔ Disk reads/writes
✔ Waiting on APIs

When threading FAILS

❌ CPU-bound tasks
❌ Heavy computation loops

Example — IO-bound (Good Use)

import threading
import time

def task():
    time.sleep(1)

threads = [threading.Thread(target=task) for _ in range(5)]
for t in threads: t.start()
for t in threads: t.join()

✔ Faster than sequential

Thread Safety (Interview Trap)

x = 0
def incr():
    global x
    x += 1

❌ Not thread-safe

✅ Fix:

lock = threading.Lock()
with lock:
    x += 1

2. MULTIPROCESSING — TRUE PARALLELISM

Multiprocessing bypasses the GIL by using separate processes.

When to use

✔ CPU-bound tasks
✔ Data processing
✔ ML / numerical workloads

Example

from multiprocessing import Process

def work():
    print("Working")

p = Process(target=work)
p.start()
p.join()

Interview Insight

Processes do not share memory
Communication via:
- Queue
- Pipe
- Shared memory

Common Multiprocessing Bug

if __name__ == "__main__":
    ...

❗ Required on Windows (interview favorite)

3. ASYNC / AWAIT — CONCURRENCY WITHOUT THREADS

Async is single-threaded, non-blocking concurrency.

Core Idea

Event loop
Cooperative multitasking
Tasks yield control via await

Basic Example

import asyncio

async def task():
    await asyncio.sleep(1)
    print("Done")

async def main():
    await asyncio.gather(task(), task(), task())

asyncio.run(main())

✔ Runs concurrently
✔ Single thread
✔ No GIL issues (because no threads)

When to Use Async

✔ Massive IO (APIs, sockets)
✔ Web servers
✔ Crawlers

❌ CPU-heavy logic

4. THREADING vs MULTIPROCESSING vs ASYNC (INTERVIEW TABLE)

Type	Parallel?	Best For
Threading	❌	IO-bound
Multiprocessing	✅	CPU-bound
Async	❌	High-scale IO

5. INTERVIEW KILLER QUESTIONS (CONCURRENCY)

Q: Why does Python have GIL?
✔ Simpler memory management, safe reference counting.

Q: Can async replace threads?
✔ No — different use cases.

Q: Is async faster than threading?
✔ Only for high-concurrency IO.

🧠 PART 2 — PYTHON INTERNALS & PERFORMANCE TUNING

This is where staff-level engineers separate from seniors.

1. GARBAGE COLLECTION (GC) — REAL BEHAVIOR

Python uses:

Reference counting (primary)
Cyclic GC (backup)

Inspect GC

import gc
gc.get_threshold()
gc.collect()

Memory Leak Causes (Interview Favorite)

Reference cycles with __del__
Global caches
Long-lived closures
C extensions

2. MEMORY PROFILING (REAL WORLD)

import sys
sys.getsizeof([1,2,3])

⚠ Only shallow size

3. `slots` — MEMORY OPTIMIZATION

class User:
    __slots__ = ("id", "name")

✔ Less memory
✔ Faster access
❌ No dynamic attributes

4. BYTECODE OPTIMIZATION (ADVANCED)

import dis

def f(x):
    return x + 1

dis.dis(f)

Interview insight:

Python uses stack-based VM
Fewer instructions → faster

5. LOCAL VARIABLE OPTIMIZATION

def slow():
    for _ in range(10**6):
        len([1,2,3])

def fast():
    l = len
    for _ in range(10**6):
        l([1,2,3])

✔ Locals are faster than globals

6. FUNCTION CALL COST (WHY LOOPS MATTER)

Function calls are expensive — inline logic when possible.

7. `lru_cache` — PERFORMANCE WEAPON

from functools import lru_cache

@lru_cache(maxsize=256)
def fib(n):
    if n <= 1: return n
    return fib(n-1) + fib(n-2)

Interview follow-up:

Cache eviction
Memory trade-offs

🧠 PART 3 — END-TO-END REAL PROJECT

🔥 CONCURRENT TASK EXECUTION ENGINE (INTERVIEW-IMPRESSIVE)

This project ties everything together:

OOP
Decorators
Concurrency
Performance
Clean design

🎯 PROJECT GOAL

Build a Task Runner Framework that:

Registers tasks
Executes them concurrently
Supports:
- Threading
- Multiprocessing
- Async
Logs execution time
Handles retries

1. TASK INTERFACE (OOP)

from abc import ABC, abstractmethod

class Task(ABC):
    @abstractmethod
    def run(self):
        pass

2. TASK REGISTRY (DECORATOR)

TASKS = {}

def register(name):
    def decorator(cls):
        TASKS[name] = cls
        return cls
    return decorator

3. RETRY DECORATOR

import time

def retry(n=3):
    def decorator(func):
        def wrapper(*args, **kwargs):
            for i in range(n):
                try:
                    return func(*args, **kwargs)
                except Exception:
                    if i == n-1:
                        raise
                    time.sleep(1)
        return wrapper
    return decorator

4. CONCRETE TASKS

@register("cpu")
class CPUTask(Task):
    def run(self):
        sum(i*i for i in range(10**7))

@register("io")
class IOTask(Task):
    def run(self):
        time.sleep(1)

5. EXECUTION ENGINE

Threading Engine

from concurrent.futures import ThreadPoolExecutor

def run_threaded(tasks):
    with ThreadPoolExecutor() as ex:
        ex.map(lambda t: t.run(), tasks)

Multiprocessing Engine

from multiprocessing import Pool

def run_process(tasks):
    with Pool() as p:
        p.map(lambda t: t.run(), tasks)

Async Engine

import asyncio

async def run_async(tasks):
    await asyncio.gather(*(t.run() for t in tasks))

6. RUNNER

def run(mode, names):
    tasks = [TASKS[name]() for name in names]

    if mode == "thread":
        run_threaded(tasks)
    elif mode == "process":
        run_process(tasks)

🔥 HOW TO TALK ABOUT THIS IN INTERVIEWS

You can explain:

Why CPU tasks use multiprocessing
Why IO tasks use threading / async
How decorators enable extensibility
Why OOP gives clean contracts
How retry & timing improve reliability

This sounds like real system design, not LeetCode.

🏁 FINAL VERDICT

At this point, you can:
✅ Answer any Python concurrency question
✅ Explain GIL confidently
✅ Tune Python performance
✅ Build real frameworks
✅ Speak like a senior/staff engineer

🚀 FINAL OPTIONS (YOUR CHOICE)

1 → Data Engineering Python (ETL, batching, streaming patterns)
2 → AI/ML-ready Python (vectorization, NumPy internals)
3 → Mock interview loop (multiple full interviews)

You’ve built true Python mastery 🔥

Pages: 1 2 3 4 5 6 7 8

True A→Z → PhD-level Python Mastery Course Part2

🧠 MODULE 5 — PYTHON OOP MASTERY (VERY DEEP)

5.1 EVERYTHING IS AN OBJECT (FOUNDATIONAL TRUTH)

5.2 CLASS CREATION — WHAT REALLY HAPPENS

5.3 INSTANCE CREATION — __new__ vs __init__

Key difference

5.4 INSTANCE vs CLASS VARIABLES (INTERVIEW FAVORITE)

5.5 MUTABLE CLASS VARIABLE TRAP (VERY COMMON)

5.6 METHOD TYPES (CRITICAL)

5.7 INHERITANCE — SINGLE & MULTIPLE

5.8 METHOD RESOLUTION ORDER (MRO) — CORE TOPIC

MRO defines:

5.9 C3 LINEARIZATION (INTERVIEW THEORY)

5.10 super() — NOT PARENT CALL

5.11 DIAMOND PROBLEM (SOLVED BY MRO)

5.12 ENCAPSULATION & NAME MANGLING

5.13 DUNDER METHODS — OBJECT BEHAVIOR CONTROL

Common dunders

5.14 __str__ vs __repr__ (INTERVIEW CLASSIC)

5.15 OPERATOR OVERLOADING

5.16 EQUALITY & HASHING (VERY IMPORTANT)

5.17 __call__ — OBJECT AS FUNCTION

5.18 ABSTRACT BASE CLASSES (ABC)

5.19 COMPOSITION vs INHERITANCE (DESIGN)

❌ Overusing inheritance

✅ Prefer composition

5.20 __slots__ — MEMORY OPTIMIZATION (ADVANCED)

🧪 PRACTICE (INTERVIEW LEVEL)

Q1

Q2

Q3

Q4

Q5

🎯 INTERVIEW CHECKPOINT (CRITICAL)

🚀 NEXT STEP (CHOOSE ONE)

🧠 PART 1 — 30 OOP INTERVIEW QUESTIONS (TRICKY & REAL)

1️⃣ How is a Python class created internally?

2️⃣ Difference between __new__ and __init__?

3️⃣ When would you override __new__?

4️⃣ Instance variable vs class variable?

5️⃣ Why is this dangerous?

6️⃣ Attribute lookup order?

7️⃣ What is MRO?

8️⃣ Which algorithm does Python use for MRO?

9️⃣ What problem does C3 solve?

🔟 Is super() calling the parent?

1️⃣1️⃣ Output?

1️⃣2️⃣ Why multiple inheritance works safely in Python?

1️⃣3️⃣ @classmethod vs @staticmethod?

1️⃣4️⃣ Typical use of @classmethod?

1️⃣5️⃣ Is Python truly encapsulated?

1️⃣6️⃣ What is name mangling?

1️⃣7️⃣ Difference: __str__ vs __repr__?

1️⃣8️⃣ Why override __repr__?

1️⃣9️⃣ What happens if __eq__ is overridden?

2️⃣0️⃣ Why?

2️⃣1️⃣ What makes an object hashable?

2️⃣2️⃣ Can a tuple be unhashable?

2️⃣3️⃣ What does __call__ do?

2️⃣4️⃣ Why use __slots__?

2️⃣5️⃣ Drawback of __slots__?

2️⃣6️⃣ ABC vs duck typing?

2️⃣7️⃣ When to prefer composition?

2️⃣8️⃣ Why inheritance is often overused?

2️⃣9️⃣ What is polymorphism in Python?

3️⃣0️⃣ One-line OOP interview killer?

🧠 PART 2 — ADVANCED INTERNALS

🔥 DESCRIPTORS & METACLASSES (INTERVIEW-SAFE DEEP)

2.1 DESCRIPTORS — HOW ATTRIBUTE ACCESS REALLY WORKS

Example: Custom Attribute Validation

2.2 PROPERTY IS A DESCRIPTOR

2.3 DATA vs NON-DATA DESCRIPTORS

2.4 METACLASSES — CLASSES THAT CREATE CLASSES

Metaclass Example: Enforcing Method Presence

2.5 Why Metaclasses Are Rare

🧠 PART 3 — OOP-HEAVY REAL PROJECT

🔥 MINI FRAMEWORK (INTERVIEW-IMPRESSIVE)

🎯 GOAL

3.1 CORE INTERFACE (ABC)

3.2 DESCRIPTOR-BASED VALIDATION

5.3 INSTANCE CREATION — `new` vs `init`

5.10 `super()` — NOT PARENT CALL

5.14 `str` vs `repr` (INTERVIEW CLASSIC)

5.17 `call` — OBJECT AS FUNCTION

5.20 `slots` — MEMORY OPTIMIZATION (ADVANCED)

2️⃣ Difference between `new` and `init`?

3️⃣ When would you override `new`?

🔟 Is `super()` calling the parent?

1️⃣3️⃣ `@classmethod` vs `@staticmethod`?

1️⃣4️⃣ Typical use of `@classmethod`?

1️⃣7️⃣ Difference: `str` vs `repr`?

1️⃣8️⃣ Why override `repr`?

1️⃣9️⃣ What happens if `eq` is overridden?

2️⃣3️⃣ What does `call` do?

2️⃣4️⃣ Why use `slots`?

2️⃣5️⃣ Drawback of `slots`?

2️⃣ Difference between `new` and `init`?

3️⃣ When would you override `new`?

🔟 Is `super()` calling the parent?

1️⃣3️⃣ `@classmethod` vs `@staticmethod`?

1️⃣4️⃣ Typical use of `@classmethod`?

1️⃣7️⃣ Difference: `str` vs `repr`?

1️⃣8️⃣ Why override `repr`?

1️⃣9️⃣ What happens if `eq` is overridden?

2️⃣3️⃣ What does `call` do?

2️⃣4️⃣ Why use `slots`?

2️⃣5️⃣ Drawback of `slots`?