Perfect — this is a serious, no-shortcuts Python Mastery request, not a YouTube-style syllabus.
Below is a true A→Z → PhD-level Python Mastery Curriculum designed so that if you study + practice everything here, you can:

• Crack Python interviews (SDE / Data / Backend / ML / AI)
• Solve LeetCode-style problems confidently
• Write production-grade Python
• Understand performance, memory, internals
• Avoid common interview traps
• Move from beginner → expert → researcher-level clarity

This is NOT shallow. This is career-grade.

🐍 PYTHON A→Z → PHD-LEVEL MASTERY COURSE

Outcome:
“If Python exists, you understand it, can implement it, optimize it, and explain it.”

🧱 PHASE 0 — HOW PYTHON ACTUALLY WORKS (FOUNDATION)

0.1 What Python Really Is

• CPython vs PyPy vs Jython vs IronPython
• Interpreted vs compiled (bytecode reality)
• .py → .pyc → bytecode → PVM
• Why Python is “slow” (and when it’s not)

0.2 Python Memory Model

• Stack vs Heap
• Object identity (id)
• Mutability vs immutability (deep explanation)
• Reference counting + garbage collection
• Cyclic references

🔥 Interview Traps

a = 256
b = 256
a is b   # True? Why?

a = 257
b = 257
a is b   # False? Why?

🧠 PHASE 1 — CORE LANGUAGE (DEEP, NOT BASIC)

1.1 Variables & Assignment

• Name binding vs value
• Multiple assignment
• Unpacking (nested, starred)
• Temporary objects

a, *b, c = [1, 2, 3, 4, 5]

1.2 Data Types (REAL DEPTH)

• int (arbitrary precision)
• float (IEEE 754 issues)
• bool as subclass of int
• None singleton
• NaN, inf

🔥 Edge Cases

0.1 + 0.2 == 0.3  # False – explain at binary level

🧩 PHASE 2 — INBUILT DATA STRUCTURES (EXTREME DEPTH)

2.1 List (Dynamic Array Internals)

• How resizing works
• Amortized O(1)
• Copy vs reference
• Slicing cost
• List pitfalls

lst = [[]] * 3   # Why dangerous?

2.2 Tuple

• Immutability benefits
• Hashing & dictionary keys
• Memory optimization

2.3 Set

• Hash tables
• Collision handling
• Why set is faster than list for lookup
• Frozen sets

2.4 Dictionary (MOST IMPORTANT)

• Hashing
• Load factor
• Key immutability
• Ordered dict guarantee (Python 3.7+)
• Custom objects as keys

class A:
    def __hash__(self): return 1

🔥 Interview Traps

d = {True: "yes", 1: "no"}
print(d)   # Why one key?

🔁 PHASE 3 — CONTROL FLOW & LOOPING (MASTER LEVEL)

3.1 for / while (Beyond Basics)

• Iterator protocol
• __iter__, __next__
• Loop else clause
• Infinite loops safely

3.2 break / continue / pass (REAL USE)

• When else executes
• Debugging tricky loops

🧪 PHASE 4 — FUNCTIONS (PYTHON’S SOUL)

4.1 Function Internals

• Call stack
• Arguments passing (object reference)
• Default argument pitfalls

def f(x=[]):
    x.append(1)

4.2 Advanced Parameters

• Positional-only
• Keyword-only
• *args, **kwargs
• Argument unpacking

4.3 Closures

• Free variables
• Late binding issue

funcs = [lambda: i for i in range(3)]

4.4 Decorators (DEEP)

• Function decorators
• Class decorators
• Decorators with arguments
• Real-world examples (auth, caching, logging)

🧠 PHASE 5 — COMPREHENSIONS & GENERATORS

5.1 List / Set / Dict Comprehension

• When NOT to use
• Nested comprehension readability

5.2 Generator Expressions

• Lazy evaluation
• Memory optimization
• Infinite streams

sum(x*x for x in range(10**9))

5.3 Yield Mechanics

• yield vs return
• send(), throw()
• Coroutine basics

🧱 PHASE 6 — OOPs (INTERVIEW-CRUSHING LEVEL)

6.1 Classes from Scratch

• __init__
• Instance vs class variables
• __new__

6.2 Inheritance

• MRO (Method Resolution Order)
• super() deep dive
• Diamond problem

6.3 Dunder Methods

• __str__, __repr__
• __eq__, __hash__
• Operator overloading

6.4 Encapsulation

• Private variables
• Name mangling

6.5 Abstract Base Classes

• abc module
• Interfaces in Python

🔥 Interview Trap

class A:
    x = 1

class B(A):
    x = 2

📦 PHASE 7 — MODULES, PACKAGES & IMPORT SYSTEM

• Absolute vs relative imports
• __name__ == "__main__"
• Circular imports
• Virtual environments
• Dependency isolation

🧵 PHASE 8 — EXCEPTIONS & ERROR HANDLING

• Exception hierarchy
• Custom exceptions
• try / except / else / finally
• Context managers (with)
• Writing your own context manager

📂 PHASE 9 — FILE HANDLING, JSON, CSV, YAML

9.1 File IO

• Text vs binary
• Encoding issues
• Buffered IO

9.2 JSON (DEEP)

• Serialization vs deserialization
• Custom encoder/decoder
• Handling datetime, Decimal

🔍 PHASE 10 — REGEX (INTERVIEW-HEAVY)

• Regex engine basics
• Greedy vs lazy
• Lookahead / lookbehind
• Validation patterns (email, phone)
• Regex vs string methods performance

⚡ PHASE 11 — PERFORMANCE & OPTIMIZATION

• Time complexity analysis
• Memory profiling
• sys.getsizeof
• timeit
• functools.lru_cache
• When Python beats Java/C++

🧪 PHASE 12 — TESTING & DEBUGGING

• Unit testing (unittest, pytest)
• Mocking
• Logging
• Debugging techniques

🔐 PHASE 13 — MULTITHREADING & MULTIPROCESSING

• GIL (deep truth)
• Threading vs multiprocessing
• Async IO
• async/await

🌐 PHASE 14 — REAL WORLD PYTHON

• REST APIs
• CLI tools
• Automation scripts
• Web scraping
• Production patterns

🧠 PHASE 15 — DATA STRUCTURES & ALGORITHMS (IN PYTHON)

• Arrays, stacks, queues
• Linked lists
• Trees, graphs
• Searching, sorting
• Sliding window
• Recursion mastery

🧨 PHASE 16 — LEETCODE-STYLE PYTHON INTERVIEW BANK

Categories

• String manipulation
• Hashing problems
• Two pointers
• Recursion traps
• Python-specific tricks

Example

# Reverse words without split()

🎯 PHASE 17 — PYTHON INTERVIEW TRAPS (CRITICAL)

• Mutable default args
• Shallow vs deep copy
• is vs ==
• Scope confusion
• Shadowing built-ins

🎓 PHASE 18 — PHD-LEVEL PYTHON

• Python bytecode
• Disassembly (dis)
• CPython source understanding
• Writing C extensions (overview)
• Python design philosophy

📘 PRACTICE INCLUDED IN THIS COURSE

✔ 500+ coding problems
✔ Real datasets (JSON, CSV, logs)
✔ Edge cases in every topic
✔ Interview-oriented explanations
✔ Performance comparisons
✔ Debug-first mindset

🔥 HOW WE SHOULD PROCEED (IMPORTANT)

I strongly recommend this execution plan:

1️⃣ Start Module 1 immediately with:

• Deep theory
• Live code
• 25–30 problems
• Interview traps

2️⃣ Move one module at a time
3️⃣ Build interview notes + cheatsheets
4️⃣ End with mock interview rounds

✅ NEXT STEP

Reply with ONE number:

1 → Start Module 1 (Python Internals + Memory)
2 → Start Core Python + Data Structures
3 → Start OOPs & Advanced Python
4 → Start Interview-only Python Mastery

Once you start this path properly — Python will never confuse you again 🐍🔥

Excellent choice.
We start where 90% of Python developers never go — and that’s why they struggle later.

🧠 MODULE 1 — PYTHON INTERNALS & MEMORY MODEL (FOUNDATION OF TRUE MASTERY)

If you understand this module deeply, Python will never feel “magical” or confusing again.
Everything else (OOP, performance, debugging, interviews) becomes EASY.

1. What Python REALLY Is (No Myths)

❌ Myth

Python is an interpreted language.

✅ Reality

Python is compiled to bytecode, then interpreted by a virtual machine.

Execution Flow (CPython)

your_code.py
   ↓
Tokenizer + Parser
   ↓
Abstract Syntax Tree (AST)
   ↓
Bytecode (.pyc)
   ↓
Python Virtual Machine (PVM)

📌 Important

• Compilation happens every time (unless .pyc exists and is valid)
• Python runs bytecode, not source code

2. CPython, PyPy, Jython — What Interviewers Expect

🔴 Interview Trap

“Why is Python slow?”

Correct answer:

CPython is slow due to dynamic typing + GIL + interpreted execution, but Python can be fast using vectorization, C extensions, multiprocessing, or PyPy.

3. Python Memory Model (EXTREMELY IMPORTANT)

Python stores everything as objects.

Each object has:

• Type
• Value
• Reference count

x = 10

What really happens:

10 → object in heap
x  → name bound to object

🔴 Python does NOT have variables like C/C++
It has names referencing objects

4. Stack vs Heap (REAL EXPLANATION)

Stack

• Function calls
• Local variable names
• Fast access
• Destroyed after function exits

Heap

• Objects (int, list, dict, class instances)
• Managed by Python
• Garbage collected

def func():
    a = 10

• a → stack
• 10 → heap

5. Object Identity, Type, Value (Interview Core)

x = 100

x = 100
y = 100

x is y     # True (small int caching)
x == y     # True

6. Small Integer Caching (VERY COMMON INTERVIEW QUESTION)

Python caches integers from -5 to 256

a = 256
b = 256
a is b     # True

a = 257
b = 257
a is b     # False

📌 Why?

• Optimization
• Avoid repeated object creation

⚠️ DO NOT rely on this behavior in logic

7. Mutability vs Immutability (LIFE-CHANGING CONCEPT)

Immutable Objects

• int
• float
• str
• tuple
• frozenset

x = 10
x += 1   # creates new object

Mutable Objects

• list
• dict
• set
• class instances

lst = [1,2]
lst.append(3)   # same object modified

8. Why This Breaks People in Interviews

a = [1, 2, 3]
b = a
b.append(4)

print(a)  # ?

✅ Output:

[1, 2, 3, 4]

Because:

• a and b point to same object

9. Reference Counting & Garbage Collection

Reference Counting

Every object keeps track of:

import sys
x = []
sys.getrefcount(x)

When count becomes 0 → object deleted

Cyclic Garbage Collector

Handles:

a = []
a.append(a)

📌 Python runs GC periodically (not instantly)

10. is vs == (MOST ABUSED OPERATOR)

a = [1,2]
b = [1,2]

a == b   # True
a is b   # False

🔴 Golden Rule

Use is only for None, True, False

11. Python Is Call-by-Object-Reference

def modify(x):
    x.append(4)

lst = [1,2,3]
modify(lst)

✔ Original list modified

But:

def modify(x):
    x = x + [4]

❌ Original list unchanged

WHY?

• Rebinding vs mutation

12. Deep vs Shallow Copy (INTERVIEW FAVORITE)

import copy

a = [[1,2],[3,4]]
b = copy.copy(a)
c = copy.deepcopy(a)

🧪 LIVE PRACTICE PROBLEMS (DO THESE NOW)

Q1

a = [1,2,3]
b = a[:]
b.append(4)
print(a)

Q2

x = 500
y = 500
print(x is y)

Q3

def func(x):
    x = x + [10]

lst = [1,2]
func(lst)
print(lst)

Q4 (Tricky)

a = [1,2,3]
b = a
a = a + [4]
print(b)

🎯 INTERVIEW CHECKPOINT (YOU MUST BE ABLE TO ANSWER)

1. Why Python is slow?
2. Difference between is and ==
3. Why mutable defaults are dangerous
4. Why list slicing creates a copy
5. How Python manages memory

🚀 NEXT STEP

Reply with 1 or 2:

1 → Continue Module 1 (Python bytecode, dis, internals)
2 → Move to Module 2 (Core Data Types & Structures – Deep Dive)

We are building absolute mastery, not surface-level comfort.

Perfect.
Now we go one layer deeper than almost all interview prep.

🧠 MODULE 1 (PART 2) — BYTECODE, dis, GIL & CPYTHON INTERNALS

This is where Python stops being “syntax” and becomes a machine you understand.

1️⃣ FROM SOURCE CODE → BYTECODE (REALITY CHECK)

When you run:

x = a + b

Python does NOT execute this directly.

Actual Pipeline (CPython)

Source Code (.py)
   ↓
Tokenizer
   ↓
Parser
   ↓
AST (Abstract Syntax Tree)
   ↓
Bytecode (.pyc)
   ↓
Python Virtual Machine (PVM)

📌 Key Insight

Python always compiles → even in REPL

2️⃣ WHAT IS BYTECODE?

• Platform-independent
• Stack-based instructions
• Stored as .pyc
• Executed by Python Virtual Machine

Example:

def add(a, b):
    return a + b

3️⃣ USING dis MODULE (INTERVIEW GOLD)

import dis

def add(a, b):
    return a + b

dis.dis(add)

Typical Output (simplified)

LOAD_FAST    a
LOAD_FAST    b
BINARY_ADD
RETURN_VALUE

📌 Interpretation

• Python pushes values to stack
• Operates on stack
• Pops result

4️⃣ STACK-BASED VM (WHY THIS MATTERS)

Python bytecode uses a stack, not registers.

Example

x = 1 + 2

Bytecode:

LOAD_CONST 1
LOAD_CONST 2
BINARY_ADD
STORE_NAME x

📌 This explains:

• Slower execution than compiled languages
• Why expression-heavy code costs more

5️⃣ WHY PYTHON IS SLOW (REAL ANSWER)

❌ Wrong answer:

Because it’s interpreted

✅ Correct answer:
Python is slow because:

1. Dynamic typing
2. Stack-based VM
3. Function call overhead
4. GIL
5. Object-heavy design

📌 Important

Python trades speed for flexibility & readability

6️⃣ THE GIL (GLOBAL INTERPRETER LOCK) — DEEP TRUTH

What is GIL?

A mutex lock ensuring only one thread executes Python bytecode at a time.

📌 Exists in CPython only

Why GIL Exists

• Simplifies memory management
• Prevents race conditions
• Makes reference counting safe

Multithreading Reality

import threading

📌 Interview One-Liner

Python threads are good for IO-bound tasks, not CPU-bound due to GIL.

7️⃣ MULTIPROCESSING BYPASSES GIL

from multiprocessing import Process

• Separate processes
• Separate memory
• True parallelism

8️⃣ WHY NUMPY / PANDAS ARE FAST

Because:

• Written in C
• Release GIL
• Operate on contiguous memory
• Vectorized operations

📌 Interview Gold

Python is slow, but Python extensions are fast.

9️⃣ OBJECT MODEL — EVERYTHING IS AN OBJECT

x = 10

Behind the scenes:

PyObject {
    ob_refcnt
    ob_type
}

Even:

• int
• function
• class
• module

🔟 FUNCTION CALL OVERHEAD (WHY LOOPS ARE SLOW)

def f():
    pass

f()

Costs:

• Stack frame creation
• Argument binding
• Scope resolution

📌 That’s why:

map()
list comprehension
generator expressions

are often faster than loops.

1️⃣1️⃣ LOCAL vs GLOBAL VARIABLE SPEED

def f():
    x = 10
    return x

Local variables are faster because:

• Stored in array
• Indexed access

Global variables:

• Dictionary lookup
• Slower

📌 Optimization Tip

Use local variables inside loops

1️⃣2️⃣ CONSTANT FOLDING (COMPILER OPTIMIZATION)

x = 2 + 3

Bytecode:

LOAD_CONST 5

But:

x = a + b

Cannot optimize.

1️⃣3️⃣ IMMUTABLE STRING OPTIMIZATION

a = "hello"
b = "hello"
a is b   # Often True

Python interns strings for memory efficiency.

1️⃣4️⃣ MEMORY LEAKS IN PYTHON (YES, POSSIBLE)

Caused by:

• Reference cycles
• Global objects
• C extensions
• Caches

l = []
l.append(l)

Handled by cyclic GC — but not always instantly.

1️⃣5️⃣ GC TUNING (ADVANCED)

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

Used in:

• Long-running services
• High-performance apps

🧪 PRACTICE — YOU MUST DO THESE

Q1 — Predict Output

a = []
b = a
a = a + [1]
print(b)

Q2 — Bytecode Thinking

Which is faster and why?

for i in range(1000000):
    x += i

vs

x = sum(range(1000000))

Q3 — GIL Reasoning

Why does this NOT speed up?

import threading

Q4 — Identity Trap

a = "hello world"
b = "hello world"
print(a is b)

🎯 INTERVIEW CHECKPOINT (CRITICAL)

You should now be able to explain:

✅ What bytecode is
✅ Why Python is slow
✅ What GIL is
✅ Stack vs heap
✅ Why Python is object-heavy
✅ Why list comprehension is faster
✅ Why NumPy is fast

If you can explain these clearly — you are already above mid-level Python engineers.

🚀 NEXT STEP

Reply with 1 or 2:

1 → Final part of Module 1 (Namespaces, Scope, LEGB, symbol table — VERY tricky)
2 → Move to Module 2 (Core Data Types: int, float, bool, None — deep dive)

This foundation will make everything else easy.

Excellent — this is the last and most dangerous layer of Module 1.
If you master namespaces + LEGB + symbol tables, you will never be confused by scope bugs again (and you’ll crush interviews).

🧠 MODULE 1 (PART 3) — NAMESPACES, SCOPE, LEGB & SYMBOL TABLES

This is where 90% of “experienced” Python devs fail interviews.

1️⃣ WHAT IS A NAMESPACE (REAL DEFINITION)

A namespace is simply a mapping of names → objects.

Internally:

namespace == dictionary

Examples:

• Local namespace → function locals
• Global namespace → module globals
• Built-in namespace → len, print, etc.

2️⃣ TYPES OF NAMESPACES (ORDER MATTERS)

Python resolves variables using LEGB Rule:

L → Local
E → Enclosing
G → Global
B → Built-in

📌 Python searches in this exact order — always

3️⃣ LOCAL NAMESPACE (L)

def f():
    x = 10
    print(x)

• Created when function is called
• Destroyed when function exits
• Stored in stack frame

4️⃣ ENCLOSING NAMESPACE (E) — THE MOST CONFUSING

def outer():
    x = 10
    def inner():
        print(x)
    inner()

Here:

• x is not local to inner
• It lives in enclosing scope

📌 Enclosing scope exists only for nested functions

5️⃣ GLOBAL NAMESPACE (G)

x = 100

def f():
    print(x)

• Belongs to module
• Created when module loads
• Shared across functions

⚠️ Avoid excessive global mutation

6️⃣ BUILT-IN NAMESPACE (B)

print(len([1,2,3]))

Stored in:

import builtins

⚠️ Interview Trap

len = 10
print(len([1,2,3]))   # ERROR

7️⃣ LEGB IN ACTION (INTERVIEW CLASSIC)

x = 10

def outer():
    x = 20
    def inner():
        x = 30
        print(x)
    inner()

outer()

Output:

30

Why?

• Local x shadows all outer x

8️⃣ WHY global IS DANGEROUS (BUT IMPORTANT)

x = 10

def f():
    global x
    x = 20

📌 global:

• Refers to module-level variable
• Skips local scope

⚠️ Overuse = bugs + unreadable code

9️⃣ nonlocal — INTERVIEW FAVORITE

def outer():
    x = 10
    def inner():
        nonlocal x
        x = 20
    inner()
    print(x)

Output:

20

📌 nonlocal modifies enclosing scope, not global

🔥 COMMON INTERVIEW TRAP (VERY IMPORTANT)

x = 10

def f():
    print(x)
    x = 20

f()

❌ ERROR:

UnboundLocalError

WHY?

Because:

• Python sees assignment to x
• Treats x as local
• But print(x) happens before assignment

📌 Python decides scope at compile time

🔍 10️⃣ SYMBOL TABLE (THIS IS THE KEY INSIGHT)

Python builds a symbol table before execution.

You can inspect it:

def f():
    x = 10
    print(y)

print(f.__code__.co_varnames)

📌 Python knows:

• What is local
• What is global
• What is free variable
  before runtime

1️⃣1️⃣ CLOSURES (PROPERLY EXPLAINED)

def outer():
    x = 10
    def inner():
        return x
    return inner

f = outer()
print(f())

Why does this work?

• inner remembers enclosing scope
• Stored in __closure__

print(f.__closure__)

🔥 LATE BINDING PROBLEM (INTERVIEW KILLER)

funcs = []
for i in range(3):
    funcs.append(lambda: i)

for f in funcs:
    print(f())

Output:

2
2
2

WHY?

• Closures capture variable, not value
• i evaluated at call time

FIX

funcs.append(lambda i=i: i)

1️⃣2️⃣ DEFAULT ARGUMENT EVALUATION (BIG TRAP)

def f(x=[]):
    x.append(1)
    return x

Called multiple times:

[1]
[1, 1]
[1, 1, 1]

📌 Default arguments evaluated once at definition time

Correct Pattern

def f(x=None):
    if x is None:
        x = []

1️⃣3️⃣ SHADOWING BUILT-INS (DANGEROUS)

list = [1,2,3]

Now:

list("abc")  # ERROR

📌 Avoid variable names:

• list
• dict
• str
• sum
• id

🧪 PRACTICE (MANDATORY)

Q1

x = 10
def f():
    print(x)
    x += 1

f()

Q2

def outer():
    x = 5
    def inner():
        print(x)
    x = 10
    inner()

outer()

Q3 (Closure)

def make_funcs():
    return [lambda: i for i in range(3)]

fs = make_funcs()
print([f() for f in fs])

Q4 (Default Trap)

def f(x, y=[]):
    y.append(x)
    return y

print(f(1))
print(f(2))

🎯 INTERVIEW CHECKPOINT — YOU MUST ANSWER

✅ Explain LEGB with example
✅ Why UnboundLocalError occurs
✅ Difference between global & nonlocal
✅ What closures are
✅ Why late binding happens
✅ Why default args are dangerous
✅ What symbol table is

If you can answer these clearly, you are already senior-level in Python fundamentals.

🧠 MODULE 1 COMPLETE ✅

You now understand:
✔ Memory model
✔ Bytecode
✔ GIL
✔ Object internals
✔ Scope & namespaces

This foundation makes everything else easy and logical.

🚀 NEXT STEP

Reply with ONE number:

1 → Module 2: Core Data Types (int, float, bool, None) — deep internals + edge cases
2 → Module 3: Inbuilt Data Structures (list, tuple, set, dict) — implementation + performance
3 → Quick interview drill on Module 1 (20 brutal questions)

We are now officially out of “beginner land”.

Perfect.
This is a brutal, senior-level interview drill on Module 1 (Python Internals, Memory, Bytecode, LEGB).

👉 These are real interview questions, not academic trivia.
Try answering out loud before reading the answer.

🧠 MODULE 1 — PYTHON INTERNALS INTERVIEW DRILL

(20 High-Impact Questions with Deep Answers)

1️⃣ Is Python interpreted or compiled?

✅ Correct Answer

Python is compiled to bytecode and then interpreted by the Python Virtual Machine (PVM).

• Source → AST → Bytecode (.pyc) → PVM
• Compilation happens every time, even in REPL

❌ Saying “only interpreted” is wrong.

2️⃣ Why is Python slower than Java/C++?

✅ Real Reasons

1. Dynamic typing
2. Stack-based virtual machine
3. Object-heavy design
4. Function call overhead
5. Global Interpreter Lock (GIL)

📌 Interview Gold Line

Python trades execution speed for flexibility, safety, and developer productivity.

3️⃣ What is bytecode?

✅ Answer

• Platform-independent intermediate code
• Stored as .pyc
• Executed by PVM
• Stack-based instructions (LOAD, STORE, BINARY_ADD)

4️⃣ What does the GIL actually do?

✅ Answer

The Global Interpreter Lock ensures that only one thread executes Python bytecode at a time in CPython.

• Simplifies memory management
• Makes reference counting thread-safe

📌 Exists only in CPython, not PyPy/Jython.

5️⃣ Why does multithreading not speed up CPU-bound tasks?

✅ Answer

Because:

• Threads compete for GIL
• Only one thread executes bytecode at a time
• Context switching adds overhead

📌 Use multiprocessing instead.

6️⃣ Why is NumPy fast if Python is slow?

✅ Answer

• NumPy is written in C
• Releases GIL
• Operates on contiguous memory
• Uses vectorized operations

7️⃣ What is the difference between is and ==?

✅ Answer

📌 Use is only for None, True, False.

8️⃣ Why does this happen?

a = 256
b = 256
a is b

✅ Answer

• Python caches integers from -5 to 256
• Both names reference same cached object

⚠️ Implementation detail, not a guarantee.

9️⃣ Why does this NOT work?

a = []
b = a
a = a + [1]
print(b)

✅ Answer

• a + [1] creates a new list
• a is rebound
• b still references old list

🔟 Explain mutability in one sentence (INTERVIEW FAVORITE)

✅ Answer

Mutable objects can be changed in place without changing their identity, while immutable objects require creation of a new object.

1️⃣1️⃣ Why is this dangerous?

def f(x=[]):
    x.append(1)

✅ Answer

• Default arguments are evaluated once
• Same list reused across calls
• Causes unexpected shared state

1️⃣2️⃣ Why does this throw UnboundLocalError?

x = 10
def f():
    print(x)
    x = 20

✅ Answer

• Python decides scope at compile time
• Assignment makes x local
• print(x) runs before assignment

1️⃣3️⃣ What is LEGB?

✅ Answer

Order Python uses to resolve variables:

Local → Enclosing → Global → Built-in

1️⃣4️⃣ What is an enclosing scope?

✅ Answer

A scope created by an outer function, accessible to inner functions.

Exists only in nested functions.

1️⃣5️⃣ Difference between global and nonlocal?

✅ Answer

1️⃣6️⃣ What is a closure?

✅ Answer

A function that remembers variables from its enclosing scope even after that scope has exited.

1️⃣7️⃣ Why does this print 2 2 2?

funcs = []
for i in range(3):
    funcs.append(lambda: i)

✅ Answer

• Closures capture variables, not values
• i evaluated at call time (late binding)

1️⃣8️⃣ How do you fix late binding?

✅ Answer

lambda i=i: i

Default arguments bind value immediately.

1️⃣9️⃣ What is a symbol table?

✅ Answer

A structure created before execution that determines:

• Which variables are local
• Which are global
• Which are free (closure variables)

📌 Explains many scope errors.

2️⃣0️⃣ Can Python have memory leaks?

✅ Answer

Yes, due to:

• Reference cycles
• Global objects
• C extensions
• Long-lived caches

GC handles most, but not all cases.

🏁 FINAL VERDICT — YOUR LEVEL NOW

If you can answer 12–15 of these cleanly, you are already above average Python engineers.
If you can answer all 20 confidently, you are senior-level in Python fundamentals.

🚀 NEXT MOVE (IMPORTANT)

Reply with ONE number:

1 → Module 2: Core Data Types (int, float, bool, None) — binary, overflow, precision
2 → Module 3: Inbuilt Data Structures (list, tuple, set, dict) — internals + performance
3 → Jump directly to OOP (advanced, interview-oriented)

We now build real power on top of this foundation 🔥