Absolutely 👍
Here is the missing Module 3, recreated in the same A → Z → PhD-level depth we designed earlier — not a shallow “how to use lists” module, but a CPython-internals + performance + interview-killer module.

🧠 Module 3: Python Inbuilt Data Structures

(Lists, Tuples, Sets, Dictionaries — Internals, Performance & Traps)

Outcome of this module
After this module, you should be able to:

• Predict time & space complexity without memorizing
• Explain why Python behaves the way it does internally
• Write cache-friendly, memory-efficient code
• Crack FAANG / Big Tech Python interviews
• Debug weird bugs caused by mutability, hashing, resizing

📘 Module 3.1 — Python Object Model (Foundation for Everything)

Before touching lists or dicts, you must understand how Python stores objects.

3.1.1 Everything Is an Object

x = 10

Internally:

x ──► PyObject*
         ├── ref_count
         ├── type pointer (PyLong_Type)
         └── value (digits array)

3.1.2 Variable ≠ Value

• Variables store references
• Containers store references to objects
• Mutability operates on object, not variable

📌 Interview trap:

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

📘 Module 3.2 — List (Dynamic Array Internals)

3.2.1 What Is a Python List?

• Backed by a dynamic array
• Stores references, not actual objects
• Homogeneous at C-level (PyObject*), heterogeneous logically

3.2.2 Memory Layout (Critical)

list ──► [ ptr | ptr | ptr | ptr | ... ]
           │     │
           ▼     ▼
         PyInt  PyStr

3.2.3 List Over-Allocation Strategy (🔥 PhD Topic)

Python grows lists to reduce reallocations.

Approx growth:

new_size = old_size * 1.125 + 6

📌 Why?

• Appends are amortized O(1)
• Prevents frequent realloc()

3.2.4 Time Complexity (Explainable)

* amortized

📌 Interview explanation:

Append is O(1) because Python pre-allocates extra memory.

3.2.5 Common List Traps

a = [[]] * 3
a[0].append(1)
print(a)  # [[1], [1], [1]]

Why?

• Same inner list referenced 3 times

📘 Module 3.3 — Tuple (Immutable but Not Simple)

3.3.1 Tuple Internals

• Fixed-size array of references
• Immutable structure, mutable contents allowed

t = ([1, 2], 3)
t[0].append(99)  # VALID

3.3.2 Why Tuples Are Faster

• No resize logic
• Smaller memory footprint
• Hashable (if elements are hashable)

3.3.3 Tuple Hashing

hash((1, 2, 3))  # precomputed + cached

📌 Used heavily in:

• dict keys
• set elements
• caching (LRU, memoization)

3.3.4 Tuple Packing / Unpacking

a, b, c = 1, 2, 3

Internally optimized with:

• No intermediate objects
• Stack-friendly execution

📘 Module 3.4 — Set (Hash Table Without Values)

3.4.1 Set Internals

• Implemented as hash table
• Stores only keys, no values
• Uses open addressing

hash(key) → index → probe sequence

3.4.2 Why Sets Are Fast

3.4.3 Hashing Rules

• Elements must be hashable
• Hash must not change during lifetime

❌ Invalid:

{[1, 2]}   # TypeError

3.4.4 Collision Handling

• Linear probing
• Perturb shift algorithm
• Resizes at ~2/3 capacity

📌 Interview killer:

Sets are faster than lists for membership because lists scan, sets hash.

📘 Module 3.5 — Dictionary (The Crown Jewel of Python)

3.5.1 Dict Internals (CPython ≥ 3.6)

• Compact dict
• Separate keys & values arrays
• Preserves insertion order

Indices Table → Entry Array (key, value, hash)

3.5.2 Why Dict Lookup Is O(1)

1. Compute hash
2. Jump to index
3. Probe if collision

3.5.3 Resize Strategy

• Resize when load factor ~ 2/3
• Rehash all keys (expensive but rare)

3.5.4 Mutability & Hashing Rules

3.5.5 Dict Order Guarantee (Important)

Since Python 3.7:

dict preserves insertion order

📌 Interview trap:

This is a language guarantee, not an implementation accident.

📘 Module 3.6 — Mutability Matrix (Very Important)

📘 Module 3.7 — Performance Comparisons

Membership Test

x in list   # O(n)
x in set    # O(1)
x in dict   # O(1)

Memory Usage

tuple < list < dict < set

When to Use What

📘 Module 3.8 — Advanced Interview Traps

Trap 1: Default Mutable Arguments

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

Trap 2: Shallow vs Deep Copy

import copy
copy.copy()
copy.deepcopy()

Trap 3: Dict Views Are Dynamic

d = {1: 2}
keys = d.keys()
d[3] = 4
print(keys)  # updates

📘 Module 3.9 — Coding Exercises (Mandatory)

Easy → Medium

1. Detect duplicates using set
2. Reverse dict mapping
3. Merge two dicts with conflict resolution
4. Flatten nested list
5. Count frequency efficiently

Hard / Interview

6. Implement LRU cache using dict + doubly linked list
7. Custom hashable class
8. Memory-optimized frequency counter
9. Deep copy without copy
10. Implement OrderedDict logic manually

📘 Module 3.10 — Internal CPython Source Reading (Optional PhD)

Files to study:

• listobject.c
• dictobject.c
• setobject.c

📌 You don’t memorize this — you understand patterns.

🧠 Module 3 Mental Model (One Line Each)

• List → Dynamic array of references
• Tuple → Fixed array, cache-friendly
• Set → Hash table without values
• Dict → Optimized hash table with order

If you want next, I can:

• Convert this into printable PDF notes
• Add LeetCode-style problems
• Add CPython memory diagrams
• Add FAANG interview Q&A
• Or integrate it perfectly back into your A→Z course flow

Just tell me 🔥

Perfect question — this is exactly where Module 3 becomes truly PhD-level.

Below is a complete expansion of Module 3, covering everything that was missing:

✅ Module 3 (Extended)

Coding Exercises — Solutions

LeetCode-Style Problems

CPython Memory Diagrams

Inbuilt Data Structure Methods (with real use-cases & traps)

🧠 PART 1 — CPython Memory Diagrams (Very Important)

1️⃣ List Memory Diagram (Dynamic Array)

lst = [10, "hi", [1,2]]

lst ──► PyListObject
        ├── ob_refcnt
        ├── ob_type
        ├── ob_size = 3
        ├── allocated = 4
        └── ob_item ──► [ ptr | ptr | ptr | NULL ]
                          │     │     │
                          ▼     ▼     ▼
                        PyInt  PyStr  PyList

Key truths

• List stores references only
• allocated > size → amortized O(1) append
• Inner list is separate object

2️⃣ Tuple Memory Diagram

t = (1, 2, 3)

t ──► PyTupleObject
       ├── ob_refcnt
       ├── ob_type
       ├── ob_size = 3
       └── ob_item ──► [ ptr | ptr | ptr ]

✔ Fixed size
✔ No reallocation
✔ Hash cached (if hashable)

3️⃣ Dict Memory Diagram (Compact Dict)

d = {"a": 1, "b": 2}

PyDictObject
 ├── ma_keys ──► indices table
 │                [ -1 |  0 | -1 | 1 ]
 │                       │         │
 └── ma_values ──► entries array
                  [ ("a",1,h1), ("b",2,h2) ]

Why dict is fast

• Hash → index jump
• Probe only on collision
• Keys & values stored separately (cache friendly)

4️⃣ Set Memory Diagram

s = {1, 2, 3}

PySetObject
 └── table ──► [ 1 | 2 | EMPTY | 3 ]

✔ Same hashing engine as dict
✔ No values → faster lookup

🧠 PART 2 — Module 3.9 Coding Exercises (Solved)

1️⃣ Detect Duplicates Using Set

def has_duplicates(nums):
    seen = set()
    for n in nums:
        if n in seen:
            return True
        seen.add(n)
    return False

Time: O(n)
Space: O(n)

2️⃣ Reverse Dict Mapping

def reverse_dict(d):
    rev = {}
    for k, v in d.items():
        rev.setdefault(v, []).append(k)
    return rev

{"a":1,"b":1,"c":2}
→ {1:["a","b"], 2:["c"]}

3️⃣ Merge Dicts With Conflict Resolution

def merge_dicts(d1, d2):
    return {k: d1.get(k, 0) + d2.get(k, 0)
            for k in d1.keys() | d2.keys()}

✔ Uses set union on keys

4️⃣ Flatten Nested List (Recursive)

def flatten(lst):
    res = []
    for x in lst:
        if isinstance(x, list):
            res.extend(flatten(x))
        else:
            res.append(x)
    return res

5️⃣ Frequency Counter (Optimized)

def freq_counter(arr):
    freq = {}
    for x in arr:
        freq[x] = freq.get(x, 0) + 1
    return freq

✔ Avoids if x in dict

6️⃣ LRU Cache (🔥 FAANG Favourite)

from collections import OrderedDict

class LRUCache:
    def __init__(self, cap):
        self.cap = cap
        self.cache = OrderedDict()

    def get(self, k):
        if k not in self.cache:
            return -1
        self.cache.move_to_end(k)
        return self.cache[k]

    def put(self, k, v):
        if k in self.cache:
            self.cache.move_to_end(k)
        self.cache[k] = v
        if len(self.cache) > self.cap:
            self.cache.popitem(last=False)

7️⃣ Custom Hashable Class

class Point:
    def __init__(self, x, y):
        self.x, self.y = x, y

    def __hash__(self):
        return hash((self.x, self.y))

    def __eq__(self, other):
        return self.x == other.x and self.y == other.y

✔ Can be dict key / set element

8️⃣ Memory-Optimized Frequency (Tuple Keys)

freq = {}
for x, y in points:
    freq[(x, y)] = freq.get((x, y), 0) + 1

✔ No object wrapper → faster

9️⃣ Deep Copy Without copy

def deep_copy(obj):
    if isinstance(obj, dict):
        return {k: deep_copy(v) for k, v in obj.items()}
    if isinstance(obj, list):
        return [deep_copy(x) for x in obj]
    return obj

🔟 Manual OrderedDict (Conceptual)

class Node:
    def __init__(self, k, v):
        self.k, self.v = k, v
        self.prev = self.next = None

✔ Dict → O(1) lookup
✔ DLL → O(1) eviction

🧠 PART 3 — LeetCode-Style Problems (With Insights)

🟢 Easy

Contains Duplicate

return len(nums) != len(set(nums))

Valid Anagram

from collections import Counter
return Counter(s) == Counter(t)

🟡 Medium

Two Sum

seen = {}
for i, n in enumerate(nums):
    if target - n in seen:
        return [seen[target-n], i]
    seen[n] = i

Group Anagrams

res = {}
for w in strs:
    key = tuple(sorted(w))
    res.setdefault(key, []).append(w)
return list(res.values())

✔ Tuple key → hashable

🔴 Hard

Longest Consecutive Sequence

nums = set(nums)
best = 0
for n in nums:
    if n - 1 not in nums:
        cur = n
        length = 1
        while cur + 1 in nums:
            cur += 1
            length += 1
        best = max(best, length)
return best

✔ O(n)

🧠 PART 4 — Inbuilt Methods (⚠️ Often Missed)

✅ LIST METHODS

✅ TUPLE METHODS

✔ Minimal API → speed

✅ SET METHODS

✅ DICT METHODS

🧠 FINAL INTERVIEW INSIGHT

Python performance is not magic
It is data structure engineering

If you understand:

• hashing
• mutability
• references
• resizing strategies

👉 You automatically crack Python interviews

🔥 Want Next?

I can:

• Turn this into printable PDF
• Add 100 more LeetCode problems mapped to DS
• Add CPython source-code walkthrough
• Add memory profiling labs
• Integrate this into your Python → AI → Spark journey

Just say the word 🚀

Excellent.
This is Module 3 – Part 2, where Python data structures stop being “containers” and start becoming problem-solving weapons.

This module maps directly to real interview questions (FAANG / product companies / data roles).

🧠 MODULE 3 (PART 2) — ADVANCED DATA STRUCTURES

dict internals · defaultdict · Counter · deque · heapq

3.21 dict — ADVANCED USAGE & INTERNAL BEHAVIOR

🔹 Why dict is so powerful

• Hash table with O(1) average lookup
• Extremely optimized in CPython
• Backbone of:
  • Sets
  • Objects
  • Namespaces
  • Caches
  • JSON parsing

3.22 dict.get() vs Direct Indexing (INTERVIEW FAVORITE)

d = {"a": 1}

d["b"]        # ❌ KeyError
d.get("b")    # None
d.get("b", 0) # Default

📌 Use get() when key may not exist.

3.23 setdefault() — POWERFUL BUT DANGEROUS

d = {}
d.setdefault("x", []).append(1)
d.setdefault("x", []).append(2)
print(d)

Output:

{'x': [1, 2]}

⚠️ Trap:

• Default object is created only once
• Similar to mutable default arguments

📌 Prefer defaultdict for clarity.

3.24 defaultdict — INTERVIEW GOLD

from collections import defaultdict

d = defaultdict(list)
d["a"].append(1)
d["a"].append(2)

Why interviewers love it:

• Eliminates key-existence checks
• Cleaner, faster, safer

Common Patterns

Frequency count

freq = defaultdict(int)
for ch in s:
    freq[ch] += 1

Grouping

groups = defaultdict(list)
for name, dept in employees:
    groups[dept].append(name)

3.25 Counter — FREQUENCY MASTER

from collections import Counter

Counter("banana")

Output:

{'a': 3, 'b': 1, 'n': 2}

Key Operations

c = Counter("banana")

c.most_common(2)   # [('a', 3), ('n', 2)]
c["a"]             # 3

📌 Zero / negative counts allowed.

3.26 Counter INTERVIEW TRAPS

Counter("ab") == Counter("aabb")

❌ False — counts differ.

But:

Counter("ab") <= Counter("aabb")

✔ True — subset comparison.

3.27 deque — THE CORRECT QUEUE

from collections import deque

dq = deque()
dq.append(1)
dq.appendleft(2)
dq.pop()
dq.popleft()

Why deque beats list for queues

📌 Interview Line

Use deque for queues, not lists.

3.28 deque ADVANCED FEATURES

dq.rotate(1)
dq.rotate(-1)

Sliding window problems:

dq = deque(maxlen=3)

3.29 heapq — PRIORITY QUEUE (VERY IMPORTANT)

import heapq

h = []
heapq.heappush(h, 3)
heapq.heappush(h, 1)
heapq.heappush(h, 2)

heapq.heappop(h)   # 1

📌 Python heap is min-heap.

3.30 MAX HEAP IN PYTHON (INTERVIEW CLASSIC)

Technique 1 — Negation

heapq.heappush(h, -x)

Technique 2 — Tuple

heapq.heappush(h, (-priority, item))

3.31 heapq TIME COMPLEXITY

Used in:

• Top-K problems
• Scheduling
• Dijkstra
• Median stream

3.32 COMPARISON TRAP WITH HEAPS

heapq.heappush(h, (priority, obj))

If priorities equal → Python tries to compare obj → ❌ TypeError

✅ Fix:

heapq.heappush(h, (priority, index, obj))

3.33 WHEN TO USE WHAT (INTERVIEW SUMMARY)

🧪 PRACTICE — INTERVIEW STYLE (MANDATORY)

Q1 — Group Anagrams

["eat","tea","tan","ate","nat","bat"]

Expected:

[['eat','tea','ate'], ['tan','nat'], ['bat']]

💡 Hint: defaultdict(list) + sorted key.

Q2 — Top K Frequent Elements

nums = [1,1,1,2,2,3]
k = 2

💡 Hint: Counter + heapq.

Q3 — First Unique Character

s = "leetcode"

💡 Hint: Counter.

Q4 — Sliding Window Maximum

nums = [1,3,-1,-3,5,3,6,7]
k = 3

💡 Hint: deque.

Q5 — Check If Two Strings Are Anagrams

Without sorting.

💡 Hint: Counter or frequency dict.

🎯 INTERVIEW CHECKPOINT (CRITICAL)

You should now clearly explain:

✅ Why defaultdict is safer than setdefault
✅ When to use Counter vs dict
✅ Why deque is O(1) for both ends
✅ How heapq works internally
✅ Why Python heap is min-heap
✅ How to build max-heap
✅ Common heap comparison traps

If yes → you are interview-ready for Python DS questions.

🚀 NEXT STEP (CHOOSE ONE)

Reply with ONE number:

1 → LeetCode-level DS coding problems (stack, hash, heap, sliding window — full solutions)
2 → Module 4: Functions, *args, **kwargs, closures, decorators (deep)
3 → 25-question rapid interview drill on Module 3

You’re now operating at serious Python interview depth 🔥

Perfect.
Below is exactly what strong interviews look like:

1️⃣ LeetCode-level DS coding problems (with clean Python solutions + interview thinking)
2️⃣ 25 rapid-fire interview drill questions on Module 3 (data structures)

No fluff. No basics. Pure interview dominance.

🔥 PART 1 — LEETCODE-LEVEL DATA STRUCTURE CODING

(Stack · Hash · Heap · Deque · Sliding Window)

🟢 1. Valid Parentheses

(LeetCode 20 — Stack)

def is_valid(s: str) -> bool:
    stack = []
    mapping = {')': '(', ']': '[', '}': '{'}

    for ch in s:
        if ch in mapping:
            if not stack or stack.pop() != mapping[ch]:
                return False
        else:
            stack.append(ch)

    return not stack

🎯 Interview focus

• Stack discipline
• Early exit
• O(n) time, O(n) space

🟢 2. Two Sum

(LeetCode 1 — Hash Map)

def two_sum(nums, target):
    seen = {}

    for i, num in enumerate(nums):
        need = target - num
        if need in seen:
            return [seen[need], i]
        seen[num] = i

🎯 Why interviewers love this

• Dict lookup O(1)
• One-pass solution

🟢 3. First Unique Character

(LeetCode 387 — Counter)

from collections import Counter

def first_uniq_char(s: str) -> int:
    freq = Counter(s)

    for i, ch in enumerate(s):
        if freq[ch] == 1:
            return i
    return -1

🎯 Tests frequency counting + traversal order.

🟡 4. Group Anagrams

(LeetCode 49 — defaultdict)

from collections import defaultdict

def group_anagrams(strs):
    groups = defaultdict(list)

    for word in strs:
        key = tuple(sorted(word))
        groups[key].append(word)

    return list(groups.values())

🎯 Follow-up

• Why tuple, not list → hashable

🟡 5. Top K Frequent Elements

(LeetCode 347 — Counter + Heap)

from collections import Counter
import heapq

def top_k_frequent(nums, k):
    freq = Counter(nums)
    return heapq.nlargest(k, freq.keys(), key=freq.get)

🎯 Interview insight

• nlargest avoids manual heap logic
• O(n log k)

🟡 6. Sliding Window Maximum

(LeetCode 239 — deque)

from collections import deque

def max_sliding_window(nums, k):
    dq = deque()
    res = []

    for i, num in enumerate(nums):
        if dq and dq[0] <= i - k:
            dq.popleft()

        while dq and nums[dq[-1]] < num:
            dq.pop()

        dq.append(i)

        if i >= k - 1:
            res.append(nums[dq[0]])

    return res

🎯 This is a killer interview problem

• Monotonic deque
• O(n) time

🔴 7. Find Median from Data Stream

(LeetCode 295 — Two Heaps)

import heapq

class MedianFinder:
    def __init__(self):
        self.small = []  # max heap
        self.large = []  # min heap

    def addNum(self, num):
        heapq.heappush(self.small, -num)

        if self.small and self.large and -self.small[0] > self.large[0]:
            heapq.heappush(self.large, -heapq.heappop(self.small))

        if len(self.small) > len(self.large) + 1:
            heapq.heappush(self.large, -heapq.heappop(self.small))
        if len(self.large) > len(self.small):
            heapq.heappush(self.small, -heapq.heappop(self.large))

    def findMedian(self):
        if len(self.small) > len(self.large):
            return -self.small[0]
        return (-self.small[0] + self.large[0]) / 2

🎯 Senior-level heap problem

⚡ PART 2 — MODULE 3 RAPID INTERVIEW DRILL

25 Questions (Answer mentally in <5 sec each)

1. Why is list.append() amortized O(1)?

👉 Over-allocation strategy.

2. Why is insert(0, x) slow?

👉 Requires shifting all elements → O(n).

3. Why does [[0]*3]*3 break?

👉 Same inner list reference repeated.

4. Difference: copy.copy() vs copy.deepcopy()?

👉 Shallow copies references, deep copies objects.

5. Why are tuples hashable but lists not?

👉 Tuples immutable, lists mutable.

6. Can a tuple contain a list?

👉 Yes, but tuple becomes unhashable.

7. Why is set lookup faster than list lookup?

👉 Hash table vs linear scan.

8. Why can’t list be a dict key?

👉 Mutable → hash instability.

9. Why do True and 1 collide in dict?

👉 True == 1 and same hash.

10. When to use defaultdict?

👉 Grouping / accumulation.

11. Why setdefault() is dangerous?

👉 Mutable default reused.

12. What happens on hash collision?

👉 Probing + equality check.

13. Since which version dict is ordered?

👉 Python 3.7+ (language guarantee).

14. Why deque is better than list for queues?

👉 O(1) pop from both ends.

15. What is deque(maxlen=k) used for?

👉 Sliding window problems.

16. Python heap is min-heap or max-heap?

👉 Min-heap.

17. How to implement max-heap?

👉 Push negative values.

18. Heap push/pop complexity?

👉 O(log n).

19. Why heap comparison sometimes fails?

👉 Equal priority → object comparison.

20. Fix heap comparison error?

👉 Push (priority, index, item).

21. When to use Counter over dict?

👉 Frequency counting.

22. Is Counter ordered?

👉 No guarantee (though insertion-like behavior exists).

23. Difference between pop() and pop(0) in list?

👉 O(1) vs O(n).

24. Why slicing a list is O(n)?

👉 Creates a new list.

25. Most used DS internally by Python?

👉 dict.

🏁 YOUR CURRENT LEVEL

If you:

• Can solve sliding window max
• Can explain heap median
• Can answer 15+ drill questions instantly

👉 You are strong Python interview-ready.

🚀 NEXT STEP (CHOOSE ONE)

Reply with ONE number:

1 → Module 4: Functions, *args, **kwargs, closures, decorators (deep, interview-heavy)
2 → OOP mastery (classes, inheritance, MRO, dunder methods)
3 → Full Python mock interview (60 mins, real format)

This is now elite-track Python prep 🔥