Perfect 👍
Given how deeply you approached Strings and Lists, a Deep Python Dictionary (dict) tutorial will take your Python fundamentals to an interview + real-world production level.

Below is a complete “Deep Python Dictionaries in 3 Days” roadmap, with:

• 🔬 Very deep internal concepts
• 🧠 Patterns & mental models
• 💻 All important problems (basics → advanced)
• 📌 Interview-oriented insights
• 🧾 A compact cheat sheet at the end

🧠 Deep Python Dictionaries in 3 Days

From CPython internals → algorithmic mastery

📅 OVERVIEW

🩵 DAY 1 – Dictionary Internals (VERY DEEP)

1️⃣ What is a Dictionary REALLY?

A Python dictionary is:

• A hash table
• Mapping: key → value
• Average-case O(1) lookup, insert, delete

Internally (CPython):

PyDictObject
 ├── hash table (array of indices)
 ├── entries array (key, value, hash)
 └── resizing logic

2️⃣ Hashing – The Core Concept

🔹 What happens when you do:

d[key] = value

Steps:

1. hash(key) is computed
2. Hash is mapped to an index using modulo
3. Collision handled using open addressing
4. Key comparison using == (not hash)

🔹 Immutable Keys Rule (VERY IMPORTANT)

Valid keys:

• int, float, str, tuple (of immutables)
• frozenset

Invalid keys:

• list, dict, set

Why?

hash(key) MUST NOT change

🔹 Example

d = {}
d[(1, 2)] = "ok"   # valid
d[[1, 2]] = "no"   # ❌ TypeError

3️⃣ Dictionary Mutability & References

d1 = {"a": 1}
d2 = d1
d2["b"] = 2
print(d1)  # {'a': 1, 'b': 2}

Same object → same memory.

Copy safely:

d2 = d1.copy()          # shallow copy
d3 = dict(d1)

4️⃣ Shallow vs Deep Copy (CRITICAL)

import copy

d = {"a": [1, 2]}
shallow = d.copy()
deep = copy.deepcopy(d)

shallow["a"].append(3)
print(d)   # {'a': [1, 2, 3]}

Why?

• Shallow copy copies references
• Deep copy copies recursively

5️⃣ Dictionary Resizing (Hidden Performance Detail)

• Dict grows when ~66% full
• Resizing is expensive → avoid frequent inserts in tight loops

Optimization tip:
If size known beforehand → build dict once, avoid repeated resizing.

6️⃣ Dictionary Views (Dynamic!)

d = {"a": 1, "b": 2}
keys = d.keys()
d["c"] = 3
print(keys)  # reflects change

Views are live, not snapshots.

🧠 DAY 1 – Core Dictionary Operations

Creation Patterns

d = {}
d = dict()
d = {"a": 1}
d = dict(a=1, b=2)

Safe Access

d.get("x", 0)

Update Patterns

d["a"] = 10
d.update({"b": 20})

Delete

del d["a"]
d.pop("b", None)
d.clear()

🧩 DAY 1 – Practice Problems (Foundational)

1️⃣ Count Occurrences of Each Element

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

2️⃣ Most Frequent Element

def most_frequent(lst):
    freq = {}
    for x in lst:
        freq[x] = freq.get(x, 0) + 1
    return max(freq, key=freq.get)

3️⃣ Check If Two Dicts Are Equal

def dict_equal(d1, d2):
    return d1 == d2

4️⃣ Invert a Dictionary

def invert_dict(d):
    return {v: k for k, v in d.items()}

5️⃣ Merge Two Dictionaries

def merge_dicts(d1, d2):
    return {**d1, **d2}

💎 DAY 2 – Dictionary Algorithm Patterns

1️⃣ Frequency Map Pattern (MOST IMPORTANT)

Used in:

• Anagram
• Majority element
• Mode
• Histogram problems

Valid Anagram

def is_anagram(s, t):
    if len(s) != len(t):
        return False
    freq = {}
    for ch in s:
        freq[ch] = freq.get(ch, 0) + 1
    for ch in t:
        if ch not in freq:
            return False
        freq[ch] -= 1
        if freq[ch] < 0:
            return False
    return True

First Non-Repeating Character

def first_unique_char(s):
    freq = {}
    for ch in s:
        freq[ch] = freq.get(ch, 0) + 1
    for i, ch in enumerate(s):
        if freq[ch] == 1:
            return i
    return -1

Group Anagrams

from collections import defaultdict

def group_anagrams(words):
    groups = defaultdict(list)
    for w in words:
        key = tuple(sorted(w))
        groups[key].append(w)
    return list(groups.values())

2️⃣ Hash Map for Index Tracking

Two Sum

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

Longest Subarray with Sum K

def longest_subarray_k(nums, k):
    prefix = 0
    seen = {0: -1}
    best = 0
    for i, n in enumerate(nums):
        prefix += n
        if prefix - k in seen:
            best = max(best, i - seen[prefix - k])
        if prefix not in seen:
            seen[prefix] = i
    return best

3️⃣ Prefix Sum + Dict Pattern

Subarray Sum Equals K

def subarray_sum(nums, k):
    prefix = 0
    freq = {0: 1}
    count = 0
    for n in nums:
        prefix += n
        count += freq.get(prefix - k, 0)
        freq[prefix] = freq.get(prefix, 0) + 1
    return count

🔥 DAY 3 – Advanced Dictionary Concepts

1️⃣ defaultdict vs dict

from collections import defaultdict
d = defaultdict(int)
d["a"] += 1

Avoids:

if key not in d: d[key] = 0

2️⃣ Counter Internals

from collections import Counter
Counter("banana")

Internally still a dict → but optimized in C.

3️⃣ Ordered Dict (Python ≥3.7)

• Dicts preserve insertion order
• No need for OrderedDict anymore (mostly)

4️⃣ Nested Dictionaries

d = {
    "user1": {"age": 25, "city": "NY"},
    "user2": {"age": 30, "city": "LA"}
}

Access safely:

d.get("user1", {}).get("age")

5️⃣ Dictionary as Cache (Memoization)

def fib(n, memo={}):
    if n in memo:
        return memo[n]
    if n <= 1:
        return n
    memo[n] = fib(n-1, memo) + fib(n-2, memo)
    return memo[n]

6️⃣ Dictionary Comprehensions

{k: v*v for k, v in enumerate(range(5))}

Conditional:

{k: v for k, v in d.items() if v > 10}

7️⃣ Sorting Dictionaries

sorted(d.items(), key=lambda x: x[1])

🧾 DICTIONARY CHEAT SHEET

Creation

d = {}
d = dict(a=1)

Access

d[key]
d.get(key, default)

Update

d[key] = value
d.update(other)

Delete

del d[key]
d.pop(key)

Iteration

for k in d:
for v in d.values():
for k, v in d.items():

Patterns

🎯 Interview Golden Rules

1. Dict lookup is O(1) average
2. Keys must be immutable
3. Use get() to avoid KeyError
4. Use defaultdict to simplify logic
5. Avoid modifying dict while iterating
6. Prefer dict over list for membership tests

🏁 What You’ve Achieved

You now understand:

• Dict internals & hashing
• Mutability & copying
• Frequency, prefix sum & grouping patterns
• Nested dicts & caching
• Interview-grade dict problems

If you want, next we can do:

1. Deep Sets in 2 Days
2. Deep Tuples & NamedTuple
3. Python Memory Model (Objects, GC, Ref Counting)
4. Data Structures Interview Pack (Lists + Dicts + Sets combined)

Just tell me what to continue 🚀