LEARN COMPLETE PYTHON IN 24 HOURS

3. Advanced Data Structures & Collections

Python’s standard library provides specialized data structures that go beyond basic list, dict, set, and tuple. These save time and improve performance.

3.1 collections module: namedtuple, deque, Counter, defaultdict, OrderedDict

The collections module offers high-performance alternatives.

1. namedtuple – Tuple with named fields (readable like objects, lightweight)

Python

from collections import namedtuple # Define Point = namedtuple('Point', ['x', 'y']) p1 = Point(10, 20) p2 = Point(x=5, y=15) # keyword args also work print(p1.x, p1.y) # 10 20 print(p1) # Point(x=10, y=20) print(p1._asdict()) # {'x': 10, 'y': 20}

Benefits: More readable than plain tuples, immutable, no memory overhead like classes.

2. deque – Double-ended queue (fast append/pop from both ends)

Python

from collections import deque dq = deque([1, 2, 3]) dq.append(4) # right dq.appendleft(0) # left print(dq) # deque([0, 1, 2, 3, 4]) print(dq.pop()) # 4 (from right) print(dq.popleft()) # 0 (from left) dq.extend([5, 6]) # right extend dq.extendleft([ -1, -2]) # left extend (reverses order!) print(dq) # deque([-2, -1, 1, 2, 3, 5, 6])

Use cases: Queues, stacks, sliding windows, BFS, undo/redo.

3. Counter – Counts hashable objects (like frequency map)

Python

from collections import Counter words = ["apple", "banana", "apple", "cherry", "banana", "apple"] cnt = Counter(words) print(cnt) # Counter({'apple': 3, 'banana': 2, 'cherry': 1}) print(cnt['apple']) # 3 print(cnt.most_common(2)) # [('apple', 3), ('banana', 2)] # Arithmetic operations c1 = Counter(a=3, b=1) c2 = Counter(a=1, b=2) print(c1 + c2) # Counter({'a': 4, 'b': 3}) print(c1 - c2) # Counter({'a': 2})

Use cases: Word frequency, voting systems, finding duplicates/most common items.

4. defaultdict – Dictionary that provides default value for missing keys

Python

from collections import defaultdict # Normal dict → KeyError on missing key d = {} # d['key'] += 1 # Error # defaultdict dd = defaultdict(int) # default = 0 dd['a'] += 1 dd['b'] += 5 print(dd) # defaultdict(<class 'int'>, {'a': 1, 'b': 5}) # Other defaults dd_list = defaultdict(list) dd_list['fruits'].append("apple") dd_list['fruits'].append("banana") print(dd_list['fruits']) # ['apple', 'banana']

Use cases: Grouping, counting without checking if key in dict.

5. OrderedDict – Dictionary that remembers insertion order (Note: Since Python 3.7, regular dict also preserves order — OrderedDict is mostly for explicit clarity or older code.)

Python

from collections import OrderedDict od = OrderedDict() od['a'] = 1 od['b'] = 2 od['c'] = 3 print(od) # OrderedDict([('a', 1), ('b', 2), ('c', 3)])

When to use OrderedDict today: When you need .popitem(last=False) (FIFO behavior) or want to clearly signal order matters.

3.2 dataclasses (Python 3.7+) – Cleaner Classes

dataclasses reduce boilerplate when creating classes mainly for storing data.

Python

from dataclasses import dataclass, field @dataclass class Person: name: str age: int = 0 city: str = "Unknown" hobbies: list[str] = field(default_factory=list) # mutable default safe def introduce(self): return f"Hi, I'm {self.name} from {self.city}, {self.age} years old." p = Person("Anshuman", 25, "Muzaffarpur") print(p) # Person(name='Anshuman', age=25, city='Muzaffarpur', hobbies=[]) p.hobbies.append("coding") print(p.hobbies) # ['coding'] p2 = Person("Rahul") # age=0, city="Unknown" print(p2) # Person(name='Rahul', age=0, city='Unknown', hobbies=[])

Advantages over regular class:

• Auto init, repr, eq, ne

• No need to write init manually

• Safe mutable defaults with field(default_factory=...)

• Type hints are enforced by tools like mypy

Options: @dataclass(frozen=True) → immutable, @dataclass(order=True) → adds comparison methods.

3.3 Heapq – Priority Queues

heapq provides min-heap (smallest element first) — very efficient for priority queues.

Python

import heapq # List as heap (in-place) tasks = [] # Push (priority, task) heapq.heappush(tasks, (3, "Write report")) heapq.heappush(tasks, (1, "Fix bug")) heapq.heappush(tasks, (2, "Review code")) print(heapq.heappop(tasks)) # (1, 'Fix bug') ← smallest priority first # Peek without pop print(tasks[0]) # (2, 'Review code')

Real example – Task scheduler

Python

import heapq from datetime import datetime queue = [] heapq.heappush(queue, (datetime(2026, 3, 10), "Submit project")) heapq.heappush(queue, (datetime(2026, 3, 6), "Exam revision")) while queue: deadline, task = heapq.heappop(queue) print(f"{deadline.date()}: {task}")

Tip: Use tuples (priority, item) — heap compares first element, then second if tie.

3.4 Bisect – Binary Search & Insertion

bisect maintains sorted lists efficiently (O(log n) search/insert).

Python

import bisect sorted_list = [10, 20, 30, 40, 50] # Find insertion point pos = bisect.bisect_left(sorted_list, 25) print(pos) # 2 # Insert while keeping sorted bisect.insort(sorted_list, 25) print(sorted_list) # [10, 20, 25, 30, 40, 50] # Right insertion (for duplicates) bisect.insort_right(sorted_list, 25) print(sorted_list) # [10, 20, 25, 25, 30, 40, 50]

Use cases: Maintaining sorted data, finding rank/position, interval problems.

Mini Project – Leaderboard with heapq & bisect

Python

import heapq # Min-heap for top 5 scores (negative for max-heap effect) leaderboard = [] def add_score(name, score): heapq.heappush(leaderboard, (-score, name)) # negative → largest first if len(leaderboard) > 5: heapq.heappop(leaderboard) # remove lowest add_score("Anshuman", 950) add_score("Rahul", 880) add_score("Priya", 990) # Show top scores print("Top 5:") for score, name in sorted(leaderboard): # sort for display print(f"{name}: {-score}")

This completes the full Advanced Data Structures & Collections section — now you can write more efficient, Pythonic code!

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