intermediatePython Framework • Core Concepts

Advanced Data Structures

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Tuples: Data Integrity

Tuples are immutable lists. They guarantee that your data hasn't been tampered with. Use them for fixed collections like coordinates or configuration settings.

Python
# Coordinates should not change
location = (40.7128, -74.0060)
# location[0] = 0  # CRASH! TypeError

lat, lng = location  # Clean unpacking

Counter: Frequency Analysis

Stop writing loops to count things! `Counter` does it instantly and provides powerful methods like `most_common()`. It's a data scientist's best friend.

Python
from collections import Counter

votes = ['red', 'blue', 'red', 'green', 'blue', 'red']
stats = Counter(votes)

print(stats.most_common(1))
# [('red', 3)] - The winner is Red!

NamedTuple: Self-Documenting Code

Replace mysterious indices like `user[0]` with readable names like `user.id`. `NamedTuple` gives you the memory efficiency of a tuple with the readability of a class.

Python
from collections import namedtuple

Color = namedtuple('Color', ['r', 'g', 'b'])
midnight = Color(25, 25, 112)

print(midnight.r, midnight.g, midnight.b)  # 25 25 112

Deque: Fast Queues

Lists are slow at adding/removing from the start. `deque` (Double-Ended Queue) is lightning fast at both ends. Perfect for queues, sliding windows, and undo history.

Python
from collections import deque

history = deque(maxlen=3)
history.append('view_page')
history.append('click_btn')
history.append('login')
history.append('logout')

print(history)
# deque(['click_btn', 'login', 'logout']) - 'view_page' vanished!

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