Data Structures: Python's Built-in Containers Mastery

TL;DR

Python’s built-in data structures - lists, dictionaries, sets, and tuples - provide powerful tools for organizing data with different performance characteristics and use cases.

Interesting!

Python dictionaries are so optimized that they’re used internally by Python itself to store object attributes, making attribute access lightning-fast.

Lists: Ordered and Mutable

# List creation and manipulation
fruits = ['apple', 'banana', 'cherry']
fruits.append('date')

# List comprehensions
squares = [x**2 for x in range(10) if x % 2 == 0]
print(squares)  # [0, 4, 16, 36, 64]

Dictionaries: Key-Value Power

# Dictionary operations
student = {'name': 'Alice', 'grade': 'A', 'age': 20}

# Dictionary comprehensions
scores = {'alice': 95, 'bob': 87, 'charlie': 92}
high_scores = {k: v for k, v in scores.items() if v > 90}

Sets: Unique Collections

colors1 = {'red', 'green', 'blue'}
colors2 = {'blue', 'yellow', 'red'}

print(colors1 & colors2)  # {'red', 'blue'} - intersection 
print(colors1 | colors2)  # union

Choose the right data structure: lists for ordered collections, dictionaries for key-value mapping, sets for uniqueness, and tuples for immutable data.

For more advanced data structure needs, explore Python's collections module which provides specialized containers like Counter, defaultdict, and deque. These structures work seamlessly with control flow patterns and built-in functions for efficient data processing. Dictionary comprehensions are detailed in PEP 274 and conditional expressions from PEP 308 enhance data structure creation.

Reference: Python Tutorial - Data Structures