Advanced Data Structures

Dictionaries and sets are versatile data structures that provide fast access and retrieval of data, making them essential tools for handling key-value pairs and unique elements.

# Example of dictionaries and sets
# Dictionary: Mapping names to ages
ages = {“Alice”: 25, “Bob”: 30, “Charlie”: 22}
print(ages[“Alice”]) # Output: 25

# Set: Storing unique elements
fruits = {“apple”, “banana”, “orange”, “apple”}
print(fruits) # Output: {“apple”, “banana”, “orange”}

The Collections module offers specialized data structures beyond the built-in data types, such as deque, namedtuple, and defaultdict, adding extra flexibility and functionality to your code.

# Example of deque, namedtuple, and defaultdict
from collections import deque, namedtuple, defaultdict

# Deque: Double-ended queue for fast appends and pops
my_queue = deque([1, 2, 3])
my_queue.append(4)
print(my_queue) # Output: deque([1, 2, 3, 4])

# Namedtuple: Named tuples for readable data structures
Person = namedtuple(“Person”, [“name”, “age”])
person1 = Person(“Alice”, 25)
print(person1.name) # Output: Alice

# Defaultdict: Dictionary with default factory function
student_scores = defaultdict(int)
student_scores[“Alice”] = 95
print(student_scores[“Bob”]) # Output: 0 (default int value)

Heapq is a Python module that provides heap-based priority queues. These data structures are useful when you need to efficiently manage items based on their priority.

# Example of heapq and priority queues
import heapq

# Creating a priority queue
my_heap = [3, 1, 5, 4, 2] heapq.heapify(my_heap)

# Pushing and popping items with priority
heapq.heappush(my_heap, 6)
print(heapq.heappop(my_heap)) # Output: 1 (lowest priority element)