Python collections, heapq, bisect, and array modules: examples and usage

collections module provides supplementary container data types such as namedtuple and deque .

from collections import namedtuple
# Define a coordinate point
Point = namedtuple('Point', ['x', 'y'])
p = Point(11, 22)
print("Point coordinates:", p.x, p.y)

from collections import deque
# Create a double‑ended queue with a maximum length of 3
dq = deque(maxlen=3)
dq.append(1)
dq.append(2)
dq.append(3)
dq.append(4)
print("Deque after appending 4:", dq)
dq.appendleft(5)
print("Deque after appending 5 to the left:", dq)

heapq module implements heap queue algorithms for building min‑heaps or max‑heaps.

import heapq
# Build a min‑heap
heap = []
elements = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
for element in elements:
    heapq.heappush(heap, element)
print("Heap:", heap)

import heapq
# Pop the smallest element
print("Smallest element:", heapq.heappop(heap))

import heapq
# Build a max‑heap using negative values
max_heap = [-element for element in elements]
for element in max_heap:
    heapq.heappush(heap, element)
print("Max Heap:", [-element for element in heap])

bisect module offers functions for binary searching in sorted lists.

import bisect
# Find the position of an element in a sorted list
sorted_list = [1, 2, 4, 4, 8]
index = bisect.bisect_left(sorted_list, 4)
print("Index of 4:", index)

import bisect
# Insert an element while maintaining order
bisect.insort_left(sorted_list, 6)
print("List after inserting 6:", sorted_list)

array module provides compact array types that are more memory‑efficient than Python lists.

import array
# Create an integer array
arr = array.array('i', [1, 2, 3])
print("Array:", arr)

import array
# Modify the array by appending a value
arr.append(4)
print("Array after appending 4:", arr)

import array
# Slice the array
slice_arr = arr[1:3]
print("Sliced array:", slice_arr)