Introduction to Python Standard Library Modules: collections, heapq, bisect, array, weakref, and pprint

1. collections module

The collections module offers specialized container datatypes such as Counter for counting hashable objects, defaultdict which automatically creates default values for missing keys, and deque for fast double‑ended queue operations.

from collections import Counter
li = ["Dog", "Cat", "Mouse", 42, "Dog", 42, "Cat", "Dog"]
a = Counter(li)
print(a)  # Output: Counter({'Dog': 3, 42: 2, 'Cat': 2, 'Mouse': 1})

from collections import defaultdict
s = [('yellow', 1), ('blue', 2), ('yellow', 3), ('blue', 4), ('red', 1)]
d = defaultdict(list)
for k, v in s:
    d[k].append(v)
print(d)  # Output: defaultdict(list, {'yellow': [1, 3], 'blue': [2, 4], 'red': [1]})

from collections import deque
d = deque("ghi")
d.append('j')
d.appendleft('f')
print(d)  # Output: deque(['f', 'g', 'h', 'i', 'j'])

2. heapq module

The heapq module implements heap queue algorithms, also known as priority queues, with a default min‑heap where each parent node is less than or equal to its children.

import heapq
heap = []
heapq.heappush(heap, (5, 'write code'))
heapq.heappush(heap, (7, 'release product'))
heapq.heappush(heap, (1, 'write spec'))
print(heapq.heappop(heap))  # Output: (1, 'write spec')

3. bisect module

The bisect module provides binary search and insertion utilities for sorted lists, enabling efficient location of insertion points.

import bisect
sorted_list = [1, 2, 4, 5]
position = bisect.bisect(sorted_list, 3)
print(position)  # Output: 2

4. array module

The array module supplies a compact array type that stores only homogeneous numeric values, offering memory efficiency compared to regular Python lists.

import array
arr = array.array('i', [1, 2, 3, 4])
print(arr)  # Output: array('i', [1, 2, 3, 4])

5. weakref module

Weak references allow objects to be referenced without preventing their garbage collection, useful for caches, breaking reference cycles, and monitoring object finalization. Key components include weakref.ref , weakref.proxy , WeakKeyDictionary , WeakValueDictionary , WeakSet , and finalize .

Example of creating a weak reference and checking its value after the original object is deleted:

import weakref
class MyClass:
    pass
obj = MyClass()
r = weakref.ref(obj)
print(r())  # Output: <__main__.MyClass object at ...>
del obj  # Remove strong reference
print(r())  # Output: None

Example of registering a callback to be invoked when the object is about to be collected:

def callback(ref):
    print("Object has been garbage collected.")
obj = MyClass()
r = weakref.ref(obj, callback)
del obj  # Triggers callback and prints the message

Using weak references correctly can improve memory management and application stability.

6. pprint module

The pprint module provides “pretty‑print” capabilities that format complex nested data structures for easier reading.

import pprint
data = {"key": ["value1", "value2"]}
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(data)