`collections`

The Python collections module provides specialized container data types that extend the capabilities of Python’s built-in containers, such as lists, tuples, sets, and dictionaries.

This module introduces additional data structures that are particularly useful for handling complex data scenarios.

Here’s a quick example:

Python
      
>>> from collections import Counter

>>> Counter("mississippi")
Counter({'i': 4, 's': 4, 'p': 2, 'm': 1})

Key Features

Provides high-performance, specialized container data types
Includes namedtuple for creating tuple subclasses with named fields
Implements deque for fast, memory-efficient appends and pops from both ends of a sequence
Offers Counter for counting hashable objects
Includes OrderedDict for maintaining the order of keys
Provides defaultdict for dictionaries with default values
Contains ChainMap for linking multiple mappings

Frequently Used Classes and Functions

Object	Type	Description
`collections.namedtuple`	Function	A factory function for creating tuple subclasses with named fields
`collections.deque`	Class	A list-like container with fast appends and pops on either end
`collections.Counter`	Class	A dictionary subclass for counting hashable objects
`collections.OrderedDict`	Class	A dictionary subclass that remembers the order entries were added
`collections.defaultdict`	Class	A dictionary subclass that calls a factory function to supply missing values
`collections.ChainMap`	Class	A class for linking multiple mappings into a single unit

Examples

Creating a named tuple:

Python
      
>>> from collections import namedtuple

>>> Point = namedtuple("Point", ["x", "y"])
>>> point = Point(11, y=22)
>>> point.x, point.y
(11, 22)

Using deque for efficient appends and pops:

Python
      
        
      
    
>>> from collections import deque

>>> numbers = deque([1, 2, 3])
>>> numbers.append(4)
>>> numbers.popleft()
1
>>> list(numbers)
[2, 3, 4]

Common Use Cases

Creating tuple-like classes with namedtuple for better code readability
Using deque for implementing queues and stacks efficiently
Counting elements in an iterable with Counter
Maintaining insertion order with OrderedDict
Providing default values for dictionary keys with defaultdict
Combining multiple dictionaries with ChainMap

Real-World Example

Suppose you need to count the occurrence of words in a text and maintain them in order by frequency. You can achieve this using Counter and OrderedDict:

Python
      
        
      
    
>>> from collections import Counter, OrderedDict

>>> text = "lorem ipsum dolor sit amet ipsum lorem"
>>> word_counts = Counter(text.split())
>>> ordered_word_counts = OrderedDict(word_counts.most_common())
>>> ordered_word_counts
OrderedDict(
    [
        ('lorem', 2),
        ('ipsum', 2),
        ('dolor', 1),
        ('sit', 1),
        ('amet', 1)
    ]
)