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Python Collections Module

Last Updated : 26 Jul, 2024
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The collection Module in Python provides different types of containers. A Container is an object that is used to store different objects and provide a way to access the contained objects and iterate over them. Some of the built-in containers are Tuple, List, Dictionary, etc. In this article, we will discuss the different containers provided by the collections module.

Table of Content:

Counters

A counter is a sub-class of the dictionary. It is used to keep the count of the elements in an iterable in the form of an unordered dictionary where the key represents the element in the iterable and value represents the count of that element in the iterable.

Note: It is equivalent to bag or multiset of other languages.

Syntax:

class collections.Counter([iterable-or-mapping])

Initializing Counter Objects

The counter object can be initialized using the counter() function and this function can be called in one of the following ways:

  • With a sequence of items
  • With a dictionary containing keys and counts
  • With keyword arguments mapping string names to counts

Example:

Python 
# A Python program to show different 
# ways to create Counter 
from collections import Counter 
  
# With sequence of items  
print(Counter(['B','B','A','B','C','A','B',
               'B','A','C']))
  
# with dictionary 
print(Counter({'A':3, 'B':5, 'C':2}))
  
# with keyword arguments 
print(Counter(A=3, B=5, C=2))

Output:

Counter({'B': 5, 'A': 3, 'C': 2})
Counter({'B': 5, 'A': 3, 'C': 2})
Counter({'B': 5, 'A': 3, 'C': 2})

Note: For more information, refer  Counters in Python.

OrderedDict

An OrderedDict is also a sub-class of dictionary but unlike dictionary, it remembers the order in which the keys were inserted. 

Syntax:

class collections.OrderDict()

Example:

Python 
# A Python program to demonstrate working
# of OrderedDict 

from collections import OrderedDict 
  
print("This is a Dict:\n") 
d = {} 
d['a'] = 1
d['b'] = 2
d['c'] = 3
d['d'] = 4
  
for key, value in d.items(): 
    print(key, value) 
  
print("\nThis is an Ordered Dict:\n") 
od = OrderedDict() 
od['a'] = 1
od['b'] = 2
od['c'] = 3
od['d'] = 4
  
for key, value in od.items(): 
    print(key, value)

Output:

This is a Dict:

a 1
b 2
c 3
d 4

This is an Ordered Dict:

a 1
b 2
c 3
d 4

While deleting and re-inserting the same key will push the key to the last to maintain the order of insertion of the key.

Example:

Python 
# A Python program to demonstrate working
# of OrderedDict 

from collections import OrderedDict 


od = OrderedDict() 
od['a'] = 1
od['b'] = 2
od['c'] = 3
od['d'] = 4
  
print('Before Deleting')
for key, value in od.items(): 
    print(key, value) 
    
# deleting element
od.pop('a')

# Re-inserting the same
od['a'] = 1

print('\nAfter re-inserting')
for key, value in od.items(): 
    print(key, value)

Output:

Before Deleting
a 1
b 2
c 3
d 4

After re-inserting
b 2
c 3
d 4
a 1

Note: for more information, refer OrderedDict in Python

DefaultDict

A DefaultDict is also a sub-class to dictionary. It is used to provide some default values for the key that does not exist and never raises a KeyError.

Syntax:

class collections.defaultdict(default_factory)

default_factory is a function that provides the default value for the dictionary created. If this parameter is absent then the KeyError is raised.

Initializing DefaultDict Objects

DefaultDict objects can be initialized using DefaultDict() method by passing the data type as an argument.

Example:

Python 
# Python program to demonstrate 
# defaultdict 
   
   
from collections import defaultdict 
   
   
# Defining the dict 
d = defaultdict(int) 
   
L = [1, 2, 3, 4, 2, 4, 1, 2] 
   
# Iterate through the list 
# for keeping the count 
for i in L: 
       
    # The default value is 0 
    # so there is no need to  
    # enter the key first 
    d[i] += 1
       
print(d)

Output:

defaultdict(<class 'int'>, {1: 2, 2: 3, 3: 1, 4: 2})

Example 2:

Python 
# Python program to demonstrate 
# defaultdict 
  
  
from collections import defaultdict 
  
  
# Defining a dict 
d = defaultdict(list) 
  
for i in range(5): 
    d[i].append(i) 
      
print("Dictionary with values as list:") 
print(d)

Output:

Dictionary with values as list: 
defaultdict(<class ‘list’>, {0: [0], 1: [1], 2: [2], 3: [3], 4: [4]})

Note: For more information, refer Defaultdict in Python

ChainMap

A ChainMap encapsulates many dictionaries into a single unit and returns a list of dictionaries.

Syntax:

class collections.ChainMap(dict1, dict2)

Example:

Python 
# Python program to demonstrate 
# ChainMap 
   
   
from collections import ChainMap 
   
   
d1 = {'a': 1, 'b': 2}
d2 = {'c': 3, 'd': 4}
d3 = {'e': 5, 'f': 6}

# Defining the chainmap 
c = ChainMap(d1, d2, d3) 
   
print(c)

Output:

ChainMap({'a': 1, 'b': 2}, {'c': 3, 'd': 4}, {'e': 5, 'f': 6})

Accessing Keys and Values from ChainMap

Values from ChainMap can be accessed using the key name. They can also be accessed by using the keys() and values() method.

Example:

Python 
# Python program to demonstrate 
# ChainMap 
   
   
from collections import ChainMap 
   
   
d1 = {'a': 1, 'b': 2}
d2 = {'c': 3, 'd': 4}
d3 = {'e': 5, 'f': 6}

# Defining the chainmap 
c = ChainMap(d1, d2, d3) 
   
# Accessing Values using key name
print(c['a'])

# Accessing values using values()
# method
print(c.values())

# Accessing keys using keys()
# method
print(c.keys())

Output:

1 
ValuesView(ChainMap({‘a’: 1, ‘b’: 2}, {‘c’: 3, ‘d’: 4}, {‘e’: 5, ‘f’: 6})) 
KeysView(ChainMap({‘a’: 1, ‘b’: 2}, {‘c’: 3, ‘d’: 4}, {‘e’: 5, ‘f’: 6}))

Adding new dictionary

A new dictionary can be added by using the new_child() method. The newly added dictionary is added at the beginning of the ChainMap.

Example:

Python 
# Python code to demonstrate ChainMap and 
# new_child() 
  
import collections 
  
# initializing dictionaries 
dic1 = { 'a' : 1, 'b' : 2 } 
dic2 = { 'b' : 3, 'c' : 4 } 
dic3 = { 'f' : 5 } 
  
# initializing ChainMap 
chain = collections.ChainMap(dic1, dic2) 
  
# printing chainMap 
print ("All the ChainMap contents are : ") 
print (chain) 
  
# using new_child() to add new dictionary 
chain1 = chain.new_child(dic3) 
  
# printing chainMap
print ("Displaying new ChainMap : ") 
print (chain1)

Output:

All the ChainMap contents are : 
ChainMap({'a': 1, 'b': 2}, {'b': 3, 'c': 4})
Displaying new ChainMap : 
ChainMap({'f': 5}, {'a': 1, 'b': 2}, {'b': 3, 'c': 4})

Note: For more information, refer ChainMap in Python

NamedTuple

A NamedTuple returns a tuple object with names for each position which the ordinary tuples lack. For example, consider a tuple names student where the first element represents fname, second represents lname and the third element represents the DOB. Suppose for calling fname instead of remembering the index position you can actually call the element by using the fname argument, then it will be really easy for accessing tuples element. This functionality is provided by the NamedTuple.

Syntax:

class collections.namedtuple(typename, field_names)

Example:

Python 
# Python code to demonstrate namedtuple()
  
from collections import namedtuple
  
# Declaring namedtuple() 
Student = namedtuple('Student',['name','age','DOB']) 
  
# Adding values 
S = Student('Nandini','19','2541997') 
  
# Access using index 
print ("The Student age using index is : ",end ="") 
print (S[1]) 
  
# Access using name  
print ("The Student name using keyname is : ",end ="") 
print (S.name)

Output:

The Student age using index is : 19
The Student name using keyname is : Nandini

Conversion Operations 

1. _make(): This function is used to return a namedtuple() from the iterable passed as argument.

2. _asdict(): This function returns the OrdereDict() as constructed from the mapped values of namedtuple().

Example:

Python 
# Python code to demonstrate namedtuple() and 
# _make(), _asdict()
  

from collections import namedtuple
  
# Declaring namedtuple() 
Student = namedtuple('Student',['name','age','DOB']) 
  
# Adding values 
S = Student('Nandini','19','2541997') 
  
# initializing iterable  
li = ['Manjeet', '19', '411997' ] 
  
# initializing dict 
di = { 'name' : "Nikhil", 'age' : 19 , 'DOB' : '1391997' } 
  
# using _make() to return namedtuple() 
print ("The namedtuple instance using iterable is  : ") 
print (Student._make(li)) 
  
# using _asdict() to return an OrderedDict() 
print ("The OrderedDict instance using namedtuple is  : ") 
print (S._asdict())

Output:

The namedtuple instance using iterable is  : 
Student(name='Manjeet', age='19', DOB='411997')
The OrderedDict instance using namedtuple is  : 
OrderedDict([('name', 'Nandini'), ('age', '19'), ('DOB', '2541997')])

Note: For more  information, refer NamedTuple in Python

Deque

Deque (Doubly Ended Queue) is the optimized list for quicker append and pop operations from both sides of the container. It provides O(1) time complexity for append and pop operations as compared to list with O(n) time complexity.

Syntax:

class collections.deque(list)

This function takes the list as an argument.

Example:

Python 
# Python code to demonstrate deque
  

from collections import deque
  
# Declaring deque
queue = deque(['name','age','DOB']) 
  
print(queue)

Output:

deque(['name', 'age', 'DOB'])

Inserting Elements

Elements in deque can be inserted from both ends. To insert the elements from right append() method is used and to insert the elements from the left appendleft() method is used.

Example:

Python 
# Python code to demonstrate working of  
# append(), appendleft()
  

from collections import deque 
  
# initializing deque 
de = deque([1,2,3]) 
  
# using append() to insert element at right end  
# inserts 4 at the end of deque 
de.append(4) 
  
# printing modified deque 
print ("The deque after appending at right is : ") 
print (de) 
  
# using appendleft() to insert element at right end  
# inserts 6 at the beginning of deque 
de.appendleft(6) 
  
# printing modified deque 
print ("The deque after appending at left is : ") 
print (de)

Output:

The deque after appending at right is : 
deque([1, 2, 3, 4])
The deque after appending at left is : 
deque([6, 1, 2, 3, 4])

Removing Elements

Elements can also be removed from the deque from both the ends. To remove elements from right use pop() method and to remove elements from the left use popleft() method.

Example:

Python 
# Python code to demonstrate working of  
# pop(), and popleft() 

from collections import deque

# initializing deque 
de = deque([6, 1, 2, 3, 4])

# using pop() to delete element from right end  
# deletes 4 from the right end of deque 
de.pop() 
  
# printing modified deque 
print ("The deque after deleting from right is : ") 
print (de) 
  
# using popleft() to delete element from left end  
# deletes 6 from the left end of deque 
de.popleft() 
  
# printing modified deque 
print ("The deque after deleting from left is : ") 
print (de)

Output:

The deque after deleting from right is : 
deque([6, 1, 2, 3])
The deque after deleting from left is : 
deque([1, 2, 3])

Note: For more information, refer Deque in Python.

UserDict

UserDict is a dictionary-like container that acts as a wrapper around the dictionary objects. This container is used when someone wants to create their own dictionary with some modified or new functionality. 

Syntax:

class collections.UserDict([initialdata])

Example:

Python 
# Python program to demonstrate 
# userdict 
   
  
from collections import UserDict 
   
  
# Creating a Dictionary where 
# deletion is not allowed 
class MyDict(UserDict): 
      
    # Function to stop deletion 
    # from dictionary 
    def __del__(self): 
        raise RuntimeError("Deletion not allowed") 
          
    # Function to stop pop from  
    # dictionary 
    def pop(self, s = None): 
        raise RuntimeError("Deletion not allowed") 
          
    # Function to stop popitem  
    # from Dictionary 
    def popitem(self, s = None): 
        raise RuntimeError("Deletion not allowed") 
      
# Driver's code 
d = MyDict({'a':1, 
    'b': 2, 
    'c': 3})
  
d.pop(1)

Output:

Traceback (most recent call last):
  File "/home/f8db849e4cf1e58177983b2b6023c1a3.py", line 32, in <module>
    d.pop(1) 
  File "/home/f8db849e4cf1e58177983b2b6023c1a3.py", line 20, in pop
    raise RuntimeError("Deletion not allowed") 
RuntimeError: Deletion not allowed
Exception ignored in: <bound method MyDict.__del__ of {'a': 1, 'b': 2, 'c': 3}>
Traceback (most recent call last):
  File "/home/f8db849e4cf1e58177983b2b6023c1a3.py", line 15, in __del__
RuntimeError: Deletion not allowed

Note: For more information, refer UserDict in Python

UserList

UserList is a list like container that acts as a wrapper around the list objects. This is useful when someone wants to create their own list with some modified or additional functionality.

Syntax:

class collections.UserList([list])

Example:

Python 
# Python program to demonstrate 
# userlist 
   
  
from collections import UserList 
   
  
# Creating a List where 
# deletion is not allowed 
class MyList(UserList): 
      
    # Function to stop deletion 
    # from List 
    def remove(self, s = None): 
        raise RuntimeError("Deletion not allowed") 
          
    # Function to stop pop from  
    # List 
    def pop(self, s = None): 
        raise RuntimeError("Deletion not allowed") 
      
# Driver's code 
L = MyList([1, 2, 3, 4]) 
  
print("Original List") 
  
# Inserting to List" 
L.append(5) 
print("After Insertion") 
print(L) 
  
# Deleting From List 
L.remove()

Output:

Original List
After Insertion
[1, 2, 3, 4, 5]
Traceback (most recent call last):
  File "/home/c90487eefa7474c0566435269f50a52a.py", line 33, in <module>
    L.remove() 
  File "/home/c90487eefa7474c0566435269f50a52a.py", line 15, in remove
    raise RuntimeError("Deletion not allowed") 
RuntimeError: Deletion not allowed

Note: For more information, refer UserList in Python

UserString

UserString is a string like container and just like UserDict and UserList it acts as a wrapper around string objects. It is used when someone wants to create their own strings with some modified or additional functionality. 

Syntax:

class collections.UserString(seq)

Example:

Python 
# Python program to demonstrate 
# userstring 
   
  
from collections import UserString 
   
  
# Creating a Mutable String 
class Mystring(UserString): 
      
    # Function to append to 
    # string 
    def append(self, s): 
        self.data += s 
          
    # Function to remove from  
    # string 
    def remove(self, s): 
        self.data = self.data.replace(s, "") 
      
# Driver's code 
s1 = Mystring("Geeks") 
print("Original String:", s1.data) 
  
# Appending to string 
s1.append("s") 
print("String After Appending:", s1.data) 
  
# Removing from string 
s1.remove("e") 
print("String after Removing:", s1.data)

Output:

Original String: Geeks
String After Appending: Geekss
String after Removing: Gkss

Note: For more information, refer UserString in Python

Python Collections Module – FAQs

How to use the Counter class in the collections module?

The Counter class is a specialized dictionary for counting hashable objects. It is used to count the frequency of elements in an iterable.

Example:

from collections import Counter

# Create a Counter object
c = Counter(['a', 'b', 'c', 'a', 'b', 'a'])

# Output the counts
print(c)  # Counter({'a': 3, 'b': 2, 'c': 1})

# Access count of a specific element
print(c['a'])  # 3

# Find most common elements
print(c.most_common(2))  # [('a', 3), ('b', 2)]

What is namedtuple and its applications in Python?

namedtuple is a factory function in the collections module that creates tuple subclasses with named fields. It allows you to access elements using named attributes instead of numerical indices.

Example:

from collections import namedtuple

# Define a namedtuple class
Person = namedtuple('Person', ['name', 'age'])

# Create an instance of Person
p = Person(name='John', age=30)

# Access elements using attributes
print(p.name)  # John
print(p.age)   # 30

Applications:

  • Use namedtuple for data structures where you want to give names to the elements, making the code more readable.
  • Useful for representing records or simple data objects.

How to use defaultdict in Python Collections?

defaultdict is a subclass of the built-in dict class. It provides a default value for missing keys, which avoids KeyError exceptions when accessing or modifying dictionary elements.

Example:

from collections import defaultdict

# Create a defaultdict with default value of int (0)
d = defaultdict(int)

# Increment counts
d['a'] += 1
d['b'] += 2

print(d)  # defaultdict(<class 'int'>, {'a': 1, 'b': 2})

# Accessing a missing key returns the default value
print(d['c'])  # 0

What are the benefits of using OrderedDict in Python?

OrderedDict is a subclass of the built-in dict that maintains the order of keys based on the order they were first inserted. In Python 3.7 and later, the built-in dict also maintains insertion order, but OrderedDict provides additional features.

Benefits:

  • Order Preservation: Maintains the order of keys as they were added, which is useful for applications requiring ordered data.
  • Reordering: You can move an existing key to either end of the OrderedDict using methods like move_to_end.
  • Equality Comparison: OrderedDict considers the order of keys in equality comparisons, unlike the standard dict.


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