The Collections Module in Python is different than the other built-in modules. This module contains some different types of containers which is used to store different types of objects.
The Python collection module is defined as a container that is used to store collections of data, for example – list, dict, set, and tuple, etc. It was introduced to improve the functionalities of the built-in collection containers. Python collection module was first introduced in its 2.4 release.
Some common and widely used collection modules are as follows:
- Counters
- Default Dictionary
- ChainMap
- NamedTuple
- Deque
- UserDict
- UserList
- UserString
Counters
The Counters is a part of the Dictionary and is used to count of number of elements in pairs. The dictionary is an unordered, mutable, and indexed collection where each key is unique and maps to the associated value, but since Python 3.7, the insertion in the dictionary is ordered.
We have to import the ordereddict module by using the following command:
from collections import OrderedDict
Python Counter Syntax
It has the following syntax:
class collections.OrderDict() Python Counter Example
Let us take an example to demonstrate the Counter collection module in Python.
Example
#importing the counter from the collections
from collections import OrderedDict
print("This is a Dictionary:\n")
d = {}
d['w'] = 11
d['x'] = 12
d['y'] = 13
d['z'] = 14
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 Dictionary:
w 11
x 12
y 13
z 14
This is an Ordered Dict:
a 1
b 2
c 3
d 4
Example of deleting and reinserting a key
Let us take an example to demponstrate how to delete and reinsert a key using Python collection module.
Example
#importing the counter from the collections
from collections import OrderedDict
odict = OrderedDict()
odict['w'] = 100
odict['x'] = 200
odict['y'] = 300
odict['z'] = 400
print('Before Deleting')
for key, value in odict.items():
print(key, value)
# deleting the element
odict.pop('w')
# Re-inserting the same element
odict['w'] = 100
print('\nAfter re-inserting')
for key, value in odict.items():
print(key, value)Output:
Before Deleting
w 100
x 200
y 300
z 400
After re-inserting
x 200
y 300
z 400
w 100
DefaultDict
The DefaultDict consists of default values assigned to a key that does not exist, and because of this, it does not raise any error.
Python DefaultDictSyntax
It has the following syntax:
class collections.defaultdict(default_factory) We can initialize the default dictionary by using the DefaultDict() function by passing data types as arguments.
Python DefaultDict Example
Let us take an example to demonstrate the DefaultDict collection module in Python.
Example
#importing the counter from the collections
from collections import defaultdict
# here we are creating a default dictionary with the default value of 0 (int)
dict = defaultdict(int)
A = [1,4,2,3,1,2,5,6,2]
# Counting occurrences of each element in the list
for i in A:
dict[i] += 1
print(dict)Output:
defaultdict(<class 'int'>, {1: 2, 4: 1, 2: 3, 3: 1, 5: 1, 6: 1})ChainMap
The ChainMap collects and accumulates multiple dictionaries into a single unit and then generates a list of dictionaries.
We can import the ChainMap by the following code:
from collections import ChainMap Python ChainMap Syntax
It has the following syntax:
class collections.ChainMap(dict1, dict2) Python ChainMap Example
Let us take an example to illustrate the ChainMap collection module in Python.
Example
#importing the counter from the collections
from collections import ChainMap
dict1 = {'a': 100, 'b': 200}
dict2 = {'c': 300, 'd': 400}
dict3 = {'e': 500, 'f': 600}
#merging the multiple dictionaries
c = ChainMap(dict1, dict2, dict3)
# printing the output
print(c)Output:
ChainMap({'a': 100, 'b': 200}, {'c': 300, 'd': 400}, {'e': 500, 'f': 600})We can also use the key name to access the value from ChainMap.keys() and values() methods can also be used to access.
Example
# Importing namedtuple from the collections module
from collections import ChainMap
dict1 = {'a': 100, 'b': 200}
dict2 = {'c': 300, 'd': 400}
dict3 = {'e': 500, 'f': 600}
# here we are creating the chainmap
cm = ChainMap(dict1, dict2 , dict3)
# Accessing Values using key name
print(cm['a'])
# Accessing values using values() method
print(cm.values())
# Accessing keys using keys() method
print(cm.keys())Output:
100
ValuesView(ChainMap({'a': 100, 'b': 200}, {'c': 300, 'd': 400}, {'e': 500, 'f': 600}))
KeysView(ChainMap({'a': 100, 'b': 200}, {'c': 300, 'd': 400}, {'e': 500, 'f': 600}))
Adding a new dictionary
We can use the new_child() method to add a new dictionary at the beginning of the ChainMap.
Example
#importing the collections module
import collections
# creating the dictionaries
dict1 = { 'a' : 123, 'b' : 456 }
dict2 = { 'b' : 789, 'c' : 111 }
dict3 = { 'f' : 1213 }
# initializing ChainMap
chain = collections.ChainMap(dict1, dict2)
# printing chainMap
print ("The contents of the ChainMap are: ")
print (chain)
# here we are using the new_child() to add new dictionary
n_chain = chain.new_child(dict3)
# printing chainMap
print ("Showing new ChainMap : ")
print (n_chain)Output:
The contents of the ChainMap are:
ChainMap({'a': 123, 'b': 456}, {'b': 789, 'c': 111})
Showing new ChainMap :
ChainMap({'f': 1213}, {'a': 123, 'b': 456}, {'b': 789, 'c': 111})
NamedTuple
A tuple is an ordered, immutable collection of elements, which means that once created, its elements cannot be changed. A NamedTuple is similar to a tuple, the only difference being that it consists of named fields, which makes the data easier to read, interpret, and access.
We can import the NamedTuple with the following code:
from collections import namedtuple
Python NamedTuple Syntax
It has the following syntax:
class collections.namedtuple(typename, field_names) Python NamedTuple Example
Let us take an example to demonstrate the NamedTuple collection module in Python.
Example
# Importing namedtuple from the collections module
from collections import namedtuple
# Creating namedtuple()
Employee = namedtuple('Employee', ['name', 'age', 'ID_number'])
# Creating an instance of an Employee
E = Employee('Abhay', '23', '280202')
# Access using index
print("The Employee age using index is: ", end="")
print(E[1])
# Access using field name
print("The Employee ID number using field name is: ", end="")
print(E.ID_number)
print("The Employee name is: ", end="")
print(E.name)Output:
The Employee age using index is: 23
The Employee ID number using field name is: 280202
The Employee name is: Abhay
Deque
Deque stands for Doubly Ended Queue. It is a data structure in the collections which is used to perform append and pop operations on both ends of the data structure.
Deque word is often pronounced as the word “Deck”. The time complexity for the pop and append operations is O(1), whereas for the list it is O(n).
Python Deque Syntax:
The Syntax for the deque data structure is:
class collections.deque(list) Python Deque Example
Let us take an example to demonstrate the deque collection module in Python.
Example
#importing the deque data structure from collection
from collections import deque
# Declaring deque
q1 = deque(['name','company','empid'])
print(q1)Output:
deque(['name', 'company', 'empid'])Inserting Elements
Using the deque data structure, we add and insert the elements in the deque from both ends. We use the append() method to insert the element from the right and the appendleft() method to insert from the left.
Python Example for Inserting Elements
Let us take an example to demonstrate how to insert element in a deque using append() function.
Example
#importing the deque data structure from collection
from collections import deque
# Initializing deque with initial values
de = deque([123, 456, 789])
# here we appending a number 2025 to the right end of deque
de.append(2025)
#printing the deque after the number to the right end
print("The deque after appending at right is :")
print(de)
#now we are appending the number to the left end of the deque
de.appendleft(100)
#printing the deque after the number to the left
print("The deque after appending at left is :")
print(de)Output:
The deque after appending at right is :
deque([123, 456, 789, 2025])
The deque after appending at left is :
deque([100, 123, 456, 789, 2025])
Removing Elements
We added the elements on the left and right sides using the append() method. Similarly, we can also remove the elements from the deque using the pop() method. We use the pop() method to remove the element from the deque from the right end and popleft() to remove from the left end.
Python Example to Remove Elements using Deque
Let us take an example to demonstrate how to remove elements from a deque using the pop function in Python.
Example
#importing the deque data structure from collection
from collections import deque
# Initialize deque with initial values
de = deque([2016, 2017, 2018, 2019, 2020])
# here we deleting a number from the right end of deque
de.pop()
#printing the deque after deleting the number from the right end
print("The deque after deleting from right is :")
print(de)
#now we are deleting the number from the left end of the deque
de.popleft()
#printing the deque after deleting the number from the left
print("The deque after deleting from left is :")
print(de)Output:
The deque after deleting from right is :
deque([2016, 2017, 2018, 2019])
The deque after deleting from left is :
deque([2017, 2018, 2019])
UserDict
UserDict behaves as a container that is wrapped around the dictionary objects. It is used for creating a dictionary with extra features, functions, etc.
Python UserDict Syntax:
It has the following syntax:
class collections.UserDict([initialdata]) Python UserDict Example
Let us take an example to demonstrate the UserDict collection module in Python.
Example
#importing the UserDict from collection
from collections import UserDict
# we are creating a dictionary where deletion is prohibited
class Dict(UserDict):
# It stops using 'del' on dictionary
def __del__(self):
raise RuntimeError("Deletion not allowed")
# It prevents using pop() on dictionary
def pop(self, s=None):
raise RuntimeError("Deletion not allowed")
# It prevents using popitem() on dictionary
def popitem(self, s=None):
raise RuntimeError("Deletion not allowed")
# Create an instance of MyDict
d = Dict({'a': 100, 'b': 200, 'c': 300})
d.pop(1)Output:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[13], line 21
19 # Create an instance of MyDict
20 d = Dict({'a': 1, 'b': 2, 'c': 3})
---> 21 d.pop(1)
Cell In[13], line 13
12 def pop(self, s=None):
---> 13 raise RuntimeError("Deletion not allowed")
RuntimeError: Deletion not allowed
UserList
We studied that UserDict behaves a wrapper for dictionary objects, similarly, the UserList acts as a container that is wrapped around the list objects. It is used for creating a string with extra features, functions, etc.
Python UserList Syntax:
It has the following syntax:
class collections.UserList([list]) Python UserList Example
Let us take an example to demonstrate the UserList collection module in Python.
Example
#importing the UserList from collection
from collections import UserList
# we are creating a list and deletion of the object is prohibited
class List(UserList):
# Prevents using remove() on list
def remove(self, s=None):
raise RuntimeError("Deletion not allowed")
# Prevents using pop() on list
def pop(self, s=None):
raise RuntimeError("Deletion not allowed")
# We are Creating an instance of List
L = List([100, 200, 300, 400])
print("Original List")
#here we are Appending 500 to the list
L.append(500)
print("After Insertion")
print(L)
# Attempt to remove an item (will raise error)
L.remove()Output:
Original List
After Insertion
[100, 200, 300, 400, 500]
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[6], line 24
22 print(L)
23 # Attempt to remove an item (will raise error)
---> 24 L.remove()
Cell In[6], line 9
8 def remove(self, s=None):
----> 9 raise RuntimeError("Deletion not allowed")
RuntimeError: Deletion not allowed
UserString
Just UserList behaves as a container that is wrapped around the list objects. The UserString acts as a container that is wrapped around the string objects. It is used for creating a string with extra features, functions, etc.
Python UserString Syntax
It has the following syntax:
class collections.UserString(sequence) Python UserString Example
Let us take an example to demonstrate the UserString collection module in Python.
Example
# importing the UserString from collection
from collections import UserString
# Creating a Mutable String
class string(UserString):
# defining a function to append to string
def append(self, s):
self.data += s
# defining a function to remove from string
def remove(self, s):
self.data = self.data.replace(s, "")
# inputting the string
str_obj = string("Learn Python App")
print("Original String:", str_obj.data)
# appending a character or word to the string
str_obj.append("!")
print("String After Appending:", str_obj.data)
# deleting a specific letter from the string
# (In this case, removing 'p' from 'App')
str_obj.remove("p")
print("String after Removing:", str_obj.data)Output:
Original String: Learn Python App
String After Appending: Learn Python App!
String after Removing: Learn Python A!Conclusion
In this tutorial, we learnt about Python Collections. The Collections Module in Python is different than the other built-in modules. This module contains some different types of containers which is used to store different types of objects. We learnt about various collection modules like Counters, Default Dictionary, ChainMap, NamedTuple, Deque, UserDict, UserList, and UserString
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