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
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:
Python3
# 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:
Python3
# A Python program to demonstrate working# of OrderedDict from collections import OrderedDict print("This is a Dict:\n") d = {} d['a'] = 1d['b'] = 2d['c'] = 3d['d'] = 4 for key, value in d.items(): print(key, value) print("\nThis is an Ordered Dict:\n") od = OrderedDict() od['a'] = 1od['b'] = 2od['c'] = 3od['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:
Python3
# A Python program to demonstrate working# of OrderedDict from collections import OrderedDict od = OrderedDict() od['a'] = 1od['b'] = 2od['c'] = 3od['d'] = 4 print('Before Deleting')for key, value in od.items(): print(key, value) # deleting elementod.pop('a') # Re-inserting the sameod['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:
Python3
# 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:
Python3
# 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:
Python3
# 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:
Python3
# 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 nameprint(c['a']) # Accessing values using values()# methodprint(c.values()) # Accessing keys using keys()# methodprint(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:
Python3
# 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 chainMapprint ("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:
Python3
# 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 OrderedDict() as constructed from the mapped values of namedtuple().
Example:
Python3
# 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:
Python3
# Python code to demonstrate deque from collections import deque # Declaring dequequeue = 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:
Python3
# 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 left 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:
Python3
# 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:
Python3
# 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:
Python3
# 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:
Python3
# 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
Improve your Coding Skills with Practice
Thank you for your valuable feedback!
Please Login to comment…