Python | Counter Objects | elements()
Last Updated : 25 Jul, 2024
Counter class is a special type of object data-set provided with the collections module in Python3. Collections module provides the user with specialized container datatypes, thus, providing an alternative to Python's general-purpose built-ins like dictionaries, lists, and tuples.
Counter is a sub-class that is used to count hashable objects. It implicitly creates a hash table of an iterable when invoked.
elements() is one of the functions of Counter class, when invoked on the Counter object will return an itertool of all the known elements in the Counter object.
Parameters : Doesn't take any parameters
Return type : Returns an itertool for all the elements with positive count in the Counter object
Errors and Exceptions :
-> It will print garbage value when directly printed because it returns an itertool, not a specific data-container.
-> If the count of an item is already initialized in Counter object, then it will ignore the ones with zero and negative values.
Code #1: Working of elements() on a simple data container
Python3 # import counter class from collections module from collections import Counter # Creation of a Counter Class object using # string as an iterable data container x = Counter("geeksforgeeks") # printing the elements of counter object for i in x.elements(): print ( i, end = " ") Output:
g g e e e e k k s s f o r
We can also create Counter class object using a list as an iterable data container.
Python # import counter class from collections module from collections import Counter #Creating a Counter class object using list as an iterable data container a = [12, 3, 4, 3, 5, 11, 12, 6, 7] x = Counter(a) #directly printing whole x print(x) #We can also use .keys() and .values() methods to access Counter class object for i in x.keys(): print(i, ":", x[i]) #We can also make a list of keys and values of x x_keys = list(x.keys()) x_values = list(x.values()) print(x_keys) print(x_values)
Output:
Counter({12: 2, 3: 2, 4: 1, 5: 1, 11: 1, 6: 1, 7: 1})
12 : 2
3 : 2
4 : 1
5 : 1
11 : 1
6 : 1
7 : 1
[12, 3, 4, 5, 11, 6, 7]
[2, 2, 1, 1, 1, 1, 1]
Code #2: Elements on a variety of Counter Objects with different data-containers
Python3 # import counter class from collections module from collections import Counter # Creation of a Counter Class object using # a string as an iterable data container # Example - 1 a = Counter("geeksforgeeks") # Elements of counter object for i in a.elements(): print ( i, end = " ") print() # Example - 2 b = Counter({'geeks' : 4, 'for' : 1, 'gfg' : 2, 'python' : 3}) for i in b.elements(): print ( i, end = " ") print() # Example - 3 c = Counter([1, 2, 21, 12, 2, 44, 5, 13, 15, 5, 19, 21, 5]) for i in c.elements(): print ( i, end = " ") print() # Example - 4 d = Counter( a = 2, b = 3, c = 6, d = 1, e = 5) for i in d.elements(): print ( i, end = " ") Output:
g g e e e e k k s s f o r
geeks geeks geeks geeks for gfg gfg python python python
1 2 2 21 21 12 44 5 5 5 13 15 19
a a b b b c c c c c c d e e e e e
Code #3: To demonstrate what elements() return when it is printed directly
Python3 # import Counter from collections from collections import Counter # creating a raw data-set x = Counter ("geeksforgeeks") # will return a itertools chain object # which is basically a pseudo iterable container whose # elements can be used when called with a iterable tool print(x.elements()) Output:
itertools.chain object at 0x037209F0
Code #4: When the count of an item in Counter is initialized with negative values or zero.
Python3 # import Counter from collections from collections import Counter # creating a raw data-set using keyword arguments x = Counter (a = 2, x = 3, b = 3, z = 1, y = 5, c = 0, d = -3) # printing out the elements for i in x.elements(): print( "% s : % s" % (i, x[i]), end ="\n")
Output:
a : 2
a : 2
x : 3
x : 3
x : 3
b : 3
b : 3
b : 3
z : 1
y : 5
y : 5
y : 5
y : 5
y : 5
Note: We can infer from the output that items with values less than 1 are omitted by elements().
Applications:
Counter object along with its functions are used collectively for processing huge amounts of data. We can see that Counter() creates a hash-map for the data container invoked with it which is very useful than by manual processing of elements. It is one of a very high processing and functioning tools and can even function with a wide range of data too.
Similar Reads
Python Collections Module 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
13 min read
Namedtuple in Python Python supports a type of container dictionary called "namedtuple()" present in the module "collections". In this article, we are going to see how to Create a NameTuple and operations on NamedTuple.What is NamedTuple in Python?In Python, NamedTuple is present inside the collections module. It provid
8 min read
Deque in Python A deque stands for Double-Ended Queue. It is a data structure that allows adding and removing elements from both ends efficiently. Unlike regular queues, which are typically operated on using FIFO (First In, First Out) principles, a deque supports both FIFO and LIFO (Last In, First Out) operations.E
6 min read
ChainMap in Python Python contains a container called "ChainMap" which encapsulates many dictionaries into one unit. ChainMap is member of module "collections". 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}
3 min read
Python | Counter Objects | elements() Counter class is a special type of object data-set provided with the collections module in Python3. Collections module provides the user with specialized container datatypes, thus, providing an alternative to Python's general-purpose built-ins like dictionaries, lists, and tuples. Counter is a sub-c
6 min read
OrderedDict in Python OrderedDict is a subclass of Python's built-in dictionary dict that remembers the order in which keys are inserted. Unlike older versions of Python where dictionaries did not guarantee order, OrderedDict preserves insertion order reliably.Note: From Python 3.7 onwards, the built-in dict also preserv
7 min read
Defaultdict in Python In Python, defaultdict is a subclass of the built-in dict class from the collections module. It is used to provide a default value for a nonexistent key in the dictionary, eliminating the need for checking if the key exists before using it.Key Features of defaultdict:When we access a key that doesn'
6 min read
Collections.UserDict in Python An unordered collection of data values that are used to store data values like a map is known as Dictionary in Python. Unlike other Data Types that hold only a single value as an element, Dictionary holds key:value pair. Key-value is provided in the dictionary to make it more optimized. Note: For mo
2 min read
Collections.UserList in Python Python Lists are array-like data structure but unlike it can be homogeneous. A single list may contain DataTypes like Integers, Strings, as well as Objects. List in Python are ordered and have a definite count. The elements in a list are indexed according to a definite sequence and the indexing of a
2 min read
Collections.UserString in Python Strings are the arrays of bytes representing Unicode characters. However, Python does not support the character data type. A character is a string of length one. Example: Python3 # Python program to demonstrate # string # Creating a String # with single Quotes String1 = 'Welcome to the Geeks World'
2 min read