# Time Complexity of Counter in Python

Time Complexity of Counter in Python | In this article, we will learn the time complexity of Counter in Python programming language.

Time Complexity is the total amount of time taken to execute a piece of code. This piece of code can be an algorithm or simply a logic that is efficient. It can be defined as the amount of time taken by an algorithm to run, as a function of the length of the input.

A counter is a container that keeps track of how many times equivalent values are added. It is a part of the collections module and is a subclass of the dictionary. The counter is a subclass of a Python dictionary that is specially created for counting hashable objects. It’s a dictionary that stores objects as keys and counts them as values. Counter items count can be positive, zero, or negative integers.

A Counter class is a special type of object data set provided by the collections module in Python. It is an unordered collection in which elements and their counts are stored as a dictionary. Here is an example of the time complexity of a counter in the Python programming language.

``````import collections
count = collections.Counter('hello')
print(count)

count['s'] += 1
print(count)``````

Output:-

Counter({‘l’: 2, ‘h’: 1, ‘e’: 1, ‘o’: 1})
Counter({‘l’: 2, ‘h’: 1, ‘e’: 1, ‘o’: 1, ‘s’: 1})

In the above example, the time complexity of count = collections.Counter(‘hello’) is O(n), where n is the length of the string, and count[‘s’] += 1 is O(1).

Dictionaries are implemented as something called a hash table or hash map, explicitly to make sure that the lookup time is O(1). The time complexity of a Counter in Python Programming language can be O(n).

If you enjoyed this post, share it with your friends. Do you want to share more information about the topic discussed above or do you find anything incorrect? Let us know in the comments. Thank you!