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')

count['s'] += 1


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).

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