Python – Bigrams Frequency in String

Sometimes while working with Python Data, we can have problem in which we need to extract bigrams from string. This has application in NLP domains. But sometimes, we need to compute the frequency of unique bigram for data collection. The solution to this problem can be useful. Lets discuss certain ways in which this task can be performed. Method #1 : Using Counter() + generator expression The combination of above functions can be used to solve this problem. In this, we compute the frequency using Counter() and bigram computation using generator expression and string slicing. 

Python3

# Python3 code to demonstrate working of
# Bigrams Frequency in String
# Using Counter() + generator expression
from collections import Counter

# initializing string
test_str = 'geeksforgeeks'

# printing original string
print("The original string is : " + str(test_str))

# Bigrams Frequency in String
# Using Counter() + generator expression
res = Counter(test_str[idx : idx + 2] for idx in range(len(test_str) - 1))

# printing result
print("The Bigrams Frequency is : " + str(dict(res)))

Output : 

The original string is : geeksforgeeks The Bigrams Frequency is : {‘ee’: 2, ‘ks’: 2, ‘ek’: 2, ‘sf’: 1, ‘fo’: 1, ‘ge’: 2, ‘rg’: 1, ‘or’: 1}

Method #2 : Using Counter() + zip() + map() + join The combination of above functions can also be used to solve this problem. In this, we perform the task of constructing bigrams using zip() + map() + join. 

Python3

# Python3 code to demonstrate working of
# Bigrams Frequency in String
# Using Counter() + zip() + map() + join
from collections import Counter

# initializing string
test_str = 'geeksforgeeks'

# printing original string
print("The original string is : " + str(test_str))

# Bigrams Frequency in String
# Using Counter() + zip() + map() + join
res = Counter(map(''.join, zip(test_str, test_str[1:])))

# printing result
print("The Bigrams Frequency is : " + str(dict(res)))

Output : 

The original string is : geeksforgeeks The Bigrams Frequency is : {‘ee’: 2, ‘ks’: 2, ‘ek’: 2, ‘sf’: 1, ‘fo’: 1, ‘ge’: 2, ‘rg’: 1, ‘or’: 1}

The Time and Space Complexity for all the methods are the same:

Time Complexity: O(n)

Auxiliary Space: O(n)