When working with text data, it is often necessary to analyze the relationships between adjacent words or phrases. One common task is to find the bigram of an adjacent sentence in Python. A bigram is simply a pair of consecutive words in a sentence.

Option 1: Using the split() function

One way to find the bigram of an adjacent sentence is by using the split() function in Python. This function splits a string into a list of words based on a specified delimiter, which in this case would be a space.

sentence = "I love Python programming"
words = sentence.split()
bigram = [(words[i], words[i+1]) for i in range(len(words)-1)]
print(bigram)

In this code, we first define the sentence as a string. We then use the split() function to split the sentence into a list of words. Next, we iterate over the list of words using a for loop and create a tuple of each consecutive pair of words. Finally, we print the bigram.

Option 2: Using the zip() function

Another way to find the bigram of an adjacent sentence is by using the zip() function in Python. This function takes two or more iterables and returns an iterator that generates tuples containing elements from each iterable.

sentence = "I love Python programming"
words = sentence.split()
bigram = list(zip(words, words[1:]))
print(bigram)

In this code, we again split the sentence into a list of words. We then use the zip() function to create a list of tuples, where each tuple contains a consecutive pair of words. Finally, we print the bigram.

Option 3: Using the nltk library

If you are working with a large corpus of text data, it may be more efficient to use the nltk library in Python. This library provides various tools and functions for natural language processing tasks, including finding bigrams.

import nltk

sentence = "I love Python programming"
words = nltk.word_tokenize(sentence)
bigram = list(nltk.bigrams(words))
print(bigram)

In this code, we first import the nltk library. We then use the word_tokenize() function to split the sentence into a list of words. Next, we use the bigrams() function from the nltk library to find the bigram of the sentence. Finally, we print the bigram.

After considering these three options, the best approach depends on the specific requirements of your project. If you are working with a simple sentence and want a quick solution, Option 1 or Option 2 would be sufficient. However, if you are working with a large corpus of text data and need more advanced natural language processing capabilities, Option 3 using the nltk library would be the better choice.