Group Similar Start and End Character Words using Python

In Python, we can group words with similar stat and end characters using methods like dictionaries and loops, utilizing regular expressions, and implementing list comprehensions.The task involves analyzing a collection of words and identifying groups of words that share common starting and ending characters. This can be a useful technique in various natural language processing applications, such as text classification, information retrieval, and spell−checking. In this article, we will explore these methods to group similar start and end character words in Python.

Method 1:Using Dictionaries and loops

This method utilizes a dictionary to group words based on their similar start and end characters. By iterating through the list of words and extracting the start and end characters of each word, we can create a key for the dictionary. The words are then appended to the corresponding list in the dictionary, forming groups based on their start and end characters.

Syntax

list_name.append(element)

Here, the append() function is a list method used to add an element to the end of the list_name. List_name is the list in which the append method is being applied.

Example

In the below example, we define a function group_words that takes a list of words as input. We initialize an empty dictionary called groups to store the groups of words. For each word in the input list, we extract the start character (word[0]) and end character (word[−1]). We then create a tuple key using these characters.

If the key already exists in the dictionary, we append the current word to the corresponding list. Otherwise, we create a new list with the current word as its first element. Finally, we return the resulting dictionary of groups.

def group_words(words):
    groups = {}
    for word in words:
        start_char = word[0]
        end_char = word[-1]
        key = (start_char, end_char)
        if key in groups:
            groups[key].append(word)
        else:
            groups[key] = [word]
    return groups

words = ['apple', 'banana', 'ant', 'cat', 'dog', 'elephant','amazon grape']
result = group_words(words)
print(result)

Output

{('a', 'e'): ['apple', 'amazon grape'], ('b', 'a'): ['banana'], ('a', 't'): ['ant'], ('c', 't'): ['cat'], ('d', 'g'): ['dog'], ('e', 't'): ['elephant']}

Method 2:Using Regular Expressions

In this method, regular expressions are used to match patterns within each word. By defining a specific pattern to capture the start and end characters of a word, we can extract those characters and create a key for grouping.

Syntax

import re
result = re.split(pattern, string)

Here, the re.split function from the re module takes two parameters: pattern and string. The pattern is a regular expression that defines the splitting criteria, while the string is the input string to be split. The function returns a list of substrings resulting from the split operation based on the specified pattern.

Example

In the below method, we utilize the re-module to match the start and end characters of each word using regular expressions. We define a function group_words that takes a list of words as input. Inside the loop, we use re.match to match the pattern ^(.)(.*)(.)$ against each word. If a match is found, we extract the start and end characters using match.group(1) and match.group(3) respectively. We then follow a similar process as in Method 1 to group the words based on their start and end characters.

import re

def group_words(words):
    groups = {}
    for word in words:
        match = re.match(r'^(.)(.*)(.)$', word)
        if match:
            start_char = match.group(1)
            end_char = match.group(3)
            key = (start_char, end_char)
            if key in groups:
                groups[key].append(word)
            else:
                groups[key] = [word]
    return groups

words = ['apple', 'banana', 'ant', 'cat', 'dog', 'elephant','amazon grape']
result = group_words(words)
print(result)

Output

{('a', 'e'): ['apple', 'amazon grape'], ('b', 'a'): ['banana'], ('a', 't'): ['ant'], ('c', 't'): ['cat'], ('d', 'g'): ['dog'], ('e', 't'): ['elephant']}

Method 3:Using List Comprehensions

List comprehensions offer a concise and efficient way to group words based on their start and end characters. By utilizing dictionary comprehension and subsequent list comprehension, we can create a dictionary of groups and populate it with the corresponding words.

Example

In the below example, we define a function group_words that takes a list of words as input. Using a single list comprehension, we create initial dictionary groups with all keys set to empty lists. In the next list comprehension, we iterate over each word in the input list. For each word, we access the corresponding list in the dictionary using (word[0], word[−1]) as the key and append the word to it.

Syntax

[expression for item in list if condition]

Here, the syntax consists of square brackets enclosing an expression followed by a for loop that iterates over a list. Additionally, an optional if condition can be added to filter the elements. The expression is evaluated for each item in the list that satisfies the condition, and the results are collected into a new list.

def group_words(words):
    groups = {(word[0], word[-1]): [] for word in words}
    [groups[(word[0], word[-1])].append(word) for word in words]
    return groups

words = ['apple', 'banana', 'ant', 'cat', 'dog', 'elephant','amazon grape']
result = group_words(words)
print(result)

Output

{('a', 'e'): ['apple', 'amazon grape'], ('b', 'a'): ['banana'], ('a', 't'): ['ant'], ('c', 't'): ['cat'], ('d', 'g'): ['dog'], ('e', 't'): ['elephant']}

Conclusion

In this article, we discussed how we can group similar start and end character words using various methods in Python. We grouped the words using three different methods: using dictionaries and loops, using regular expressions, and using list comprehension. By employing these techniques, you can efficiently group words and gain valuable insights from text data, opening up possibilities for various natural language processing applications.