Python program to remove words that are common in two Strings

When working with text processing, you may need to remove words that are common between two strings and keep only the unique words. This can be achieved by counting word frequencies and filtering words that appear only once across both strings.

Example

Below is a demonstration of removing common words from two strings ?

def common_words_filter(string_1, string_2):
    word_count = {}

    # Count words from first string
    for word in string_1.split():
        word_count[word] = word_count.get(word, 0) + 1

    # Count words from second string
    for word in string_2.split():
        word_count[word] = word_count.get(word, 0) + 1

    # Return words that appear only once (uncommon words)
    return [word for word in word_count if word_count[word] == 1]

string_1 = "Python is fun"
print("The first string is:")
print(string_1)

string_2 = "Python is fun to learn"
print("The second string is:")
print(string_2)

print("The uncommon words from the two strings are:")
print(common_words_filter(string_1, string_2))

Output

The first string is:
Python is fun
The second string is:
Python is fun to learn
The uncommon words from the two strings are:
['to', 'learn']

Using Set Operations

An alternative approach using set operations for better readability ?

def remove_common_words_set(string_1, string_2):
    words_1 = set(string_1.split())
    words_2 = set(string_2.split())
    
    # Find words unique to each string
    unique_words = words_1.symmetric_difference(words_2)
    return list(unique_words)

string_1 = "Python is fun"
string_2 = "Python is fun to learn"

print("Unique words using set operations:")
print(remove_common_words_set(string_1, string_2))

Unique words using set operations:
['learn', 'to']

How It Works

The first approach uses a dictionary to count word frequencies:

• Word counting: Each word from both strings is counted in a dictionary
• Filtering: Words with count = 1 are unique to one string only
• List comprehension: Creates the final list of uncommon words

The second approach uses set operations:

• Set creation: Converts word lists to sets for faster operations
• Symmetric difference: Finds words present in either set but not both

Conclusion

Use dictionary counting for frequency-based filtering or set operations for simpler unique word extraction. Both methods effectively remove common words between two strings.