5 Best Ways to Determine if Two Strings are Close in Python

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💡 Problem Formulation: Given two strings, how can we determine if they are “close” to each other? “Closeness” can be defined in different ways: character composition, sequence, edit distance, etc. For instance, the input strings “listen” and “silent” are close because they contain the same characters. However, “kitten” and “sitting” have a few different characters and a different length.

Method 1: Using Collections to Compare Character Frequency

Determining if two strings are close can start with the comparison of character frequency. The Python collections module provides a Counter class that can be particularly useful in such comparisons. This method checks whether two strings have the same character frequency, thus being ‘close’ in compositional terms.

Here’s an example:

from collections import Counter

def are_close_str1(s1, s2):
    return Counter(s1) == Counter(s2)
    
print(are_close_str1("listen", "silent"))  # Example with anagrams
print(are_close_str1("kitten", "sitting")) # Example with different lengths and characters

The output:

True
False

This code snippet defines a function are_close_str1 that uses the Counter class to compare the frequency of characters in two strings. It returns True if the frequencies are identical (as with anagrams), indicating closeness, and False otherwise.

Method 2: Using Sorted Function for Anagram Detection

Anagrams are a classic example of “close” strings, where the sequence of characters doesn’t matter but the frequency does. The built-in sorted() function can be used to reorder the characters in both strings and then check for equality. Note that this isn’t applicable for strings that are supposed to be close in sequence.

Here’s an example:

def are_close_str2(s1, s2):
    return sorted(s1) == sorted(s2)

print(are_close_str2("listen", "silent"))
print(are_close_str2("kitten", "sitting"))

The output:

True
False

In this snippet, the function are_close_str2 sorts both strings and checks whether their sorted versions are equal. It returns True if the strings are anagrams and False otherwise.

Method 3: Hamming Distance for String Closeness

Hamming distance measures how many substitutions are required to change one string into the other. This metric implies strings are “close” if they differ by a small number of substitutions. This method assumes strings are of equal length and focuses on sequence-level similarity.

Here’s an example:

def hamming_distance(s1, s2):
    if len(s1) != len(s2):
        raise ValueError("Undefined for sequences of unequal length")
    return sum(c1 != c2 for c1, c2 in zip(s1, s2))

print(hamming_distance("karolin", "kathrin"))

The output:

3

The function hamming_distance iterates over two strings in unison, counts and returns the number of positions at which the corresponding characters are different. If the strings are of unequal length, it raises an error.

Method 4: Using the Levenshtein Distance

Levenshtein distance, also known as “edit distance”, measures how many insertions, deletions, or substitutions are required to transform one string into another. This is a measure of closeness that accounts for strings of varying lengths and is sensitive to the sequence.

Here’s an example:

import numpy as np

def levenshtein_distance(s1, s2):
    if len(s1) < len(s2):
        return levenshtein_distance(s2, s1)
    
    if len(s2) == 0:
        return len(s1)

    previous_row = np.arange(len(s2) + 1)
    for i, c1 in enumerate(s1):
        current_row = [i + 1]
        for j, c2 in enumerate(s2):
            insertions = previous_row[j + 1] + 1
            deletions = current_row[j] + 1
            substitutions = previous_row[j] + (c1 != c2)
            current_row.append(min(insertions, deletions, substitutions))
        previous_row = current_row

    return previous_row[-1]

print(levenshtein_distance("kitten", "sitting"))

The output:

3

This code defines a function levenshtein_distance that calculates the number of operations needed to transform one string to another. It uses dynamic programming with the help of a numpy array to find the minimum number of edits.

Bonus One-Liner Method 5: Using FuzzyWuzzy

The FuzzyWuzzy library uses Levenshtein Distance to calculate differences between sequences. This handy one-liner can leverage the power of FuzzyWuzzy to ascertain the similarity ratio between two strings, which is a proxy for closeness.

Here’s an example:

from fuzzywuzzy import fuzz

print(fuzz.ratio("kitten", "sitting"))

The output:

71

This one-liner uses fuzz.ratio from the FuzzyWuzzy library to compare two strings and gives a score out of 100 indicating how similar they are; the higher the score, the closer the strings.

Summary/Discussion

  • Method 1: Using Collections to Compare Character Frequency. Strengths: Good for checking compositional similarity. Weaknesses: Ignores order and not suitable for strings of different lengths.
  • Method 2: Using Sorted Function for Anagram Detection. Strengths: Simple and effective for anagrams. Weaknesses: Like Method 1, it ignores order and is not suitable for strings of different lengths.
  • Method 3: Hamming Distance for String Closeness. Strengths: Measures sequence-level similarity for strings of equal length. Weaknesses: Inapplicable for strings of different lengths.
  • Method 4: Using the Levenshtein Distance. Strengths: Accounts for the number of edits required, handling strings of different lengths and sequences. Weaknesses: Computationally more expensive for longer strings.
  • Method 5: Using FuzzyWuzzy. Strengths: Provides a quick similarity score, is easy to use and understand. Weaknesses: Requires an external library and the score might not be intuitive in terms of exact edit operations.