# How To Implement Fuzzy String Matching [4 Ways In Python]

by | Jan 1, 2023 | Data Science, Natural Language Processing

This article discusses one of the most valuable tools when analysing textual data in natural language processing — fuzzy string matching. We first discuss what it is, its typical applications and how it works. We then go through the most common algorithms and problems associated with using text matching. Lastly, we provide sample code with different Python libraries so you can start using the technique immediately.

## What is fuzzy logic?

Fuzzy logic is a mathematical approach to dealing with uncertainty and imprecision in decision-making. It is based on the idea that concepts and statements can have degrees of truth rather than simply true or false.

In fuzzy logic, statements are represented by “fuzzy sets,” which are sets of values that are not necessarily precise or well-defined. Instead, these sets are defined by membership functions, which assign a degree of membership to each value in the set.

For example, consider the concept of “hot.” In traditional Boolean logic, a temperature can be either hot or not hot. In fuzzy logic, the idea of “hot” is represented by a fuzzy set, and the membership function assigns a degree of membership to each temperature based on how hot it is. So, for example, a temperature of 80 degrees Celcius might have a high degree of membership in the “hot” set. Conversely, a temperature of 20 degrees might have a lower degree of membership.

AI-generated image; the boundaries between hot and cold aren’t very clear but rather a sliding scale. A sliding scale is just as important when working with text; therefore, we use fuzzy logic.

Fuzzy logic is used in many applications, including control systems, artificial intelligence, and data analysis. It is handy when precise data is unavailable, or the application must consider multiple conflicting criteria.

## What is fuzzy string matching?

Fuzzy string matching is used to match approximately equal strings rather than precisely equal strings. It is often used when it is hard to tell if two strings are identical because of spelling, grammar, or other differences.

For example, fuzzy matching can match customer names in a database even if the names are spelt slightly differently. Fuzzy matching can also match product names, addresses, or any other text data that may have variations.

Fuzzy matching can be done in many ways, such as with algorithms based on Levenshtein distance, Jaccard similarity, and others. These techniques typically calculate a score representing the similarity between two strings, with higher scores indicating a closer match. The specific method used will depend on the requirements and constraints of the application.

## What are the applications of fuzzy string matching?

There are many applications for fuzzy string matching, as it is a helpful tool for dealing with variations in text data. Some typical applications include:

1. Data deduplication: Fuzzy string matching can identify and merge duplicate records in a database.
2. Data cleansing: Fuzzy string matching can identify and correct text data errors, such as misspellings or incorrect formatting.
3. Searching: Fuzzy string matching can improve the accuracy of search results by matching approximate rather than exact queries.
4. Natural language processing: Fuzzy string matching can improve the accuracy of natural language processing tasks, such as language translation or text classification.
5. Fraud detection: Fuzzy string matching can be used to identify fraudulent activity by matching variations of names or addresses associated with fraudulent activity.
6. Data integration: Fuzzy string matching can be used to integrate data from different sources, even if the data is not perfectly consistent.

These are just a few examples of the many applications of fuzzy string matching. It is a valuable tool for dealing with variations in text data in a wide range of contexts.

## How does fuzzy string matching work?

Fuzzy string matching compares two strings and calculates a score representing their similarity. The particular algorithm chosen to determine the similarity score will depend on the application’s needs and how it works.

For example, the Levenshtein distance algorithm determines how many single-character changes (like adding, removing, or swapping) are needed to change one string into another. The smaller the number of modifications, the closer the match.

The Jaccard similarity algorithm calculates the similarity between two strings based on the number of common characters divided by the total number of unique characters. A higher similarity score indicates a closer match.

Once the similarity score has been calculated, fuzzy matching can determine whether the two strings are close enough to be considered a match. This threshold can be set based on the needs of the application. For example, a higher threshold might be used for more critical applications where a higher level of accuracy is required. Conversely, a lower threshold might be used for less critical applications where a higher error tolerance is acceptable.

Overall, fuzzy string matching aims to identify approximately equal strings, even if they are not precisely the same, to improve the accuracy and reliability of data processing tasks.

## What algorithms are used for fuzzy string matching?

Many algorithms can be used for fuzzy string matching; some standard algorithms for fuzzy matching include:

1. Levenshtein distance: This algorithm calculates the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another. The smaller the number of modifications, the closer the match.
2. Jaccard similarity: This algorithm calculates the similarity between two strings based on the number of common characters divided by the total number of unique characters. A higher similarity score indicates a closer match.
3. Cosine similarity: This algorithm calculates the similarity between two strings based on the angle between their vectors in a vector space. A smaller angle indicates a closer match.
4. Damerau-Levenshtein distance: This algorithm is similar to the Levenshtein distance but allows for transposing two adjacent characters.
5. Hamming distance: This algorithm calculates the number of positions at which two strings differ. A smaller number of differences indicates a closer match.

These are just a few examples of algorithms that can be used for fuzzy matching. The specific algorithm chosen will depend on the requirements and constraints of the application.

## What are common problems with fuzzy string matching?

Several common problems can arise when using fuzzy string matching:

1. False positives: Algorithms can sometimes produce false positives, where two strings are considered a match even though they are different. This can be caused by variations in spelling, grammar, or other factors that need to be taken into account by the algorithm.
2. False negatives: Algorithms can also produce false negatives, where two actually similar strings are not considered a match. This can be caused by differences in formatting, punctuation, or other factors not captured by the algorithm.
3. Tuning: Algorithms often require fine-tuning to achieve the desired level of accuracy. This can involve adjusting the algorithm parameters, setting appropriate thresholds for determining a match, and preprocessing the data to remove noise and variations.
4. Scalability: Algorithms can be computationally intensive, mainly when applied to large datasets. This can be a problem if the algorithm needs to be used in real-time or on a large scale.
5. Ambiguity: Algorithms can sometimes produce ambiguous results, making it difficult to determine whether two strings are a match. This can be caused by variations in the data not captured by the algorithm.

Overall, it is essential to carefully consider the requirements and constraints of the application when using fuzzy string matching and to choose an appropriate algorithm and tuning parameters to achieve the desired level of accuracy.

## What are the popular Python libraries for fuzzy string matching?

Several Python libraries can be used for fuzzy string matching, including:

1. TheFuzz: This library provides several algorithms for fuzzy string matching, including the Levenshtein distance, Jaccard similarity, and others. It also includes functions for string preprocessing, such as removing punctuation and converting strings to lowercase.
2. python-Levenshtein: This library implements the Levenshtein distance algorithm for calculating the similarity between two strings. It also includes functions for calculating the relative distance between strings and generating all possible string alignments.
3. fuzzysearch: This library provides a fast and memory-efficient implementation of the Levenshtein distance algorithm for fuzzy string matching. It also includes functions for calculating the Jaccard similarity and the Hamming distance.
4. fuzzyhashlib: This library implements the Fuzzy Hash algorithm, a technique for calculating the similarity between two strings or byte sequences. It can be used for fuzzy string matching and detecting similar or duplicate data.

These are just a few examples of Python libraries that can be used for fuzzy string matching. The specific library chosen will depend on the requirements and constraints of the application.

## Getting started with fuzzy string matching in Python

### 1. TheFuzz

Here is an example of how to use the `thefuzz` library for fuzzy string matching in Python:

``````# First, install the fuzzywuzzy library using pip
!pip install thefuzz

# Import the necessary functions from the fuzzywuzzy library
from thefuzz import fuzz

# Define the strings to be matched
string1 = "John Smith"
string2 = "Jon Smithe"

# Calculate the Levenshtein distance between the strings
distance = fuzz.levenshtein(string1, string2)

# Print the distance
print(distance)``````

In this example, the `fuzz.levenshtein` function from the `thefuzz` library is used to calculate the Levenshtein distance between the two strings. The Levenshtein distance is a measure of the similarity between two strings, based on the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another. A smaller distance indicates a closer match.

In this case, the distance between “John Smith” and “Jon Smithe” is 1, indicating that the strings are similar but not exactly the same.

The `thefuzz` library provides several other algorithms for fuzzy string matching, including the Jaccard similarity, the token sort ratio, and others. It also includes functions for string preprocessing, such as removing punctuation and converting strings to lowercase.

### 2. python-Levenshtein

Here is an example of how to use the `python-Levenshtein` library for fuzzy string matching in Python:

``````# First, install the python-Levenshtein library using pip
!pip install python-Levenshtein

# Import the necessary functions from the python-Levenshtein library
import Levenshtein

# Define the strings to be matched
string1 = "John Smith"
string2 = "Jon Smithe"

# Calculate the Levenshtein distance between the strings
distance = Levenshtein.distance(string1, string2)

# Print the distance
print(distance)``````

In this example, the `Levenshtein.distance` function from the `python-Levenshtein` library is used to calculate the Levenshtein distance between the two strings. The Levenshtein distance is a measure of the similarity between two strings based on the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another. A smaller distance indicates a closer match.

In this case, the distance between “John Smith” and “Jon Smithe” is 1, indicating that the strings are similar but not exactly the same.

The `python-Levenshtein` library also provides functions for calculating the relative distance between strings and for generating all possible string alignments. These functions can be useful for analyzing the similarity between strings in more detail.

### 3. fuzzysearch

Here is an example of how to use the `fuzzysearch` library for fuzzy string matching in Python:

``````# First, install the fuzzysearch library using pip
!pip install fuzzysearch

# Import the necessary functions from the fuzzysearch library
import fuzzysearch

# Define the strings to be matched
string1 = "John Smith"
string2 = "Jon Smithe"

# Calculate the Levenshtein distance between the strings
distance = fuzzysearch.levenshtein(string1, string2)

# Print the distance
print(distance)``````

In this example, the `fuzzysearch.levenshtein` function from the `fuzzysearch` library is used to calculate the Levenshtein distance between the two strings. The Levenshtein distance is a measure of the similarity between two strings based on the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another. A smaller distance indicates a closer match.

In this case, the distance between “John Smith” and “Jon Smithe” is 1, indicating that the strings are similar but not the same.

The `fuzzysearch` library provides a fast and memory-efficient implementation of the Levenshtein distance algorithm and functions for calculating the Jaccard similarity and the Hamming distance. These functions can be useful for comparing the similarity of different strings in various contexts.

### 4. fuzzyhashlib

Here is an example of how to use the `fuzzyhashlib` library for fuzzy string matching in Python:

``````# First, install the fuzzyhashlib library using pip
!pip install fuzzyhashlib

# Import the necessary functions from the fuzzyhashlib library
import fuzzyhashlib

# Define the strings to be matched
string1 = "John Smith"
string2 = "Jon Smithe"

# Calculate the fuzzy hash of the strings
hash1 = fuzzyhashlib.fuzzy_hash(string1)
hash2 = fuzzyhashlib.fuzzy_hash(string2)

# Calculate the similarity between the hashes
similarity = fuzzyhashlib.compare(hash1, hash2)

# Print the similarity
print(similarity)``````

In this example, the `fuzzyhashlib.fuzzy_hash` function is used to calculate the fuzzy hash of each string, and the `fuzzyhashlib.compare` function is used to calculate the similarity between the two hashes. The fuzzy hash algorithm is a technique for calculating the similarity between two strings or byte sequences based on the number of common substrings. A higher similarity score indicates a closer match.

In this case, the similarity between “John Smith” and “Jon Smithe” would be calculated based on the number of common substrings in the two strings.

The `fuzzyhashlib` library can be used for fuzzy string matching and detecting similar or duplicate data. It provides a fast and efficient way to compare the similarity of different strings or byte sequences.

## Closing thoughts

Fuzzy string matching is an extremely useful tool, especially when working with people’s names or the names of corporations. It allows you to map entities extracted from, for example, a named entity recognition (NER) onto each other to create a more accurate further analysis.

Have you used fuzzy string matching before, and what was your use case? Let us know in the comments.

### Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

#### Meet Neri

Neri Van Otten is a machine learning and software engineer with over 12 years of Natural Language Processing (NLP) experience. Dedicated to making your projects succeed.

Popular posts

Connect with us

## Recent Articles

### How To Implement Anomaly Detection With One-Class SVM In Python

What is One-Class SVM? One-class SVM (Support Vector Machine) is a specialised form of the standard SVM tailored for unsupervised learning tasks, particularly anomaly...

### Decision Trees In ML Complete Guide [How To Tutorial, Examples, 5 Types & Alternatives]

What are Decision Trees? Decision trees are versatile and intuitive machine learning models for classification and regression tasks. It represents decisions and their...

### Isolation Forest For Anomaly Detection Made Easy & How To Tutorial

What is an Isolation Forest? Isolation Forest, often abbreviated as iForest, is a powerful and efficient algorithm designed explicitly for anomaly detection. Introduced...

### Batch Gradient Descent In Machine Learning Made Simple & How To Tutorial In Python

What is Batch Gradient Descent? Batch gradient descent is a fundamental optimization algorithm in machine learning and numerical optimisation tasks. It is a variation...

### Bias Mitigation in Machine Learning [Practical How-To Guide & 12 Strategies]

In machine learning (ML), bias is not just a technical concern—it's a pressing ethical issue with profound implications. As AI systems become increasingly integrated...

### Full-Text Search Explained, How To Implement & 6 Powerful Tools

What is Full-Text Search? Full-text search is a technique for efficiently and accurately retrieving textual data from large datasets. Unlike traditional search methods...

### Support Vector Regression (SVR) Simplified & How To Tutorial In Python

What is Support Vector Regression (SVR)? Support Vector Regression (SVR) is a machine learning technique for regression tasks. It extends the principles of Support...

### Support Vector Machines (SVM) In Machine Learning Made Simple & How To Tutorial

What are Support Vector Machines? Machine learning algorithms transform raw data into actionable insights. Among these algorithms, Support Vector Machines (SVMs) stand...

### Weight Decay In Machine Learning And Deep Learning Explained & How To Tutorial

What is Weight Decay in Machine Learning? Weight decay is a pivotal technique in machine learning, serving as a cornerstone for model regularisation. As algorithms...