# How do you get the fuzzy match in pandas?

Fuzzy matching is a technique used to find strings that are similar to a given string. The idea is to find strings that are “close” to the given string, where “close” is defined in some way. For example, we might want to find strings that are close in terms of edit distance, or close in terms of meaning.

There are many applications for fuzzy matching. For example, it can be used to find typos in a document, or to match names in a database. It can also be used to automatically correct spelling errors, or to suggest similar words when a user is typing.

There are many different ways to measure “closeness”. Edit distance is a popular choice, but it is not the only one. Other options include cosine similarity, Jaccard similarity, and Levenshtein distance.

Fuzzy matching is a powerful tool, but it is not perfect. It can sometimes find strings that are not actually similar, and it can miss strings that are similar. It is important to choose the right similarity measure for the task at hand.

Edit Distance

Edit distance is a measure of similarity between two strings, defined as the minimum number of edits required to transform one string into the other. The edits can be insertions, deletions, or substitutions of characters.

For example, the edit distance between “cat” and “bat” is 1, because we can transform “cat” into “bat” by substituting the “c” for a “b”. The edit distance between “cat” and “cats” is also 1, because we can transform “cat” into “cats” by inserting an “s”.

The edit distance between “cat” and “dog” is 3, because we would need to substitute the “c” for a “d”, substitute the “a” for an “o”, and insert a “g” to transform “cat” into “dog”.

Cosine Similarity

Cosine similarity is a measure of similarity between two strings, defined as the cosine of the angle between their vectors. The vectors are created by representing each string as a set of n-grams, and then calculating the frequency of each n-gram.

For example, the cosine similarity between “cat” and “bat” is 0.8, because they share the n-grams “ca” and “at”. The cosine similarity between “cat” and “cats” is 0.9, because they share the n-grams “ca” and “at” and “ts”.

The cosine similarity between “cat” and “dog” is 0.6, because they share the n-gram “ca” but not “at” or “ts”.

Jaccard Similarity

Jaccard similarity is a measure of similarity between two strings, defined as the size of the intersection divided by the size of the union. The union is the set of all n-grams that appear in either string, and the intersection is the set of n-grams that appear in both strings.

For example, the Jaccard similarity between “cat” and “bat” is 0.5, because they share the n-gram “ca” but not “at” or “ts”. The Jaccard similarity between “cat” and “cats” is 0.67, because they share the n-grams “ca” and “at” but not “ts”.

The Jaccard similarity between “cat” and “dog” is 0, because they do not share any n-grams.

Levenshtein Distance

Levenshtein distance is a measure of similarity between two strings, defined as the minimum number of edits required to transform one string into the other. The edits can be insertions, deletions, or substitutions of characters.

For example, the Levenshtein distance between “cat” and “bat” is 1, because we can transform “cat” into “bat” by substituting the “c” for a “b”. The Levenshtein distance between “cat” and “cats” is also 1, because we can transform “cat” into “cats” by inserting an “s”.

The Levenshtein distance between “cat” and “dog” is 3, because we would need to substitute the “c” for a “d”, substitute the “a” for an “o”, and insert a “g” to transform “cat” into “dog”.

Conclusion

Fuzzy matching is a technique used to find strings that are similar to a given string. There are many different ways to measure similarity, including edit distance, cosine similarity, Jaccard similarity, and Levenshtein distance. Each of these measures has its own strengths and weaknesses, and it is important to choose the right one for the task at hand.

## What is the fuzzy match in pandas?

Fuzzy matching is the process of finding strings that are similar to a given string. In pandas, this is often used to find columns that are similar to a given column. For example, if you have a column that contains last names, you may want to find all of the other columns that contain last names.

Fuzzy matching is done using the fuzzywuzzy library. This library provides a number of functions for finding strings that are similar to a given string. The most important function for our purposes is the fuzz.ratio function. This function takes two strings and returns a score that indicates how similar the two strings are.

The fuzz.ratio function is not perfect. It sometimes returns a score that is too low for two strings that are actually similar. However, it is generally good at finding similar strings.

Once you have the fuzz.ratio scores for all of the columns, you can use them to find the columns that are most similar to the given column. To do this, you can use the pandas.DataFrame.sort_values function. This function sorts the DataFrame by the given column. The default behavior is to sort in ascending order. However, you can specify that you want to sort in descending order by setting the ascending parameter to False.

After you have sorted the DataFrame, you can use the pandas.DataFrame.head function to get the first few rows. These rows will contain the columns that are most similar to the given column.

You can also use the pandas.DataFrame.tail function to get the last few rows. These rows will contain the columns that are least similar to the given column.

Finally, you can use the pandas.DataFrame.loc function to get a specific row. This is useful if you want to get a column that is in the middle of the DataFrame.

import pandas as pd
from fuzzywuzzy import fuzz

# Get the column that we want to match
column = data[“column”]

# Get the fuzz.ratio scores for all of the columns
scores = []
for col in data.columns:
if col != column:
score = fuzz.ratio(column, col)
scores.append((col, score))

# Sort the scores in descending order
scores.sort(key=lambda x: x[1], reverse=True)

# Get the columns that are most similar to the given column
most_similar_columns = [x[0] for x in scores[:5]]

# Get the columns that are least similar to the given column
least_similar_columns = [x[0] for x in scores[-5:]]

# Get a specific column
column_in_the_middle = data.loc[:, scores[25][0]]

## How to get the fuzzy match in pandas?

Pandas is a great tool for data analysis and manipulation, and one of its great features is its ability to do a fuzzy match on two columns of data. This can be extremely useful when you’re trying to clean up or merge data from different sources.

There are a few different ways to do a fuzzy match in pandas, but the most straightforward way is to use the pandas.Series.str.match() method. This method takes a regular expression as its first argument, and will return a Boolean series indicating whether each value in the series matches the regular expression.

For example, let’s say we have two columns of data, one with first names and one with last names, and we want to see if there are any matches between them:

import pandas as pd

first_names = pd.Series([‘John’, ‘Jane’, ‘Joe’, ‘Mary’, ‘Mike’])
last_names = pd.Series([‘Smith’, ‘Doe’, ‘Johnson’, ‘Williams’, ‘Brown’])

first_names.str.match(last_names)

This will return a Boolean series indicating whether each first name matches a last name. In this case, only ‘Jane Doe’ will return True.

If we want to get a bit more sophisticated, we can use the pandas.Series.str.contains() method. This method takes a regular expression as its first argument and returns a Boolean series indicating whether each value in the series contains a match for the regular expression.

For example, let’s say we have a column of data with full names, and we want to see if there are any matches between the first names and last names:

full_names = pd.Series([‘John Smith’, ‘Jane Doe’, ‘Joe Johnson’, ‘Mary Williams’, ‘Mike Brown’])

first_names.str.contains(last_names)

This will return a Boolean series indicating whether each first name is contained in any of the last names. In this case, all of the first names except ‘Mike’ will return True.

You can also combine these methods to get even more precise results. For example, let’s say we want to see if there are any matches between the first names and last names, but we only want to match names that are 3 letters or longer:

first_names.str.match(last_names, flags=re.IGNORECASE, regex=r’\b(\w{3,})\b’)

This will return a Boolean series indicating whether each first name is a exact match for any of the last names, ignoring case. In this case, only ‘Jane Doe’ will return True.

As you can see, there are a lot of different ways to do a fuzzy match in pandas, and which one you use will depend on your specific needs. However, the methods described here should give you a good starting point.