Вивчайте Applying Fuzzy Matching to DataFrames | Fuzzy Matching and Similarity Detection

Fuzzy matching lets you find similar but not identical entries in a pandas DataFrame, helping you handle typos, inconsistent formatting, and alternative spellings. Use this technique to detect near-duplicates, group related records, and flag inconsistencies for data cleaning tasks like deduplication, record linkage, and standardization.

Fuzzy matching compares strings with a similarity function, such as .ratio() from difflib.SequenceMatcher, which returns a score between 0 and 1. A score of 1.0 means the strings are identical; lower values show less similarity. Apply this function row-wise to two columns to create a new column of similarity scores.

The similarity threshold sets the minimum score needed for two values to count as a match. Adjusting this threshold helps you control how strict or lenient your matching is, so you can efficiently identify and address near-duplicates in your DataFrame.


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import pandas as pd
from difflib import SequenceMatcher

def similarity(a, b):
    return SequenceMatcher(None, a, b).ratio()

data = {
    "ProductA": ["Apple iPhone", "Samsung Galaxy", "Google Pixel", "Apple iPone"],
    "ProductB": ["Apple iPhone", "Samsung Galaxi", "Google Pixel", "Apple iPhone"]
}

df = pd.DataFrame(data)

def compare_columns(row):
    return similarity(row["ProductA"], row["ProductB"])

df["SimilarityScore"] = df.apply(compare_columns, axis=1)
df["IsSimilar"] = df["SimilarityScore"] > 0.85

print(df)

Step 1: Choose columns to compare

Select the specific DataFrame columns that contain the values you want to compare for similarity, such as product names or customer records.

Step 2: Define or select a similarity function

Use a function that measures how similar two strings are. The SequenceMatcher from the difflib library provides a convenient .ratio() method for this purpose.

Step 3: Apply the similarity function row-wise to the selected columns

Use the .apply() method to compare each pair of values in the chosen columns, generating a similarity score for each row.

Step 4: Set a similarity threshold to determine what counts as a match

Decide on a cutoff value (such as 0.85) that defines when two values are considered similar enough to be flagged as a match.

Step 5: Flag or extract rows where the similarity exceeds the threshold

Create a new column or filter your DataFrame to highlight or extract only those rows where the similarity score is above your chosen threshold.

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              1234567891011121314151617181920
            
import pandas as pd
from difflib import SequenceMatcher

def similarity(a, b):
    return SequenceMatcher(None, a, b).ratio()

data = {
    "ProductA": ["Apple iPhone", "Samsung Galaxy", "Google Pixel", "Apple iPone"],
    "ProductB": ["Apple iPhone", "Samsung Galaxi", "Google Pixel", "Apple iPhone"]
}

df = pd.DataFrame(data)

def compare_columns(row):
    return similarity(row["ProductA"], row["ProductB"])

df["SimilarityScore"] = df.apply(compare_columns, axis=1)
df["IsSimilar"] = df["SimilarityScore"] > 0.85

print(df)