Using FuzzyWuzzy for Fuzzy String Matching in Python

In data processing, fields often contain slight variations (e.g., "Guangxi" vs. "Guangxi Zhuang Autonomous Region"), requiring flexible matching. The article presents FuzzyWuzzy , a Python package that leverages the Levenshtein Distance algorithm to compute similarity scores between strings.

The library provides four main ratio functions:

fuzz.ratio – simple similarity score.

fuzz.partial_ratio – compares the best matching substring.

fuzz.token_sort_ratio – tokenizes, lower‑cases, and sorts words before comparison.

fuzz.token_set_ratio – removes duplicate tokens before scoring.

Additionally, the process module offers:

process.extract – returns a list of the best matches with scores.

process.extractOne – returns the single highest‑scoring match as a tuple.

Practical examples show how to fuzzy‑match company names and province names. A reusable function fuzzy_merge is defined to join two DataFrames on fuzzy‑matched keys, with parameters for the left and right tables, key columns, similarity threshold, and result limit.

<code># fuzzy matching function

def fuzzy_merge(df_1, df_2, key1, key2, threshold=90, limit=2):
    """
    :param df_1: left DataFrame
    :param df_2: right DataFrame
    :param key1: column in df_1 to match
    :param key2: column in df_2 to match
    :param threshold: minimum similarity score (0‑100)
    :param limit: number of top matches to consider
    :return: DataFrame with a new 'matches' column containing the best match
    """
    s = df_2[key2].tolist()
    m = df_1[key1].apply(lambda x: process.extract(x, s, limit=limit))
    df_1['matches'] = m
    m2 = df_1['matches'].apply(lambda x: [i[0] for i in x if i[1] >= threshold][0] if len([i[0] for i in x if i[1] >= threshold]) > 0 else '')
    df_1['matches'] = m2
    return df_1

from fuzzywuzzy import fuzz, process

# example usage
result_df = fuzzy_merge(data, company, '公司名称', '公司名称', threshold=90)
result_df</code>

The article also includes installation instructions ( pip install -i https://pypi.tuna.tsinghua.edu.cn/simple FuzzyWuzzy ) and notes that installing python-Levenshtein can speed up calculations.

Overall, the guide equips readers with the concepts, functions, and ready‑to‑use code to perform robust fuzzy string matching in Python for data cleaning and integration tasks.