Using FuzzyWuzzy for Fuzzy String Matching in Python

In everyday data processing, fields often contain slight variations (e.g., "广西" vs "广西壮族自治区"), making exact matching cumbersome.

FuzzyWuzzy is a simple Python package that uses the Levenshtein Distance algorithm to compute similarity scores between strings.

Install the library in an Anaconda Jupyter environment with:

<code>pip install -i https://pypi.tuna.tsinghua.edu.cn/simple FuzzyWuzzy</code>

The fuzz module provides four main functions:

Ratio : basic similarity (e.g., fuzz.ratio("河南省", "河南省") # 100 ).

Partial Ratio : similarity of substrings (e.g., fuzz.partial_ratio("河南", "河南省") # 80 ).

Token Sort Ratio : ignores word order and punctuation (e.g., fuzz.token_sort_ratio("西藏 区域", "区域 西藏") # 100 ).

Token Set Ratio : removes duplicate tokens before comparison (e.g., fuzz.token_set_ratio("西藏 西藏 区域", "区域 西藏") # 100 ).

The process module is useful when the set of possible matches is limited; it returns the best‑matching strings and their scores.

Example of extracting the top two matches:

<code>choices = ["河南省", "郑州市", "湖北省", "武汉市"]
process.extract("郑州", choices, limit=2)
# [('郑州市', 90), ('河南省', 0)]
</code>

For a single best match use process.extractOne :

<code>process.extractOne("郑州", choices)
# ('郑州市', 90)
</code>

Two practical applications are demonstrated:

Fuzzy matching of company names between two dataframes.

Fuzzy matching of province/city names.

The core merging function fuzzy_merge encapsulates the workflow: it extracts candidate matches with a configurable threshold (default 90) and limit , filters results below the threshold, and returns a new DataFrame with a 'matches' column.

<code># Fuzzy merge 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 to accept
    :param limit: number of top matches to consider
    :return: DataFrame with a '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
# df = fuzzy_merge(data, company, '公司名称', '公司名称', threshold=90)
# df
</code>

By wrapping this function in a reusable module, developers can quickly apply fuzzy matching to various data‑cleaning tasks without rewriting code.