在查询数据时，字符串匹配经常用到模糊匹配，这时就要用到模糊匹配算法，如Levenshtein Distance 算法，计算编辑距离，这里Python的thefuzz包实现了模糊匹配功能。
安装
pip install thefuzz使用
简单匹配
from thefuzz import fuzz
rt = fuzz.ratio("我在山东", "我在山东省")
print(rt) # 89非完全匹配
非完全匹配，精度较高。
from thefuzz import fuzz
rt = fuzz.partial_ratio("我在山东", "我在山东省")
print(rt) # 100忽略顺序匹配（Token Sort Ratio）
from thefuzz import fuzz
rt = fuzz.token_sort_ratio("我在 山东", "山东 我在")
print(rt) # 100去重子集匹配（Token Set Ratio）
from thefuzz import fuzz
rt = fuzz.token_set_ratio("我在 山东", "山东 我在 山东")
print(rt)extract提取多条数据
from thefuzz import process
choices = ["山东", "山东省", "青岛市", "青岛"]
ls = process.extract("青岛", choices, limit=2)
print(ls)
# [('青岛', 100), ('青岛市', 90)]如果只提取一条，limit参数可以改为1，或者用extractOne()。
from thefuzz import process
choices = ["山东", "山东省", "青岛市", "青岛"]
ls = process.extractOne("青岛", choices)
print(ls)Pandas Dataframe字段对比
from thefuzz import process
# 模糊匹配
def fuzzy_merge(df_1, df_2, key1, key2, threshold=90, limit=2):
"""
:param df_1: the left table to join
:param df_2: the right table to join
:param key1: key column of the left table
:param key2: key column of the right table
:param threshold: how close the matches should be to return a match, based on Levenshtein distance
:param limit: the amount of matches that will get returned, these are sorted high to low
:return: dataframe with boths keys and matches
"""
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
df = fuzzy_merge(df1, df2, '字段1', '字段2', threshold=90)
