被忽视的fuzzywuzzy库

fuzzywuzzy包一个可以对字符串进行模糊匹配的包

from fuzzywuzzy import fuzz
from fuzzywuzzy import process
  

 

  
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字符串的对比

fuzz.ratio()对位置敏感，全匹配

fuzz.partial_ratio()非完全匹配

str1 = '毛利是个小菜比'

str2 = '毛利是个小菜比,毛利是个小菜比'

print("fuzz.ratio相似度：",fuzz.ratio(str1,str2))

print("fuzz.partial_ratio相似度：",fuzz.partial_ratio(str1,str2))

  

 

  
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fuzz.ratio相似度： 64
fuzz.partial_ratio相似度： 100

  

 

  
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str1 = '毛利说：是个小菜比'
str2 = '毛利说是个小菜比'
print("fuzz.ratio相似度：",fuzz.ratio(str1,str2))
print("fuzz.partial_ratio相似度：",fuzz.partial_ratio(str1,str2))

  

 

  
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fuzz.ratio相似度： 94
fuzz.partial_ratio相似度： 88

  

 

  
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忽略顺序匹配（token_sort_ratio）

str1 = '毛利说：是个小菜比'
str2 = '是个小菜比：毛利说'
print("fuzz.ratio相似度：",fuzz.ratio(str1,str2))
print("fuzz.partial_ratio相似度：",fuzz.partial_ratio(str1,str2))
print("token_sort_ratio相似度：",fuzz.token_sort_ratio(str1,str2))

  

 

  
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fuzz.ratio相似度： 56
fuzz.partial_ratio相似度： 56
token_sort_ratio相似度： 100

  

 

  
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去重子集匹配（token_set_ratio）

str1 = '毛利说：是个小菜比'
str2 = '毛利说：是个小小菜比'
print("fuzz.ratio相似度：",fuzz.ratio(str1,str2))
print("fuzz.partial_ratio相似度：",fuzz.partial_ratio(str1,str2))
print("token_sort_ratio相似度：",fuzz.token_sort_ratio(str1,str2))
print("token_set_ratio相似度：",fuzz.token_set_ratio(str1,str2))

  

 

  
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fuzz.ratio相似度： 95
fuzz.partial_ratio相似度： 89
token_sort_ratio相似度： 95
token_set_ratio相似度： 95

  

 

  
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print(fuzz.token_sort_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear"))
print(fuzz.token_set_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear"))

  

 

  
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84
100

  

 

  
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process

用来返回模糊匹配的字符串和相似度

choices = ["python爬虫教程", "python机器学习教程", "Python数据分析教程", "pythonweb开发教程"]
print(process.extract("数据分析", choices, limit=3))
print(process.extractOne("分析", choices))

  

 

  
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[('Python数据分析教程', 90), ('python爬虫教程', 0), ('python机器学习教程', 0)]
('Python数据分析教程', 90)

  

 

  
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案例

求和

import numpy as np
import pandas as pd
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
def enum_row(row): print(row['state'])
def find_state_code(row): if row['state'] != 0: print(process.extractOne(row['state'], states, score_cutoff=80))
def capital(str): return str.capitalize()
def correct_state(row): if row['state'] != 0: state = process.extractOne(row['state'], states, score_cutoff=80) if state: state_name = state[0] return ' '.join(map(capital, state_name.split(' '))) return row['state']
def fill_state_code(row): if row['state'] != 0: state = process.extractOne(row['state'], states, score_cutoff=80) if state: state_name = state[0] return state_to_code[state_name] return ''
if __name__ == "__main__": pd.set_option('display.width', 200) data = pd.read_excel('.\\sales.xlsx', sheet_name='sheet1', header=0) print('data.head() = \n', data.head()) print('data.tail() = \n', data.tail()) print('data.dtypes = \n', data.dtypes) print('data.columns = \n', data.columns) for c in data.columns: print(c, end=' ') print() data['total'] = data['Jan'] + data['Feb'] + data['Mar'] print(data.head()) print(data['Jan'].sum()) print(data['Jan'].min()) print(data['Jan'].max()) print(data['Jan'].mean()) print('=============') # 添加一行 s1 = data[['Jan', 'Feb', 'Mar', 'total']].sum() print(s1) s2 = pd.DataFrame(data=s1) print(s2) print(s2.T) print(s2.T.reindex(columns=data.columns)) # 即： s = pd.DataFrame(data=data[['Jan', 'Feb', 'Mar', 'total']].sum()).T s = s.reindex(columns=data.columns, fill_value=0) print(s) data = data.append(s, ignore_index=True) data = data.rename(index={15:'Total'}) print(data.tail()) # apply的使用 print('==============apply的使用==========') data.apply(enum_row, axis=1) state_to_code = {"VERMONT": "VT", "GEORGIA": "GA", "IOWA": "IA", "Armed Forces Pacific": "AP", "GUAM": "GU", "KANSAS": "KS", "FLORIDA": "FL", "AMERICAN SAMOA": "AS", "NORTH CAROLINA": "NC", "HAWAII": "HI", "NEW YORK": "NY", "CALIFORNIA": "CA", "ALABAMA": "AL", "IDAHO": "ID", "FEDERATED STATES OF MICRONESIA": "FM", "Armed Forces Americas": "AA", "DELAWARE": "DE", "ALASKA": "AK", "ILLINOIS": "IL", "Armed Forces Africa": "AE", "SOUTH DAKOTA": "SD", "CONNECTICUT": "CT", "MONTANA": "MT", "MASSACHUSETTS": "MA", "PUERTO RICO": "PR", "Armed Forces Canada": "AE", "NEW HAMPSHIRE": "NH", "MARYLAND": "MD", "NEW MEXICO": "NM", "MISSISSIPPI": "MS", "TENNESSEE": "TN", "PALAU": "PW", "COLORADO": "CO", "Armed Forces Middle East": "AE", "NEW JERSEY": "NJ", "UTAH": "UT", "MICHIGAN": "MI", "WEST VIRGINIA": "WV", "WASHINGTON": "WA", "MINNESOTA": "MN", "OREGON": "OR", "VIRGINIA": "VA", "VIRGIN ISLANDS": "VI", "MARSHALL ISLANDS": "MH", "WYOMING": "WY", "OHIO": "OH", "SOUTH CAROLINA": "SC", "INDIANA": "IN", "NEVADA": "NV", "LOUISIANA": "LA", "NORTHERN MARIANA ISLANDS": "MP", "NEBRASKA": "NE", "ARIZONA": "AZ", "WISCONSIN": "WI", "NORTH DAKOTA": "ND", "Armed Forces Europe": "AE", "PENNSYLVANIA": "PA", "OKLAHOMA": "OK", "KENTUCKY": "KY", "RHODE ISLAND": "RI", "DISTRICT OF COLUMBIA": "DC", "ARKANSAS": "AR", "MISSOURI": "MO", "TEXAS": "TX", "MAINE": "ME"} states = list(state_to_code.keys()) print(fuzz.ratio('Python Package', 'PythonPackage')) print(process.extract('Mississippi', states)) print(process.extract('Mississipi', states, limit=1)) print(process.extractOne('Mississipi', states)) data.apply(find_state_code, axis=1) print('Before Correct State:\n', data['state']) data['state'] = data.apply(correct_state, axis=1) print('After Correct State:\n', data['state']) data.insert(5, 'State Code', np.nan) data['State Code'] = data.apply(fill_state_code, axis=1) print(data) # group by print('==============group by================') print(data.groupby('State Code')) print('All Columns:\n') print(data.groupby('State Code').sum()) print('Short Columns:\n') print(data[['State Code', 'Jan', 'Feb', 'Mar', 'total']].groupby('State Code').sum()) # 写入文件 data.to_excel('sales_result.xlsx', sheet_name='Sheet1', index=False)

  

 

  
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这方法好复杂，看来以后要写下office的笔记了

文章来源: maoli.blog.csdn.net，作者：刘润森！，版权归原作者所有，如需转载，请联系作者。

原文链接：maoli.blog.csdn.net/article/details/89576975