以下舉例以同一個excel中， sheet2的詞語去匹配sheet1中詞語找模糊匹配結(jié)果來舉例
導(dǎo)入數(shù)據(jù)，讀取excel中sheet1(被匹配的目標(biāo)詞庫),sheet2(需要進(jìn)行匹配的詞)

import pandas as pd
import jieba
#需要進(jìn)行匹配的詞
attendee =  pd.read_excel('路徑/testnn.xlsx',sheet_name='Sheet2')
#被匹配的目標(biāo)詞庫
account =  pd.read_excel('路徑/testnn.xlsx',sheet_name='Sheet1')
attendee = attendee.values
account = account.values
#print(attendee)
#print(account)

結(jié)果：

…………………………………………………………

一、分詞匹配

把需要匹配的詞語和目標(biāo)詞語做分詞，對比分詞匹配度判定關(guān)聯(lián)關(guān)系

1、導(dǎo)入jieba分詞包，對目標(biāo)詞和待匹配詞進(jìn)行分詞，并將其導(dǎo)入至新字典中

#需要進(jìn)行匹配的詞的分詞結(jié)果字典
Sheet2 = {}
for i in attendee:
    HCO=[]
    temp = jieba.cut(i[0], cut_all=False)
    for a in temp:
        HCO.append(a)
    Sheet2[i[0]] = HCO
#print(Sheet2)
#被匹配的目標(biāo)詞庫的分詞結(jié)果字典
Sheet1 = {}
for i in account:
     HCO = []
     temp = jieba.cut(i[0], cut_all=False)
     for a in temp:
         HCO.append(a)
     Sheet1[i[0]] = HCO
#print(Sheet1)

結(jié)果：

2、遍歷分詞后結(jié)果字典，對比相同的關(guān)鍵詞并記錄匹配情況

for i in Sheet1:
    a = i
    if i in Sheet2:
        #如果名稱完全相同則返回名稱
        resultstr = i
    #如果名稱不完全相同，對比分詞后的詞語
    for j in Sheet2:
        b = j
        #需要進(jìn)行匹配的詞的分詞數(shù)量
        origin_num = 0  
        #兩分詞結(jié)果中匹配成功的分詞詞語數(shù)量
        match_num = 0
        #存儲需要進(jìn)行匹配的詞的分詞結(jié)果
        origin_l=[]
        for k in Sheet1[i] :
            # xxxx代表分詞結(jié)果中需要人工判定排除的異常詞
            if k != 'xxxx':
                c = k
                origin_l.append(k)
                origin_num = origin_num +1
                target_l = []
                target_num = 0
                for h in Sheet2[j] :
                    # xxxx代表分詞結(jié)果中需要人工判定排除的異常詞
                    if h != 'xxxx':
                        d = h
                        target_num = target_num +1
                        target_l.append(h)
                        if c == d:
                            match_num = match_num + 1
        #選取符合條件的結(jié)果輸出，每條詞語對應(yīng)一條結(jié)果
        if match_num > origin_num - match_num:
            data = {'origin_str': a, 'target_str': b, 'origin_l': origin_l, 'target_l': target_l,'origin_num': origin_num, 'target_num':target_num, 'match_num':match_num}
            print(data)

結(jié)果概覽：

整體代碼

import pandas as pd
import jieba
#需要進(jìn)行匹配的詞
attendee =  pd.read_excel('路徑/testnn.xlsx',sheet_name='Sheet2')
#被匹配的目標(biāo)詞庫
account =  pd.read_excel('路徑/testnn.xlsx',sheet_name='Sheet1')
attendee = attendee.values
account = account.values
#print(attendee)
#print(account)

Sheet2 = {}
for i in attendee:
    HCO=[]
    temp = jieba.cut(i[0], cut_all=False)
    for a in temp:
        HCO.append(a)
    Sheet2[i[0]] = HCO
#print(Sheet2)
#被匹配的目標(biāo)詞庫的分詞結(jié)果字典
Sheet1 = {}
for i in account:
     HCO = []
     temp = jieba.cut(i[0], cut_all=False)
     for a in temp:
         HCO.append(a)
     Sheet1[i[0]] = HCO
#print(Sheet1)
for i in Sheet1:
    a = i
    if i in Sheet2:
        #如果名稱完全相同則返回名稱
        resultstr = i
    #如果名稱不完全相同，對比分詞后的詞語
    for j in Sheet2:
        b = j
        #需要進(jìn)行匹配的詞的分詞數(shù)量
        origin_num = 0
        #兩分詞結(jié)果中匹配成功的分詞詞語數(shù)量
        match_num = 0
        #存儲需要進(jìn)行匹配的詞的分詞結(jié)果
        origin_l=[]
        for k in Sheet1[i] :
            # xxxx代表分詞結(jié)果中需要人工判定排除的異常詞
            if k != 'xxxx':
                c = k
                origin_l.append(k)
                origin_num = origin_num +1
                target_l = []
                target_num = 0
                for h in Sheet2[j] :
                    # xxxx代表分詞結(jié)果中需要人工判定排除的異常詞
                    if h != 'xxxx':
                        d = h
                        target_num = target_num +1
                        target_l.append(h)
                        if c == d:
                            match_num = match_num + 1
        #選取符合條件的結(jié)果輸出
        if match_num > origin_num - match_num:
            data = {'origin_str': a, 'target_str': b, 'origin_l': origin_l, 'target_l': target_l,'origin_num': origin_num, 'target_num':target_num, 'match_num':match_num}
            print(data)

二、距離匹配

調(diào)用fuzzywuzzy包中直接進(jìn)行判斷，采用距離匹配方式
兩個字符串之間，由一個轉(zhuǎn)成另一個所需的最少編輯操作次數(shù)。
編輯操作包括：將一個字符替換成另一個字符，插入字符，刪除字符。
一般來說，編輯距離越小，兩個串的相似度越大
整體代碼

import pandas as pd
import jieba
from fuzzywuzzy import fuzz
from fuzzywuzzy import process

attendee =  pd.read_excel('路徑/testnn.xlsx',sheet_name='Sheet2')
account =  pd.read_excel('路徑/testnn.xlsx',sheet_name='Sheet1')
attendee = attendee.values
account = account.values


Sheet2 = {}
for i in attendee:
    HCO=[]
    temp = jieba.cut(i[0], cut_all=False)
    for a in temp:
        HCO.append(a)
    Sheet2[i[0]] = HCO
print(Sheet2)
Sheet1 = {}
for i in account:
     HCO = []
     temp = jieba.cut(i[0], cut_all=False)
     for a in temp:
         HCO.append(a)
     Sheet1[i[0]] = HCO
print(Sheet1)
target_l = []
data = []
n = 0
for j in Sheet2:
    target_l.append(j)
for i in Sheet1:
    n = n+1
    target= {'搜索公司':i,'目標(biāo)公司': process.extractOne( i, target_l )[0],'目標(biāo)權(quán)重': process.extractOne( i, target_l )[1]}
    data.append(target)
    print (data)
df1 = pd.DataFrame(data)
print(df1)

writer = pd.ExcelWriter('路徑/testmm.xlsx')
df1.to_excel(writer, 'Final')
writer.save()
writer.close()

結(jié)果概覽：
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