■ 背景

手边有79个学校参加 智能车深度学习组别 提交的技术报告，但是在等级的EXCEL表格中对应的学校的名称与报告名称有些区别。那么如何能够比较自动对这些名称进行匹配呢？

这里使用了PYTHON库中的difflib的 get_cose_matches函数，来寻找在一个string列表中与被匹配的string最接近的字符串，其中参数 cutoff来定义匹配的相似程度。 n参数定义匹配的字符数量。

为了能够始终找到可以匹配上的字符串， 对于cutoff采用了调整的方式，直道找到一个可以匹配的字符串。

for cf in range(20):
    match = difflib.get_close_matches(l, filelist, cutoff = 1.0 - cf/20.0, n=20)
    if len(match) > 0:
        matchlist.append(match[0])
        flag = 1
        break

Say we have a list of strings: _list = […,] and user input _input = ‘…’, how do we find the items in _list that most closely resemble _input?

Python has a built-in package called difflib with the function get_close_matches() that can help us.

get_close_matches(word, possibilities, n, cutoff) accepts four parameters:

• word - the word to find close matches for in our list

• possibilities - the list in which to search for close matches of word

• n (optional) - the maximum number of close matches to return. Must be > 0. Default is 3.

• cutoff (optional) - a float in the range [0, 1] that a possibility must score in order to be considered similar to word. 0 is very lenient, 1 is very strict. Default is 0.6.

• 相关应用：

1. 2020年创意组论文评曰

01匹配结果

前面自动匹配的过程还存在着五个错误。经过修改之后如下为正式的匹配结果：

02Python程序

#!/usr/local/bin/python
# -*- coding: gbk -*-
#============================================================
# INFOR1.PY                    -- by Dr. ZhuoQing 2020-08-06
#
# Note:
#============================================================

from headm import *
import pandas as pd
import difflib

#------------------------------------------------------------

inforexcel = tspstring2text('inforexcel').strip('\r\n')
paperdir = tspstring2text('paperdir').strip('\r\n')

printf(inforexcel)
printf(paperdir)

filedim = os.listdir(paperdir)
#printf(filedim)
filelist = filedim

#------------------------------------------------------------
excelfile = pd.read_excel(inforexcel)
listdata = excelfile.values.tolist()

excelname = []
for id,l in enumerate(listdata):
    excelname.append(l[0])

#------------------------------------------------------------
excellist = []
matchlist = []

error = 0

for l in excelname:
    excellist.append(l)

    flag = 0

    for cf in range(20):
        match = difflib.get_close_matches(l, filelist, cutoff = 1.0 - cf/20.0)
        if len(match) > 0:
            matchlist.append(match[0])
            flag = 1
            break

    if flag == 0:
        matchlist.append('*****')
        error = error + 1

#for l in excelname:
#    excellist.append(l)

#    flag = 0
#    match = difflib.get_close_matches(l, filelist, cutoff = 0.3)
#    if len(match) > 0:
#        matchlist.append(match[0])
#        flag = 1

#    if flag == 0:
#        matchlist.append('*****')
#        error = error + 1

#------------------------------------------------------------
tspmsgclr()
printf("EXCEL 文件名")
for m,n in zip(excellist, matchlist):
    printff(m, n)

#printf("No Match:%d"%error)
tspmsgcopy()

printf("Error:%d"%error)

#------------------------------------------------------------
#        END OF FILE : INFOR1.PY
#============================================================