如何使用Python中的str.replacement()函数将地址替换为只有数字和一些字母

Question

我正在尝试匹配引用索引(coClean(上的左地址和紧地址(来自单独的表(，该索引是我在#Python#JupyterNotebook 中使用以下公式创建的

import pandas as pd
df1=pd.read_csv("/content/Addmatchdf1.csv")
df2=pd.read_csv("/content/Addmatchdf2.csv")
import re
def cleanAddress(series):
return series.str.lower().str.replace(r"[a-zs,]","")
df1["coClean"]=cleanAddress(df1["Address"])
df1["coClean"]=cleanAddress(df1["Address"]) 
df = pd.merge(df1, df2, 
on =['coClean'], 
how ='inner') 

这将生成一个coClean作为引用索引。

Address_y7 Pindara Blv，LANGWARRIN，VIC 3910VIC 33402a Manor Street17莫伊拉Avenue17 Newman Avenue，卡内基，Vic 316317 Maroona Rd，Carnenegie Vic 3163

Address_x	coClean
7 Pindara Bvd LANGWARRIN VIC 3910	73910
2a Manor St BACCHUS MARSH VIC 3340	23340
38 Sommersby Rd POINT COOK VIC 3030	383030	38 Sommerrsby Road，POINT COOK，VIC 3030
17 Moira Avenue，Carnegie，Vic 3163	173163
17 Moira Avenue，Carnegie，Vic 3163	173163
17 Moira Avenue，Carnegie，Vic 3163	173163

import pandas as pd
df1 = pd.DataFrame({"Address_x":["7 Pindara Bvd LANGWARRIN VIC 3910","2a Manor St BACCHUS MARSH VIC 3340","38 Sommersby Rd POINT COOK VIC 3030","17 Moira Avenue, Carnegie, Vic 3163"],"Address_y":["7 Pindara Blv, Langwarrin, VIC 3910","2a Manor Street, BACCHUS MARSH, VIC 3340","38 Sommersby Road, Point Cook, VIC 3030","17 Moira Avenue, Carnegie, Vic 3163"]})
def cleanAddress(series):
cocleans=[]
for address in series:
number_of_letters=0
coclean=""
for i in range(len(address)):
if address[i].isnumeric():
coclean+=address[i]
elif address[i].isalpha():
number_of_letters+=1
coclean+=address[i]
if number_of_letters==4:
break
for i in range(i,len(address)):
if address[i].isnumeric():
coclean+=address[i]
cocleans.append(coclean.lower())
return cocleans
df1["coClean"]=cleanAddress(df1["Address_x"])