How can I Replace an address to have only numbers and some letters using str.replace() function in Python?

Question

I am trying to match the left and tight addresses (from separate tables) on a reference index (coClean) which I created with the following formula in #Python #JupyterNotebook

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-z\s\,]","")
df1["coClean"]=cleanAddress(df1["Address"])

df1["coClean"]=cleanAddress(df1["Address"]) 
df = pd.merge(df1, df2, 
                      on =['coClean'], 
                      how ='inner') 

This produces a coClean as a reference index.

Address_x	coClean	Address_y
7 Pindara Bvd LANGWARRIN VIC 3910	73910	7 Pindara Blv, Langwarrin, VIC 3910
2a Manor St BACCHUS MARSH VIC 3340	23340	2a Manor Street, BACCHUS MARSH, VIC 3340
38 Sommersby Rd POINT COOK VIC 3030	383030	38 Sommersby Road, Point Cook, VIC 3030
17 Moira Avenue, Carnegie, Vic 3163	173163	17 Moira Avenue, Carnegie, Vic 3163
17 Moira Avenue, Carnegie, Vic 3163	173163	17 Newman Avenue, Carnegie, VIC 3163
17 Moira Avenue, Carnegie, Vic 3163	173163	17 Maroona Rd, Carnegie VIC 3163

The problem I am facing is, apparently, some addresses under the same postcode have the same house number. But than the joining becomes difficult as the reference index is same.

How can i revise this function so that the reference index contains only

a. the house numbers
b. first four letters
c. postcode

So a new reference for '23340'(2a manor street bacchus marsh vic 3340) becomes '2aman3340'? So a list is returned like:

coClean
7pind3910
2aman3340
38somm3030
17moir3163
17newm3163
17maroo3163

I attempted to revise the function to include all letters and numbers

def cleanAddress(series):
return series.str.lower().str.replace(r"[^a-z\d]","")

But including all letters will not solve the problem as different tables contain street as st. and road as rd. Hence, a better strategy is to rely on house numbers and postcodes with some initial letters.

Thank you for your kind suggestions.

Update: I replaced

def cleanAddress(series):
return series.str.lower().str.replace(r"[a-z\s\,]","")
df1["coClean"]=cleanAddress(df1["Address"])

with

def cleanAddress(series):
    coclen=""
    number_of_letters=0
    if series:
        for i in range(len(series)):
            if series[i].isnumeric():
                coclen+=series[i]
            elif series[i].isalpha():
                number_of_letters+=1
                coclen+=series[i]
                if number_of_letters==4:
                    break
        for i in range(i,len(series)):
            if series[i].isnumeric():
                coclen+=series[i]
    return coclen

This returns an error while I execute

cleanAddress(df1["Address"])

The full error is as follows:
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-17-b653a19f5638> in <module>()
----> 1 df1["coClean"]=cleanAddress(df1["Address"])

1 frames
/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in __nonzero__(self)
   1328     def __nonzero__(self):
   1329         raise ValueError(
-> 1330             f"The truth value of a {type(self).__name__} is ambiguous. "
   1331             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
   1332         )

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Answer 1

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"])