Issue while pattern replacement in column value in pandas dataframe-CodePudding

I have a dataframe new_df as shown below .

I have issue that my current code contain replace if numeric value or special character value comes after character value in column in dataframe like 70317380CH71 or 70317380CH%(

Current output Dataframe

riskID                 locationCode                 locationCity  locationCountryCode
70317380CH71           CH71                         PRATTELN            CH
70520366HONGKONGHK     EU_LL1_H_HONG_KONG_HKG       HONG KONG           HK
70729363MIDDLETOWNUS   US_06457_MAZ_000023          MIDDLETOWN          US
70317380CH&?           CH&?                         GALE                CH

Current Code

# find rows with value in country and city
m1 = new_df[['locationCity', 'locationCountryCode']].notna().all(axis=1)
# find rows with a "_"

m2 = new_df['riskID'].str.contains('-|_', na=False)
#m3= new_df['locationCode'].str.contains('_')

# both conditions above
m = m1&m2
#n =m1&m3
# replace matching rows by number   city   country
new_df.loc[m, 'riskID'] = (new_df.loc[m, 'riskID'].str.extract('^(\d )', expand=False)
                         new_df.loc[m, 'locationCity'].str.replace(' ', '') new_df.loc[m, 'locationCountryCode'])

Expected Output

riskID                 locationCode                 locationCity  locationCountryCode
    70317380PRATTELNCH         CH71                         PRATTELN                CH
    70520366HONGKONGHK     EU_LL1_H_HONG_KONG_HKG       HONG KONG           HK
    70729363MIDDLETOWNUS   US_06457_MAZ_000023          MIDDLETOWN          US
    70317380GALECH         CH&?                         GALE                CH

How can this be done in pythin

CodePudding user response：

here are two options, both will have the same outcome. comment added with code.

option1: replace locationCode with locationCity locationCountryCode

import pandas as pd
from io import StringIO
raw_data='''
riskID,locationCode,locationCity,locationCountryCode
70317380CH71,CH71,PRATTELN,CH
70520366HONGKONGH,EU_LL1_H_HONG_KONG_HKG,HONG KONG,HK
70729363MIDDLETOWNUS,US_06457_MAZ_000023,MIDDLETOWN,US
70317380CH&?,CH&?,GALE,CH
'''
df=pd.read_csv(StringIO(raw_data))
df1=df.copy()
def change_risk_id(row): # function to handle the replacement
    risk_id=row['riskID']
    return risk_id.replace(row['locationCode'],row['locationCity'] row['locationCountryCode'])
df.insert(0,'riskID_new',df.apply(change_risk_id,axis=1))
df=df.drop('riskID',axis=1) # drop the old column
df = df.rename(columns={'riskID_new': 'riskID'}) # rename the column

option2: use regex to find the pattern and then replace

import pandas as pd
from io import StringIO
def change_risk_id_req_ex(row): # function to handle the replacement
    re_exp='(?:\d )(\S [\d\?&])*' # regular exp to find the pattern try here- https://regex101.com/r/e37o3D/1
    risk_id=row['riskID']
    search_results=re.search(re_exp,risk_id)
    if search_results.group(1):
        return risk_id.replace(search_results.group(1),row['locationCity'] row['locationCountryCode'])
    else:
        return risk_id

raw_data='''
riskID,locationCode,locationCity,locationCountryCode
70317380CH71,CH71,PRATTELN,CH
70520366HONGKONGH,EU_LL1_H_HONG_KONG_HKG,HONG KONG,HK
70729363MIDDLETOWNUS,US_06457_MAZ_000023,MIDDLETOWN,US
70317380CH&?,CH&?,GALE,CH
'''
df=pd.read_csv(StringIO(raw_data))
df1=df.copy()
df.insert(0,'riskID_new',df.apply(change_risk_id_req_ex,axis=1))
df=df.drop('riskID',axis=1) # drop the old column
df = df.rename(columns={'riskID_new': 'riskID'}) # rename the column