How to make clusters of Pandas data frame?

I am trying to make a cluster of the following pandas data frame and trying to give the names. E.g – “Personal Info” is cluster name and it consist of (PERSON,LOCATION,PHONE_NUMBER,EMAIL_ADDRESS,PASSPORT,SSN, DRIVER_LICENSE) and also addition of there Counts. which will be 460.

Clusters:
for reference I am providing clusters structure

Input data:

Names              Counts

CREDIT_CARD        10
CRYPTO             20
DATE_TIME          28
DOMAIN_NAME        40
EMAIL_ADDRESS      45
IBAN_CODE          20
IP_ADDRESS         100
NRP                38
LOCATION           36
PERSON             90
PHONE_NUMBER       105
BANK_NUMBER        29
DRIVER_LICENSE     45
ITIN               38
PASSPORT           49
SSN                90
NHS                0

Output:

Cluster names         Total count

Personal Info        (90+36+105+45+49+90) = 460
Finance              (10+29+38+20) = 97  
Network              (100+40) = 140
Others               (20+28) = 48
Info                 (0) = 0

Answer

You can create an inverse dictionary and map:

d = {'personal_info': ['PERSON','LOCATION','PHONE_NUMBER','EMAIL_ADDRESS','PASSPORT','SSN','DRIVER_LICENSE'],
    'finance':['CREDIT_CARD','BANK_NUMBER','ITIN','IBAN_CODE'],
    'info': ['NHS'],
    'network':['IP_ADDRESS','DOMAIN_NAME'],
    'others':['CRYPTO','DATE_TIME','NRP']
    }

d_inv = {x:k for k, v in d.items() for x in v}

(df['Counts'].groupby(df['PII'].map(d_inv)).sum()
   .rename_axis('Cluster names')       # rename to match output
   .reset_index(name='Total count')
)

Output:

   Cluster names  Total count
0        finance           97
1           info            0
2        network          140
3         others           86
4  personal_info          460