Getting data from different dataframe

Question

I have a data frame

Name    Subset    Type    System
A00     IU00-A    OP      A
A00     IT00      PP      A
B01     IT-01A    PP      B
B01     IU        OP      B
B03     IM-09-B   LP      A
B03     IM03A     OP      A
B03     IT-09     OP      A
D09     IT        OP      A
D09     IM        LP      A
D09     IM        OP      A

which I have converted it to

Subset Cluster    Type Cluster    Name          System
IU,IT             OP,PP           A00           A
IM,IM,IT          LP, OP, OP      B03, D09      A
IU,IT             OP,PP           B01           B

using

out = df.assign(Subset=df['Subset'].str[:2])\
        .sort_values(by=df.columns.tolist())\
        .groupby('Name', as_index=False)\
        .agg(**{'Subset Cluster': ('Subset', ', '.join), 
                'Type Cluster': ('Type', ', '.join), 
                'System': ('System', 'first')})\
        .groupby(['Subset Cluster', 'Type Cluster', 'System'], as_index=False)\
        .agg(', '.join)

In this converted dataframe, I need to add another column that will give me all subsets for a particular Name.

Output Example:

Subset Cluster    Type Cluster    Name          System    Subsets
IU,IT             OP,PP           A00           A         IU00-A,IT00
IM,IM,IT          LP, OP, OP      B03, D09      A         IM-09-B,IM03A,IT-09,IT,IM,IM   
IU,IT             OP,PP           B01           B         IT-01A,IU

Answer 1

We could assign Subset Cluster first; then use a double groupby:

out = df.assign(**{'Subset Cluster': df['Subset'].str[:2]})\
        .sort_values(by=df.columns.tolist())\
        .groupby(['Name', 'System'], as_index=False)\
        .agg(', '.join).rename(columns={'Type':'Type Cluster'})\
        .groupby(['Subset Cluster', 'Type Cluster', 'System'], as_index=False)\
        .agg(', '.join)

Output:

  Subset Cluster Type Cluster System      Name                             Subset
0     IM, IM, IT   LP, OP, OP      A  B03, D09  IM-09-B, IM03A, IT-09, IM, IM, IT
1         IT, IU       PP, OP      A       A00                       IT00, IU00-A
2         IT, IU       PP, OP      B       B01                         IT-01A, IU