Find maximum value of each group within a Pandas Frame - pandas

Question

I do have a question, hoping that you could give me a little support. I looked into the archiv here, found a solution but that's taking much time and is not "beautiful", since works with Loops
Suppose you have a following frame
System Country_Key Name Bank_number_length Check rule for bank acct no.
PEM AD Andorra 8 2
PL1 AD Andorra 15 5
PPE AD Andorra 14 5
P11 AD Andorra 9 5
P16 AD Andorra 12 4
PEM AE Emirates 3 5
PL1 AE Emirates 15 4
PPE AE Emirates 15 5
P11 AE Emirates 15 6
P16 AE Emirates 13 5
I found the following approach for two columns Get the max value from each group with pandas.DataFrame.groupby
However, in my case I do really have many columns and need to set the index for the first three columns "System", "Country_Key" and "Name"
my desire output would be the following
System Country_Key Name Bank_number_length Check rule for bank acct no.
PEM AD Andorra
PL1 15 5
PPE 5
P11 5
P16
PEM AE Emirates
PL1 15
PPE 15
P11 15 6
P16
So actually dropping the lowest values except the max value. Any kind of hint would be really benefical

Answer 1

You can try mask the not max value to empty string and mask the duplicated values to empty string
keys = ['Country_Key', 'Name']
cols = ['Bank_number_length', 'Check rule for bank acct no.']
df[cols] = df[cols].mask(df[cols].ne(df.groupby(keys)[cols].transform(max)), '')
df.loc[df.duplicated(keys), keys] = ''
print(df)
System Country_Key Name Bank_number_length Check rule for bank acct no.
0 PEM AD Andorra
1 PL1 15 5
2 PPE 5
3 P11 5
4 P16
5 PEM AE Emirates
6 PL1 15
7 PPE 15
8 P11 15 6
9 P16