Remove duplicates from all columns with condition value>0 in columns in data frame

Question

I need to remove duplicates from all of the columns.

My data:

id   country  publisher   weak     A        B        C
123    US        X          1     6.77      0        0
123    US        X          1       0      1.23     88.7
456    BZ        Y          2       0      56.87    9.65      
456    BZ        Y          2     2.76       0       0  
456    BZ        Y          2       0        0       0

I used drop_duplicates-

df1=df.drop_duplicates()

But I need a condition that will take all the values>0 for each id.

Also, I have more columns than just 'A','B','C' so I'm looking for solution that will take all the columns into account.

Here an example for what I'm looking for:

id   country  publisher  weak     A       B        C
123    US        X        1     6.77     1.23     88.7
456    BZ        Y        2     2.76     56.87    9.65

Answer 1

This will give you your desired output
groups=df.groupby(['id','country','publisher']).sum()

Answer 2

Try doing:

cols = ['A', 'B'] # change columns to aggregate more data

def app_func(s):
    return s[~s.eq(0)].bfill().dropna().drop_duplicates()

df.groupby(['id', 'country', 'publisher'])[cols].apply(app_func).reset_index()