Sum The Duplicate Rows Of Particular Columns In Dataframe

I want to add the particular columns (C, D, E, F, G) based on the duplicate rows of column B. Whereas the remaining non-duplicate rows unchanged. The output of column A must be the

Solution 1:

Create dictionary of columns names with aggregate functions and pass to agg, also here is necessary min_count=1 to sum for avoid 0 for sum NaNs values:

L = ['C','D','E','F','G']
d = {**dict.fromkeys(L, lambda x: x.sum(min_count=1)), **{'A':'first'}}
df = df.groupby('B', as_index=False, sort=False).agg(d).reindex(columns=df.columns)
print (df)
      A     B    C    D    E    F    G
0  box1  0487  1.0  1.0  1.0  1.0  1.0
1  blue  0478  2.0  2.0  2.0  2.0  2.0
2  flat  8704  1.0  1.0  1.0  NaN  2.0
3  dark  8740  1.0  1.0  1.0  1.0  1.0
4  late  4087  1.0  NaN  1.0  NaN  1.0

---

d = {**dict.fromkeys(L, 'sum'), **{'A':'first'}}
df = df.groupby('B', as_index=False, sort=False).agg(d).reindex(columns=df.columns)
print (df)
      A     B    C    D    E    F    G
0  box1  0487  1.0  1.0  1.0  1.0  1.0
1  blue  0478  2.0  2.0  2.0  2.0  2.0
2  flat  8704  1.0  1.0  1.0  0.0  2.0
3  dark  8740  1.0  1.0  1.0  1.0  1.0
4  late  4087  1.0  0.0  1.0  0.0  1.0