CODEWITHSUNDEEP
pythonpandasdataframe
ali
ali

Reputation: 151

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 first index of duplicate rows.

I have a dataframe as follows:

A     B     C  D  E  F  G
box1  0487  1     1     1
box2  0487     1     1
blue  0478  1  1        1
gray  0478  1  1  1  1
gray  0478        1  1  1
flat  8704  1  1        1
clay  8704        1     1
dark  8740  1  1  1  1  1
late  4087  1     1     1

I want the output as follows:

A     B     C   D  E  F  G
box1  0487  1   1  1  1  1
blue  0478  2   2  2  2  2
flat  8704  1   1  1     2
dark  8740  1   1  1  1  1
late  4087  1      1     1

I am pleased to hear some suggestions.

Upvotes: 1

Views: 184

Answers (1)

jezrael
jezrael

Reputation: 862641

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

Upvotes: 7

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