How to groupby and do addition with only those columns that are grouped

user_16652021 0
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I currently have the following dataframe: I want to group by probeset column and get a single value for each phchp subject by adding up the data with the same probeset values

       Probeset  phchp230v2  phchp273v3  phchp367v3
0    1554784_at    0.000000    0.000000    0.000000
1    1554784_at    0.000000    0.000000    0.000000
2     212983_at    0.244668    0.032524    0.113343
3     212983_at    0.022178    0.013750    0.011871
4  1566643_a_at    0.048200    0.089618    0.046528

What I'm looking for is this:

       Probeset  phchp230v2  phchp273v3  phchp367v3
0    1554784_at    0           0           0
1    1554784_at    0           0           0 
2     212983_at    0.244668    0.046274    0.125214
3     212983_at    0.244668    0.046274    0.125214      
4  1566643_a_at    0.048200    0.089618    0.046528

I've tried the following to no success, it does not group correctly:

for x in df_out:
    if 'phchp' in x:
        df_out[x] = df_out.groupby(['Probeset'])[x].sum()
pandas python
1 Answers

You can groupby + transform, then assign back to the DataFrame.

df1 = df.groupby('Probeset').transform('sum')
df[df1.columns] = df1

print(df)

       Probeset  phchp230v2  phchp273v3  phchp367v3
0    1554784_at    0.000000    0.000000    0.000000
1    1554784_at    0.000000    0.000000    0.000000
2     212983_at    0.266846    0.046274    0.125214
3     212983_at    0.266846    0.046274    0.125214
4  1566643_a_at    0.048200    0.089618    0.046528

Your loop also wasn't too far off, you just needed to use transform. With transform, the result of the groupby aggregation is broadcast to all rows belonging to that group, so it will align with the DataFrame index. Without transform, the groupby result has an index based on the group keys, so simple assignment back to the DataFrame won't align, given you have a RangeIndex. The small change needed is:

for x in df:
    if 'phchp' in x:
        df[x] = df.groupby('Probeset')[x].transform('sum')

And for clarity, here is the difference in the groupby results with and without transform.

# Index is unique values of `'phchp367v3'`
df.groupby('Probeset')['phchp367v3'].sum()
#Probeset
#1554784_at      0.000000
#1566643_a_at    0.046528
#212983_at       0.250428
#Name: phchp367v3, dtype: float64


# Index is the same as the original DataFrame
df.groupby('Probeset')['phchp367v3'].transform('sum')
#0    0.000000
#1    0.000000
#2    0.250428
#3    0.250428
#4    0.046528
#Name: phchp367v3, dtype: float64
user_43333590
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