CODEWITHSUNDEEP
pythonpandaspandas-groupby
dingx
dingx

Reputation: 1671

pandas groupby-apply missing columns

I have a DataFrame and i want to do a "groupby-apply" style operation, however, the "key" columns were missing after the operation: 

df = pd.DataFrame({
'col1':['A1', 'A2', 'A3'],
'col2':['B1', 'B1', 'B2'],
'col3':[1, 2, 3,]
})  
b1 = df.groupby(['col1', 'col2'], as_index=False)[['col3']].apply(lambda x: x+10)
b2 = df.groupby(['col1', 'col2'], as_index=False)['col3'].apply(lambda x: x+10)
print(b1)
print(b2)

b1 will print as: 

col3
0   11
1   12
2   13

b2 will print as: 

b
0  0    11
1  1    12
2  2    13
Name: col3, dtype: int64

how can i make use the group-key columns(['col1','col2'] also be printed, as 

col1  col2
A1    B1      11
A2    B1      12
A3    B2      13

Upvotes: 0

Views: 675

Answers (1)

BENY
BENY

Reputation: 323326

When you print the type of each groupby object you will see , that is Series, which will have the index ahead of it . Github Open issue

df.groupby(['col1', 'col2'], as_index=False)['col3'].apply(lambda x: type(x) )
Out[11]: 
col1  col2
A1    B1      <class 'pandas.core.series.Series'>
A2    B1      <class 'pandas.core.series.Series'>
A3    B2      <class 'pandas.core.series.Series'>
dtype: object

To get the expected output 

df.groupby(['col1', 'col2']).apply(lambda x: x['col3']+10).reset_index(level=-1,drop=True)
Out[32]: 
col1  col2
A1    B1      11
A2    B1      12
A3    B2      13
Name: col3, dtype: int64

Upvotes: 1

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