问题
df have
id measure t1 t2 t3
1 savings 1 2 5
1 income 10 15 14
1 misc 5 5 5
2 savings 3 6 12
2 income 4 20 80
2 misc 1 1 1
df want- add a new row to the measure for each id, called spend, calculated by subtracting measure=income - measure=savings, for each of the periods t1,t2,t3, for each id
id measure t1 t2 t3
1 savings 1 2 5
1 income 10 15 14
1 misc 5 5 5
1 spend 9 13 9
2 savings 3 6 12
2 income 4 20 80
2 misc 1 1 1
2 spend 1 14 68
Trying:
df.loc[df['Measure'] == 'spend'] =
df.loc[df['Measure'] == 'income']-
(df.loc[df['Measure'] == 'savings'])
Failing because I am not incorporating groupby for desired outcome
回答1:
Here is one way using groupby
diff
df1=df[df.measure.isin(['savings','spend'])].copy()
s=df1.groupby('id',sort=False).diff().dropna().assign(id=df.id.unique(),measure='spend')
df=df.append(s,sort=True).sort_values('id')
df
Out[276]:
id measure t1 t2 t3
0 1 savings 1.0 2.0 5.0
1 1 income 10.0 15.0 14.0
1 1 spend 9.0 13.0 9.0
2 2 savings 3.0 6.0 12.0
3 2 income 4.0 20.0 80.0
3 2 spend 1.0 14.0 68.0
Update
df1=df.copy()
df1.loc[df.measure.ne('income'),'t1':]*=-1
s=df1.groupby('id',sort=False).sum().assign(id=df.id.unique(),measure='spend')
df=df.append(s,sort=True).sort_values('id')
来源:https://stackoverflow.com/questions/57378381/pandas-add-new-row-based-on-sum-difference-of-other-rows