In general terms, is there a way to assign specific values to a column via groupby.transform(), where the groupby size is known in advance?
For example:
df = pd.DataFrame(data = {'A':[10,10,20,20],'B':['abc','def','ghi','jkl'],'GroupID':[1,1,2,2]})
funcDict = {'A':'sum','B':['specific_val_1', 'specific_val_2']}
df = df.groupby('GroupID').transform(funcDict)
where the result would be:
| index | A | B |
|---|---|---|
| 1 | 20 | specific_val_1 |
| 2 | 20 | specific_val_2 |
| 3 | 40 | specific_val_1 |
| 4 | 40 | specific_val_2 |
CodePudding user response：
transform can not accepted dict , so we can do agg with merge
out = df.groupby('GroupID',as_index=False)[['A']].sum()
out = out.merge(pd.DataFrame({'B':['specific_val_1', 'specific_val_2']}),how='cross')
Out[90]:
GroupID A B
0 1 20 specific_val_1
1 1 20 specific_val_2
2 2 40 specific_val_1
3 2 40 specific_val_2