在 Pandas groupby 模式下使用用户定义的函数，将其应用于多列并将结果分配给新的 Pandas 列

Question

I have a following data set:

> dt
   a b group
1: 1 5     a
2: 2 6     a
3: 3 7     b
4: 4 8     b

I have a following function:

def bigSum(a,b):
   return(a.min() + b.max())

I want to apply this function to a and b columns in groupby mode (by group) and assign it to the new column c of the data frame. My wished result is

    > dt
   a b group  c
1: 1 5     a  7
2: 2 6     a  7
3: 3 7     b  11
4: 4 8     b  11

For instance, if I would have used R data.table, I would do the following:

dt[, c := bigSum(a,b), by = group]

and it would work exactly as I expect. I am interested if there is something similar in pandas.

Answer 1

In pandas we have transform

g = df.groupby('group')
df['out'] = g.a.transform('min') + g.b.transform('max')
df
Out[282]: 
   a  b group  out
1  1  5     a    7
2  2  6     a    7
3  3  7     b   11
4  4  8     b   11

Update

df['new'] = df.groupby('group').apply(lambda x : bigSum(x['a'],x['b'])).reindex(df.group).values
df
Out[287]: 
   a  b group  out  new
1  1  5     a    7    7
2  2  6     a    7    7
3  3  7     b   11   11
4  4  8     b   11   11