Starting from this dataframe
df = pd.DataFrame(
np.arange(3*4).reshape((4, 3)),
index=['a', 'b', 'c', 'd'],
columns=['A', 'B', 'C']
)
print(df)
A B C
a 0 1 2
b 3 4 5
c 6 7 8
d 9 10 11
I want to apply two functions to each column to generate two columns for each original column to obtain this shape, with a multiindex column nested below each original column:
A B C
x y x y x y
a 10 100 11 101 12 102
b 13 103 14 104 15 105
c 16 106 17 107 18 108
d 19 109 20 110 21 111
however, something like this doesn't work
df.apply(lambda series:
series.transform([lambda x: x+10, lambda x: x+100])
)
and raises ValueError: If using all scalar values, you must pass an index
Note that I do not want to use agg like in this answer, since this is not an aggregation. I also want to avoid referring to column names directly.
You just need to use df.transform() and give your functions names.
def x(k):
return k + 10
def y(k):
return k + 100
df.transform([x, y])
A B C
x y x y x y
a 10 100 11 101 12 102
b 13 103 14 104 15 105
c 16 106 17 107 18 108
d 19 109 20 110 21 111