Could I work on this issue?

I tried the same example with :
df.groupby(['val']*df.shape[1],axis=1).transform('shift')
which failed with
Shape of passed values is (2, 3), indices imply (3, 3)

while
df.groupby(['val']*df.shape[1],axis=1).transform(lambda x:x.shift())

gives :
A B
0 NaN 1.0
1 NaN 2.0
2 NaN 3.0

what output are we expecting here ?

@Ethanator Absolutely! Simply make a comment here "take" and github will assign it to you. If you encounter any difficulties, feel free to reach out here.

@xxsacxx Your 2nd result looks correct to me; the values are shifted 1 to the right, and since there are no values to the left of the first column, you get NaN. The presence of NaN then coerces the dtype to being a float.

take