Groupby and lag all columns of a dataframe?

I want to lag every column in a dataframe, by group. I have a frame like this:

import numpy as np
import pandas as pd

index = pd.date_range('2015-11-20', periods=6, freq='D')

df = pd.DataFrame(dict(time=index, grp=['A']*3 + ['B']*3, col1=[1,2,3]*2,
    col2=['a','b','c']*2)).set_index(['time','grp'])

which looks like

                col1 col2
time       grp           
2015-11-20 A       1    a
2015-11-21 A       2    b
2015-11-22 A       3    c
2015-11-23 B       1    a
2015-11-24 B       2    b
2015-11-25 B       3    c

and I want it to look like this:

                col1 col2 col1_lag col2_lag
time       grp                     
2015-11-20 A       1    a        2        b
2015-11-21 A       2    b        3        c
2015-11-22 A       3    c       NA       NA
2015-11-23 B       1    a        2        b
2015-11-24 B       2    b        3        c
2015-11-25 B       3    c       NA       NA

This question manages the result for a single column, but I have an arbitrary number of columns, and I want to lag all of them. I can use groupby and apply, but apply runs the shift function over each column independently, and it doesn’t seem to like receiving an [nrow, 2] shaped dataframe in return. Is there perhaps a function like apply that acts on the whole group sub-frame? Or is there a better way to do this?

Answer

IIUC, you can simply use level="grp" and then shift by -1:

>>> shifted = df.groupby(level="grp").shift(-1)
>>> df.join(shifted.rename(columns=lambda x: x+"_lag"))
                col1 col2  col1_lag col2_lag
time       grp                              
2015-11-20 A       1    a         2        b
2015-11-21 A       2    b         3        c
2015-11-22 A       3    c       NaN      NaN
2015-11-23 B       1    a         2        b
2015-11-24 B       2    b         3        c
2015-11-25 B       3    c       NaN      NaN