groupby().last() drops columns

[gtakacs]

DataFrame.groupby().last() gives incorrect result in the following case (pandas version: 0.8.1):

df1 = DataFrame({"A": [1, 1], "B": [1, 1], "C": ["x", "y"]})
print df1.groupby("A").last()
#    B
# A   
#1  1

The integer column B is handled correctly, but the string column C is dropped.
The result is correct if column B is not present:

df2 = DataFrame({"A": [1, 1], "C": ["x", "y"]})
print df2.groupby("A").last()
#    C
# A   
#1  y

[manuteleco]

I've also come across the same issue today using "first()".

[changhiskhan]

groupby currently excludes non-numerical/non-boolean columns from many of the "shortcut" methods.
You can do something like as a workaround for now:

In [32]: df1.groupby('A').agg(lambda x: x.irow(-1))
Out[32]:
B C
A
1 1 y

[wesm]

In principle this should work though since it's not exactly a numerical computation. I'll have to look and see how difficult it will be to fix

[wesm]

Fixed after merging PR #1935

[zhaoxiaozhi-zz]

try df.groupby(['A','B'], as_index=False)['col1','col2'].first()