python - Fastest way to add an extra row to a groupby in pandas -

i'm trying create new row each group in dataframe copying last row , modifying values. approach follows, concat step appears bottleneck (i tried append too). suggestions?

def gennewobs(df):     lastrowindex = df.obsnumber.idxmax()     row = pd.dataframe(df.ix[lastrowindex].copy())     # changes other values in row here     df = pd.concat([df,row], ignore_index=true)     return df  df = df.groupby(group).apply(gennewobs) 

edit 1: have bunch of data last observation on different dates. want create final observation groups on current date.

group   date       days since last observation      1/1/2014               0      1/10/2014              9   b    1/5/2014               0   b    1/25/2014              20   b    1/27/2014              2 

if pretend current date 1/31/2014 becomes:

group   date       days since last observation      1/1/2014               0      1/10/2014              9      1/31/2014              21   b    1/5/2014               0   b    1/25/2014              20   b    1/27/2014              2   b    1/31/2014              4 

i've tried setting enlargement , slowest of techniques. ideas?

thanks user1827356, sped factor of 100 taking operation out of apply. reason first dropping group column, used idxmax instead.

def gennewobs(df):     lastrowindex = df.groupby(group).date.idxmax()     rows = df.ix[lastrowindex]     df = pd.concat([df,rows], ignore_index=true)     df = df.sort([group, date], ascending=true)     return df