python - pandas - get most recent value of a particular column indexed by another column (get maximum value of a particular column indexed by another column)

Question

I have the following dataframe:

   obj_id   data_date   value
0  4        2011-11-01  59500    
1  2        2011-10-01  35200 
2  4        2010-07-31  24860   
3  1        2009-07-28  15860
4  2        2008-10-15  200200

I want to get a subset of this data so that I only have the most recent (largest 'data_date') 'value' for each 'obj_id'.

I've hacked together a solution, but it feels dirty. I was wondering if anyone has a better way. I'm sure I must be missing some easy way to do it through pandas.

My method is essentially to group, sort, retrieve, and recombine as follows:

row_arr = []
for grp, grp_df in df.groupby('obj_id'):
    row_arr.append(dfg.sort('data_date', ascending = False)[:1].values[0])

df_new = DataFrame(row_arr, columns = ('obj_id', 'data_date', 'value'))

Answer 1

This is another possible solution. I believe it's is the fastest.

df.loc[df.groupby('obj_id').data_date.idxmax(),:]