Python pandas dataframe: find max for each unique values of an another column

Sample data (note that you posted an image which can't be used by potential answerers without retyping, so I'm making a simple example in its place):

df=pd.DataFrame({ 'id':[1,1,1,1,2,2,2,2],                   'a':range(8), 'b':range(8,0,-1) })

The key to this is just using idxmax and idxmin and then futzing with the indexes so that you can merge things in a readable way. Here's the whole answer and you may wish to examine intermediate dataframes to see how this is working.

df_max = df.groupby('id').idxmax()df_max['type'] = 'max'df_min = df.groupby('id').idxmin()df_min['type'] = 'min'df2 = df_max.append(df_min).set_index('type',append=True).stack().rename('index')df3 = pd.concat([ df2.reset_index().drop('id',axis=1).set_index('index'),                   df.loc[df2.values] ], axis=1 )df3.set_index(['id','level_2','type']).sort_index()                 a  bid level_2 type      1  a       max   3  5           min   0  8   b       max   0  8           min   3  52  a       max   7  1           min   4  4   b       max   4  4           min   7  1

Note in particular that df2 looks like this:

id  type   1   max   a    3          b    02   max   a    7          b    41   min   a    0          b    32   min   a    4          b    7

The last column there holds the index values in df that were derived with idxmax & idxmin. So basically all the information you need is in df2. The rest of it is just a matter of merging back with df and making it more readable.