Accepting Top Rows In Pandas Dataframe Based On Grouping

Related to the question here: Reordering pandas dataframe based on multiple column and sum of one column How can I accept the top 2 countries in this dataframe, when using sort col

Solution 1:

UPDATE:

In [166]: df.loc[df.Country_FAO.isin(df.groupby('Country_FAO').sum().nlargest(2, 'mean_area').index)]
Out[166]:
   Country_FAO type   mean_area        sort
5    Australia  car  12141000.018910501.04    Australia  car   6475695.018910501.06    Australia  bus    293806.018910501.00  Afghanistan  car   2029000.02141000.01  Afghanistan  car    112000.02141000.0

i would do it this way:

In [153]: df.groupby('Country_FAO').sum()
Out[153]:
              mean_area
Country_FAO
Afghanistan   2141000.0
Algeria        829351.0
Australia    18910501.0

In [154]: df.groupby('Country_FAO').sum().nlargest(2, 'mean_area')
Out[154]:
              mean_area
Country_FAO
Australia    18910501.0
Afghanistan   2141000.0

In [155]: df.groupby('Country_FAO').sum().nlargest(2, 'mean_area').index
Out[155]: Index(['Australia', 'Afghanistan'], dtype='object', name='Country_FAO')

also, you may want to reset your index:

In [156]: df.groupby('Country_FAO').sum().nlargest(2, 'mean_area').reset_index()
Out[156]:
   Country_FAO   mean_area
0    Australia  18910501.01  Afghanistan   2141000.0