接受的大熊猫数据框顶部行基于分组

Question

相关的问题在这里：Reordering pandas dataframe based on multiple column and sum of one column

我如何使用sort列时接受前2个国家在这个数据帧，： 在这种情况下，顶部2个国家将在澳大利亚和阿富汗

Country_FAO type mean_area  sort 
5 Australia car 12141000.0 18910501.0 
4 Australia car 6475695.0 18910501.0 
6 Australia bus 293806.0 18910501.0 
0 Afghanistan car 2029000.0 2141000.0 
1 Afghanistan car 112000.0 2141000.0 
2  Algeria bus 827000.0 829351.0 
3  Algeria bus  2351.0 829351.0 

- 编辑：

我也想保留type列。在这种情况下，解决方案应该是这样的：

Country_FAO type mean_area  sort 
5 Australia car 12141000.0 18910501.0 
4 Australia car 6475695.0 18910501.0 
6 Australia bus 293806.0 18910501.0 
0 Afghanistan car 2029000.0 2141000.0 
1 Afghanistan car 112000.0 2141000.0 

Answer 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 12141000.0 18910501.0 
4 Australia car 6475695.0 18910501.0 
6 Australia bus 293806.0 18910501.0 
0 Afghanistan car 2029000.0 2141000.0 
1 Afghanistan car 112000.0 2141000.0 

我会做这种方式：

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') 

还，您可能需要重置您的索引：

In [156]: df.groupby('Country_FAO').sum().nlargest(2, 'mean_area').reset_index() 
Out[156]: 
    Country_FAO mean_area 
0 Australia 18910501.0 
1 Afghanistan 2141000.0