python - 在 Pandas 中应用分组依据后获取最大计数的行值

Question

我有以下 df

>In [260]: df
>Out[260]:
    size market vegetable  confirm availability
0  Large    ABC    Tomato                   NaN
1  Large    XYZ    Tomato                   NaN
2  Small    ABC    Tomato                   NaN
3  Large    ABC     Onion                   NaN
4  Small    ABC     Onion                   NaN
5  Small    XYZ     Onion                   NaN
6  Small    XYZ     Onion                   NaN
7  Small    XYZ   Cabbage                   NaN
8  Large    XYZ   Cabbage                   NaN
9  Small    ABC   Cabbage                   NaN

1)如何获取最大尺寸的蔬菜的尺寸？

我在蔬菜和大小上使用了 groupby 来获得以下 df但是我需要获取包含最大大小的行蔬菜

In [262]: df.groupby(['vegetable','size']).count()
Out[262]:                 market  confirm availability
vegetable size
Cabbage   Large       1                     0
          Small       2                     0
Onion     Large       1                     0
          Small       3                     0
Tomato    Large       2                     0
          Small       1                     0

df2['vegetable','size'] = df.groupby(['vegetable','size']).count().apply( some logic )

必需的 Df :

  vegetable   size   max_count
0   Cabbage   Small     2
1     Onion   Small     3
2    Tomato   Large     2

2) 现在我可以说 df 有大量的“小卷心菜”。所以我需要用 small 填充所有卷心菜行的确认可用性列如何做到这一点？

    size market vegetable  confirm availability
0  Large    ABC    Tomato                   Large
1  Large    XYZ    Tomato                   Large
2  Small    ABC    Tomato                   Large
3  Large    ABC     Onion                   Small
4  Small    ABC     Onion                   Small
5  Small    XYZ     Onion                   Small
6  Small    XYZ     Onion                   Small
7  Small    XYZ   Cabbage                   Small    
8  Large    XYZ   Cabbage                   Small    
9  Small    ABC   Cabbage                   Small

Answer 1

1)

required_df = veg_df.groupby(['vegetable','size'], as_index=False)['market'].count()\
         .sort_values(by=['vegetable', 'market'])\
         .drop_duplicates(subset='vegetable', keep='last')

2)

merged_df = veg_df.merge(required_df, on='vegetable')
cols = ['size_x', 'market_x', 'vegetable', 'size_y']
dict_renaming_cols = {'size_x': 'size', 
                      'market_x': 'market',
                      'size_y': 'confirm_availability'}
merged_df = merged_df.loc[:,cols].rename(columns=dict_renaming_cols)