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）如何获得最大尺寸的蔬菜的大小？

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

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提供了大量的“小白菜”。因此，我需要在所有白菜行中填充“确认可用性”列该怎么做？

    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)

Answer 2

您可以将分组的数据帧分配给另一个对象，然后可以对“蔬菜”的索引进行其他分组以获得最大所需值

d = df.groupby(['vegetable','size']).count()
d.groupby(d.index.get_level_values(0).tolist()).apply(lambda x:x[x.confirm == x.confirm.max()])

出局：

                     market confirm availability
vegetable   size            
Cabbage Cabbage Small   2   2   0
Onion   Onion   Small   3   3   0
Tomato  Tomato  Large   2   2   0

Answer 3

您可以将GroupBy与count一起使用，然后对重复项进行排序和删除：

res = df.groupby(['size', 'vegetable'], as_index=False)['market'].count()\
        .sort_values('market', ascending=False)\
        .drop_duplicates('vegetable')

print(res)

    size vegetable  market
4  Small     Onion       3
2  Large    Tomato       2
3  Small   Cabbage       2

在熊猫中应用分组后获得最大计数的行值

3 个答案: