对每个组的n个最大值行进行筛选

Question

上下文

我希望，对于每支球队，列的数据框架，其中包含前三名得分球员。

在我看来，这是Dataframe.nlargest()和Dataframe.groupby()的结合，但我不认为这是支持的。我的理想解决办法是：

• 直接在df上执行，而不必创建其他的df和
• 相对性能(实际df形状为7M行和5 col)

输入

import pandas as pd
df = pd.read_json('{"team":{"0":"A","1":"A","2":"A","3":"A","4":"A","5":"B","6":"B","7":"B","8":"B","9":"B","10":"C","11":"C","12":"C","13":"C","14":"C"},"player":{"0":"Alice","1":"Becky","2":"Carmen","3":"Donna","4":"Elizabeth","5":"Fran","6":"Greta","7":"Heather","8":"Iris","9":"Jackie","10":"Kelly","11":"Lucy","12":"Molly","13":"Nina","14":"Ophelia"},"points":{"0":15,"1":11,"2":13,"3":8,"4":10,"5":28,"6":29,"7":18,"8":25,"9":9,"10":12,"11":23,"12":18,"13":10,"14":15}}')

| team | player    | points |
|------|-----------|--------|
| A    | Alice     | 15     |
| A    | Becky     | 11     |
| A    | Carmen    | 13     |
| A    | Donna     | 8      |
| A    | Elizabeth | 10     |
| B    | Fran      | 28     |
| B    | Greta     | 29     |
| B    | Heather   | 18     |
| B    | Iris      | 25     |
| B    | Jackie    | 9      |
| C    | Kelly     | 12     |
| C    | Lucy      | 23     |
| C    | Molly     | 18     |
| C    | Nina      | 10     |
| C    | Ophelia   | 15     |

期望输出

df_output = pd.read_json('{"team":{"0":"A","1":"A","2":"A","3":"B","4":"B","5":"B","6":"C","7":"C","8":"C"},"player":{"0":"Alice","1":"Becky","2":"Carmen","3":"Fran","4":"Greta","5":"Iris","6":"Lucy","7":"Molly","8":"Ophelia"},"points":{"0":15,"1":11,"2":13,"3":28,"4":29,"5":25,"6":23,"7":18,"8":15}}')
df_output

| team | player  | points |
|------|---------|--------|
| A    | Alice   | 15     |
| A    | Becky   | 11     |
| A    | Carmen  | 13     |
| B    | Fran    | 28     |
| B    | Greta   | 29     |
| B    | Iris    | 25     |
| C    | Lucy    | 23     |
| C    | Molly   | 18     |
| C    | Ophelia | 15     |

Answer 1

您可以使用方法：

In [1401]: df[df.groupby('team')['points'].rank(ascending=False) <= 3]
Out[1401]: 
   team   player  points
0     A    Alice      15
1     A    Becky      11
2     A   Carmen      13
5     B     Fran      28
6     B    Greta      29
8     B     Iris      25
11    C     Lucy      23
12    C    Molly      18
14    C  Ophelia      15

Answer 2

您可以将df.groupby与df.nlargest结合使用

df.groupby('team').apply(lambda x:x.nlargest(3,'points')).reset_index(drop=True)

  team   player  points
0    A    Alice      15
1    A   Carmen      13
2    A    Becky      11
3    B    Greta      29
4    B     Fran      28
5    B     Iris      25
6    C     Lucy      23
7    C    Molly      18
8    C  Ophelia      15

Answer 3

像这样的东西可能管用-

df.loc[df.groupby(['team'])['points'].nlargest(3).reset_index().drop(['team','points'], axis=1)['level_1'].values]
   team   player  points
0     A    Alice      15
2     A   Carmen      13
1     A    Becky      11
6     B    Greta      29
5     B     Fran      28
8     B     Iris      25
11    C     Lucy      23
12    C    Molly      18
14    C  Ophelia      15