Question

上下文

我想要为每个团队提供包含前三名得分手的数据框的行。

在我的脑海中，它是Dataframe.nlargest()和Dataframe.groupby()的组合，但我认为这不受支持。我理想的解决方案是：

直接在df上执行，而无需创建其他数据框
清晰易懂，并且
性能相对较好（实际df形状为7M行和5列）

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     |

所需的输出

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 df.groupby.rank 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一起使用

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

类似的方法可能有用-

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

Answer 4

另一种方法是sort_values和groupby().tail/head：

df.sort_values('points').groupby('team').tail(3)

输出：

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

或

df.sort_values('points', ascending=False).groupby('team').head(3)

输出：

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

筛选每组n个最大值的行

上下文

输入

所需的输出

4 个答案: