pandas groupby在组内排序

Question

我想将数据框分组为两列,然后对组内的聚合结果进行排序.

In [167]:
df

Out[167]:
count   job source
0   2   sales   A
1   4   sales   B
2   6   sales   C
3   3   sales   D
4   7   sales   E
5   5   market  A
6   3   market  B
7   2   market  C
8   4   market  D
9   1   market  E

In [168]:
df.groupby(['job','source']).agg({'count':sum})

Out[168]:
            count
job     source  
market  A   5
        B   3
        C   2
        D   4
        E   1
sales   A   2
        B   4
        C   6
        D   3
        E   7

我现在想在每个组中按降序对count列进行排序.然后只占前三行.得到类似的东西:

            count
job     source  
market  A   5
        D   4
        B   3
sales   E   7
        C   6
        B   4

Answer 1

您也可以一次性完成,首先进行排序,然后使用head来获取每组的前3个.

In[34]: df.sort_values(['job','count'],ascending=False).groupby('job').head(3)

Out[35]: 
   count     job source
4      7   sales      E
2      6   sales      C
1      4   sales      B
5      5  market      A
8      4  market      D
6      3  market      B

Answer 2

你想要做的实际上是groupby(在第一个groupby的结果上):sort并取每组的前三个元素.

从第一个groupby的结果开始:

In [60]: df_agg = df.groupby(['job','source']).agg({'count':sum})

我们按索引的第一级分组:

In [63]: g = df_agg['count'].groupby(level=0, group_keys=False)

然后我们想对每个组进行排序('order')并获取前三个元素:

In [64]: res = g.apply(lambda x: x.order(ascending=False).head(3))

但是,为此,有一个快捷方式可以执行此操作nlargest:

In [65]: g.nlargest(3)
Out[65]:
job     source
market  A         5
        D         4
        B         3
sales   E         7
        C         6
        B         4
dtype: int64

Answer 3

这是在排序顺序上排名前3的其他示例,以及在组内排序:

In [43]: import pandas as pd                                                                                                                                                       

In [44]:  df = pd.DataFrame({"name":["Foo", "Foo", "Baar", "Foo", "Baar", "Foo", "Baar", "Baar"], "count_1":[5,10,12,15,20,25,30,35], "count_2" :[100,150,100,25,250,300,400,500]})

In [45]: df                                                                                                                                                                        
Out[45]: 
   count_1  count_2  name
0        5      100   Foo
1       10      150   Foo
2       12      100  Baar
3       15       25   Foo
4       20      250  Baar
5       25      300   Foo
6       30      400  Baar
7       35      500  Baar


### Top 3 on sorted order:
In [46]: df.groupby(["name"])["count_1"].nlargest(3)                                                                                                                               
Out[46]: 
name   
Baar  7    35
      6    30
      4    20
Foo   5    25
      3    15
      1    10
dtype: int64


### Sorting within groups based on column "count_1":
In [48]: df.groupby(["name"]).apply(lambda x: x.sort_values(["count_1"], ascending = False)).reset_index(drop=True)
Out[48]: 
   count_1  count_2  name
0       35      500  Baar
1       30      400  Baar
2       20      250  Baar
3       12      100  Baar
4       25      300   Foo
5       15       25   Foo
6       10      150   Foo
7        5      100   Foo

Answer 4

试试这个，这是一种按降序进行分组和排序的简单方法：

df.groupby(['companyName'])['overallRating'].sum().sort_values(ascending=False).head(20)

Answer 5

如果您不需要对列进行求和,请使用@ tvashtar的答案.如果你确实需要求和,那么你可以使用@joris的答案或者这个与它非常相似的答案.

df.groupby(['job']).apply(lambda x: (x.groupby('source')
                                      .sum()
                                      .sort_values('count', ascending=False))
                                     .head(3))