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python – Pandas对groupby的结果排序取TopK

我想按两列对DataFrame进行分组,然后对各组中的汇总结果进行排序,怎么做?

pandas
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

现在,我想在每个组中按降序对计数列进行排序。然后只取前三行。得到如下的数据:

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

 

最佳回答

在第一个groupby的结果上,再次使用groupby操作:对每个组进行排序并取前三个元素的值。

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,我们要对每个组进行排序(‘order’),并采用前三个元素:

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

当然,更好的办法是使用快捷方式函数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

 

次佳回答

也可以一行命令就搞定,方法是先进行排序,然后使用head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

 

dataframe

一个更完整的示例:

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

dataframe sort


参考资料

 

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