Question

pandas groupby默认排序。但我想改变排序顺序。我怎么能这样做？

我猜我不能将sort方法应用于返回的groupby对象。

Answer 1

执行您的groupby，并使用reset_index（）将其重新导入DataFrame。然后排序。

grouped = df.groupby('mygroups').sum().reset_index()
grouped.sort_values('mygroups', ascending=False)

Answer 2

从Pandas 0.18开始，一种方法是使用分组数据的sort_index方法。

以下是一个例子：

np.random.seed(1)
n=10
df = pd.DataFrame({'mygroups' : np.random.choice(['dogs','cats','cows','chickens'], size=n), 
                   'data' : np.random.randint(1000, size=n)})

grouped = df.groupby('mygroups', sort=False).sum()
grouped.sort_index(ascending=False)
print grouped

data
mygroups      
dogs      1831
chickens  1446
cats       933

正如您所看到的，groupby列现在按降序排序，而不是默认的升序。

Answer 3

保留订单或按降序排序的其他实例：

In [97]: import pandas as pd                                                                                                    

In [98]: df = pd.DataFrame({'name':['A','B','C','A','B','C','A','B','C'],'Year':[2003,2002,2001,2003,2002,2001,2003,2002,2001]})

#### Default groupby operation:
In [99]: for each in df.groupby(["Year"]): print each                                                                           
(2001,    Year name
2  2001    C
5  2001    C
8  2001    C)
(2002,    Year name
1  2002    B
4  2002    B
7  2002    B)
(2003,    Year name
0  2003    A
3  2003    A
6  2003    A)

### order preserved:
In [100]: for each in df.groupby(["Year"], sort=False): print each                                                               
(2003,    Year name
0  2003    A
3  2003    A
6  2003    A)
(2002,    Year name
1  2002    B
4  2002    B
7  2002    B)
(2001,    Year name
2  2001    C
5  2001    C
8  2001    C)

In [106]: df.groupby(["Year"], sort=False).apply(lambda x: x.sort_values(["Year"]))                        
Out[106]: 
        Year name
Year             
2003 0  2003    A
     3  2003    A
     6  2003    A
2002 1  2002    B
     4  2002    B
     7  2002    B
2001 2  2001    C
     5  2001    C
     8  2001    C

In [107]: df.groupby(["Year"], sort=False).apply(lambda x: x.sort_values(["Year"])).reset_index(drop=True)
Out[107]: 
   Year name
0  2003    A
1  2003    A
2  2003    A
3  2002    B
4  2002    B
5  2002    B
6  2001    C
7  2001    C
8  2001    C

Answer 4

在执行groupby之前，您可以在数据框上执行sort_values()。 Pandas保留了groupby中的顺序。

In [44]: d.head(10)
Out[44]:
              name transcript  exon
0  ENST00000456328          2     1
1  ENST00000450305          2     1
2  ENST00000450305          2     2
3  ENST00000450305          2     3
4  ENST00000456328          2     2
5  ENST00000450305          2     4
6  ENST00000450305          2     5
7  ENST00000456328          2     3
8  ENST00000450305          2     6
9  ENST00000488147          1    11

for _, a in d.head(10).sort_values(["transcript", "exon"]).groupby(["name", "transcript"]): print(a)
              name transcript  exon
1  ENST00000450305          2     1
2  ENST00000450305          2     2
3  ENST00000450305          2     3
5  ENST00000450305          2     4
6  ENST00000450305          2     5
8  ENST00000450305          2     6
              name transcript  exon
0  ENST00000456328          2     1
4  ENST00000456328          2     2
7  ENST00000456328          2     3
              name transcript  exon
9  ENST00000488147          1    11

Answer 5

类似于上述答案之一，但是尝试将.sort_values()添加到.groupby()中将允许您更改排序顺序。如果您需要对单个列进行排序，则如下所示：

df.groupby('group')['id'].count().sort_values(ascending=False)

ascending=False从高到低排序，默认值为从低到高排序。

*请注意其中一些聚合。例如，.size（）和.count（）返回不同的值，因为.size（）计数NaNs。

What is the difference between size and count in pandas?

Answer 6

This kind of operation is covered under hierarchical indexing. Check out the examples here

When you groupby, you're making new indices. If you also pass a list through .agg(). you'll get multiple columns. I was trying to figure this out and found this thread via google.

It turns out if you pass a tuple corresponding to the exact column you want sorted on.

Try this:

# generate toy data 
ex = pd.DataFrame(np.random.randint(1,10,size=(100,3)), columns=['features', 'AUC', 'recall'])

# pass a tuple corresponding to which specific col you want sorted. In this case, 'mean' or 'AUC' alone are not unique. 
ex.groupby('features').agg(['mean','std']).sort_values(('AUC', 'mean'))

This will output a df sorted by the AUC-mean column only.

pandas groupby按降序排序

6 个答案: