如何删除pandas数据透视表中的多级索引

Question

我有一个给定的数据框：

df = {'TYPE' : pd.Series(['Advisory','Advisory1','Advisory2','Advisory3']),
 'CNTRY' : pd.Series(['IND','FRN','IND','FRN']),
 'VALUE' : pd.Series([1., 2., 3., 4.])}
df = pd.DataFrame(df)
df = pd.pivot_table(df,index=["CNTRY"],columns=["TYPE"]).reset_index()

旋转后，如何获得具有列和

df

的数据框，如下所示；删除多级索引，

VALUE

Type|CNTRY|Advisory|Advisory1|Advisory2|Advisory3
0     FRN     NaN      2.0      NaN     4.0 
1     IND     1.0      NaN      3.0     NaN 

Answer 1

您可以添加参数

values

:

df = pd.pivot_table(df,index="CNTRY",columns="TYPE", values='VALUE').reset_index()
print (df)
TYPE CNTRY  Advisory  Advisory1  Advisory2  Advisory3
0      FRN       NaN        2.0        NaN        4.0
1      IND       1.0        NaN        3.0        NaN

对于删除列名称

rename_axis

：

df = pd.pivot_table(df,index="CNTRY",columns="TYPE", values='VALUE') \
       .reset_index().rename_axis(None, axis=1)
print (df)
  CNTRY  Advisory  Advisory1  Advisory2  Advisory3
0   FRN       NaN        2.0        NaN        4.0
1   IND       1.0        NaN        3.0        NaN

但也许只是必要的

pivot

:

df = df.pivot(index="CNTRY",columns="TYPE", values='VALUE') \
       .reset_index().rename_axis(None, axis=1)
print (df)
  CNTRY  Advisory  Advisory1  Advisory2  Advisory3
0   FRN       NaN        2.0        NaN        4.0
1   IND       1.0        NaN        3.0        NaN

因为

pivot_table

默认聚合函数会重复

mean

:

df = {'TYPE' : pd.Series(['Advisory','Advisory1','Advisory2','Advisory1']),
 'CNTRY' : pd.Series(['IND','FRN','IND','FRN']),
 'VALUE' : pd.Series([1., 4., 3., 4.])}
df = pd.DataFrame(df)
print (df)
  CNTRY       TYPE  VALUE
0   IND   Advisory    1.0
1   FRN  Advisory1    1.0 <-same FRN and Advisory1 
2   IND  Advisory2    3.0
3   FRN  Advisory1    4.0 <-same FRN and Advisory1 

df = df.pivot_table(index="CNTRY",columns="TYPE", values='VALUE')
       .reset_index().rename_axis(None, axis=1)
print (df)
TYPE   Advisory  Advisory1  Advisory2
CNTRY                                
FRN         0.0        2.5        0.0
IND         1.0        0.0        3.0

替代

groupby

、聚合函数和

unstack

：

df = df.groupby(["CNTRY","TYPE"])['VALUE'].mean().unstack(fill_value=0)
      .reset_index().rename_axis(None, axis=1)
print (df)
  CNTRY  Advisory  Advisory1  Advisory2
0   FRN       0.0        2.5        0.0
1   IND       1.0        0.0        3.0

Answer 2

您可以将

set_index

与

unstack

一起使用

df.set_index(['CNTRY', 'TYPE']).VALUE.unstack().reset_index()

TYPE CNTRY  Advisory  Advisory1  Advisory2  Advisory3
0      FRN       NaN        2.0        NaN        4.0
1      IND       1.0        NaN        3.0        NaN

Answer 3

df.columns = df.columns.droplevel(level=1)

根据您的要求更改级别。