Python，dataframe，如何删除列中的某些值，添加NaN，然后​​将列与现有表结合起来？

Question

以下是我的表格：

df=pd.DataFrame(np.random.rand(60).reshape(20,3))
df.columns=['price','time','1-D']

现在，我想再添加9个列，这些列是＆＃39; 2-D＆＃39;，＆＃39; 3-D＆＃39;，....，＆＃39; 10-D＆＃ 39 ;. ＆＃39; 2-D＆＃39;中的值是：＆＃39; NaN＆＃39;加上＆＃39; 1-D＆＃39;中的所有值，但删除最后一个值（因为我首先添加＆＃39; NaN＆＃39;。与＆＃39; 3-D＆＃39;相同：＆ ＃39; NaN＆＃39; +列[＆＃39; 2-D＆＃39;]。

以下是我想得到的结果：

Out[153]: 
       price      time       1-D       2-D       3-D       4-D       5-D  \
0   0.414901  0.173058  0.403901       NaN       NaN       NaN       NaN   
1   0.918011  0.262294  0.641516  0.403901       NaN       NaN       NaN   
2   0.524934  0.051661  0.329890  0.641516  0.403901       NaN       NaN   
3   0.654412  0.867551  0.263398  0.329890  0.641516  0.403901       NaN   
4   0.107087  0.192514  0.598509  0.263398  0.329890  0.641516  0.403901   
5   0.232152  0.144453  0.107699  0.598509  0.263398  0.329890  0.641516   
6   0.105883  0.243597  0.602938  0.107699  0.598509  0.263398  0.329890   
7   0.269133  0.036553  0.749552  0.602938  0.107699  0.598509  0.263398   
8   0.144464  0.516613  0.179333  0.749552  0.602938  0.107699  0.598509   
9   0.901749  0.908061  0.025336  0.179333  0.749552  0.602938  0.107699   
10  0.247895  0.231501  0.157868  0.025336  0.179333  0.749552  0.602938   
11  0.132763  0.501174  0.171977  0.157868  0.025336  0.179333  0.749552   
12  0.165358  0.407833  0.284734  0.171977  0.157868  0.025336  0.179333   
13  0.960481  0.511533  0.249719  0.284734  0.171977  0.157868  0.025336   
14  0.016045  0.512287  0.206942  0.249719  0.284734  0.171977  0.157868   
15  0.948984  0.911282  0.269408  0.206942  0.249719  0.284734  0.171977   
16  0.470319  0.468252  0.289175  0.269408  0.206942  0.249719  0.284734   
17  0.751353  0.167094  0.531566  0.289175  0.269408  0.206942  0.249719   
18  0.854261  0.894898  0.513595  0.531566  0.289175  0.269408  0.206942   
19  0.016631  0.110026  0.797857  0.513595  0.531566  0.289175  0.269408   

         6-D       7-D       8-D       9-D      10-D  
0        NaN       NaN       NaN       NaN       NaN  
1        NaN       NaN       NaN       NaN       NaN  
2        NaN       NaN       NaN       NaN       NaN  
3        NaN       NaN       NaN       NaN       NaN  
4        NaN       NaN       NaN       NaN       NaN  
5   0.403901       NaN       NaN       NaN       NaN  
6   0.641516  0.403901       NaN       NaN       NaN  
7   0.329890  0.641516  0.403901       NaN       NaN  
8   0.263398  0.329890  0.641516  0.403901       NaN  
9   0.598509  0.263398  0.329890  0.641516  0.403901  
10  0.107699  0.598509  0.263398  0.329890  0.641516  
11  0.602938  0.107699  0.598509  0.263398  0.329890  
12  0.749552  0.602938  0.107699  0.598509  0.263398  
13  0.179333  0.749552  0.602938  0.107699  0.598509  
14  0.025336  0.179333  0.749552  0.602938  0.107699  
15  0.157868  0.025336  0.179333  0.749552  0.602938  
16  0.171977  0.157868  0.025336  0.179333  0.749552  
17  0.284734  0.171977  0.157868  0.025336  0.179333  
18  0.249719  0.284734  0.171977  0.157868  0.025336  
19  0.206942  0.249719  0.284734  0.171977  0.157868  

Answer 1

我认为你可以使用assign和shift：

df.assign(**{'{}-D'.format(i): df['1-D'].shift(i-1) for i in range(2,11)})

一点解释：

{'{}-D'.format(i): df['1-D'].shift(i-1) for i in range(2,11)}

构建一个dict，列名为键，列系列为值。 **运算符解包字典键和值，并将它们作为参数提供给assign函数。它与呼叫相同：

df.assign(2-D=xxx, 3-D=yyy...)