Python，数据帧，如何删除列中的一些值，并添加NaN，然后将该列与现有表合并

Question

这是我的表格的样子：

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

现在，我想再添加 9 列，分别是"2-D"、"3-D"、"..."、"10-D"。"2-D"中的值是："NaN"加上"1-D"中的所有值，但删除最后一个值(因为我一开始添加了"NaN"。与"3-D"相同："NaN"+ 列["2-D"]。

这是我想要得到的结果：

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

我认为您可以使用分配和转移：

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)}

生成一个字典，其中列名作为键，列系列作为值。** 运算符解压缩字典键和值，并将它们作为参数提供给赋值函数。这与调用相同：

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