只重复第一行和后一行，长度为数据帧的倍

Question

如何在数据帧之前重复第一行n次，在数据帧末尾重复最后一行，n表示数据帧的长度？ 我有一个数据帧：

   PT011  PT012  PT013  PT014  PT015  PT021  PT022  PT023  PT024  PT025
0 -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
1 -0.162  -0.12  -0.12  -0.10  -0.12  -0.12  -0.12  -0.12  -0.12  -0.12
2 -0.164  -0.14  -0.14  -0.11  -0.14  -0.14  -0.14  -0.14  -0.14  -0.14
3 -0.166  -0.16  -0.16  -0.11  -0.16  -0.16  -0.16  -0.16  -0.16  -0.16
4 -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15

我试过：

import pandas as pd
import numpy as np

probes = {'PT011': [-0.16,-0.162,-0.164,-0.166,-0.167],
        'PT012': [-0.1,-0.12,-0.14,-0.16,-0.15],
        'PT013': [-0.1,-0.12,-0.14,-0.16,-0.15],
        'PT014': [-0.09,-0.10,-0.11,-0.11,-0.13],
        'PT015': [-0.1,-0.12,-0.14,-0.16,-0.15],
        'PT021': [-0.1,-0.12,-0.14,-0.16,-0.15],
        'PT022': [-0.1,-0.12,-0.14,-0.16,-0.15],
        'PT023': [-0.1,-0.12,-0.14,-0.16,-0.15],
        'PT024': [-0.1,-0.12,-0.14,-0.16,-0.15],
        'PT025': [-0.2,-0.12,-0.14,-0.16,-0.15]
        }
df = pd.DataFrame(probes,columns= ['PT011', 'PT012','PT013','PT014','PT015','PT021','PT022','PT023','PT024','PT025'])
print(df)

new_df=df.iloc[np.arange(len(df)).repeat([5,1,1,1,1])]
print("Repeated dataframe:\n",new_df)

输出：

Repeated dataframe:

    PT011  PT012  PT013  PT014  PT015  PT021  PT022  PT023  PT024  PT025
0 -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
0 -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
0 -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
0 -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
0 -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
1 -0.162  -0.12  -0.12  -0.10  -0.12  -0.12  -0.12  -0.12  -0.12  -0.12
2 -0.164  -0.14  -0.14  -0.11  -0.14  -0.14  -0.14  -0.14  -0.14  -0.14
3 -0.166  -0.16  -0.16  -0.11  -0.16  -0.16  -0.16  -0.16  -0.16  -0.16
4 -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15

但是这个解决方案是不灵活的，因为每一列的长度都是可变的，不一定是5。你有没有更好更灵活的一行的想法

Answer 1

用途：

a = np.ones(len(df), dtype=int)

#if need repeat only first row 5 times 
#a[0] = len(df)+1
#if need repeat first and last row 5 times
a[[0, -1]] = len(df)+1
print (a)
[5 1 1 1 5]

new_df=df.iloc[np.arange(len(df)).repeat(a)]
print("Repeated dataframe:\n",new_df)
    PT011  PT012  PT013  PT014  PT015  PT021  PT022  PT023  PT024  PT025
0 -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
0 -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
0 -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
0 -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
0 -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
1 -0.162  -0.12  -0.12  -0.10  -0.12  -0.12  -0.12  -0.12  -0.12  -0.12
2 -0.164  -0.14  -0.14  -0.11  -0.14  -0.14  -0.14  -0.14  -0.14  -0.14
3 -0.166  -0.16  -0.16  -0.11  -0.16  -0.16  -0.16  -0.16  -0.16  -0.16
4 -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15
4 -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15
4 -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15
4 -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15
4 -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15

如果所有值都是数字，则可以使用：

new_df = pd.DataFrame(np.repeat(df.values, a, axis=0), columns=df.columns)
print("Repeated dataframe:\n",new_df)
     PT011  PT012  PT013  PT014  PT015  PT021  PT022  PT023  PT024  PT025
0  -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
1  -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
2  -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
3  -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
4  -0.160  -0.10  -0.10  -0.09  -0.10  -0.10  -0.10  -0.10  -0.10  -0.20
5  -0.162  -0.12  -0.12  -0.10  -0.12  -0.12  -0.12  -0.12  -0.12  -0.12
6  -0.164  -0.14  -0.14  -0.11  -0.14  -0.14  -0.14  -0.14  -0.14  -0.14
7  -0.166  -0.16  -0.16  -0.11  -0.16  -0.16  -0.16  -0.16  -0.16  -0.16
8  -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15
9  -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15
10 -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15
11 -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15
12 -0.167  -0.15  -0.15  -0.13  -0.15  -0.15  -0.15  -0.15  -0.15  -0.15