Python 向numpy结构化数组添加新记录

这是对numpy阵列早期学习的延续。
结构化数组是从列表的元素创建的，然后填充值（下面未显示）

填充的数组如下所示：

    x   y   z   
x   0   1   0   
y   1   0   1,5 
z   0   1,5 0   

1.如何向上面添加新顶点？
2.添加顶点后，将以下数组添加到结构化数组的最干净过程是什么（注意：此数组中并非所有顶点都是新的）：

预期产出（请与我一起承担）：

---

看起来像是蟒蛇熊猫的工作

>>> import numpy as np
>>> import pandas as pd
>>> data=np.zeros((4,5))
>>> df=pd.DataFrame(data,columns=['x','y','z','a','b'])
>>> df
   x  y  z  a  b
0  0  0  0  0  0
1  0  0  0  0  0
2  0  0  0  0  0
3  0  0  0  0  0
>>> df['c']=0  #Add a new column
>>> df
   x  y  z  a  b  c
0  0  0  0  0  0  0
1  0  0  0  0  0  0
2  0  0  0  0  0  0
3  0  0  0  0  0  0

>>> new_data=pd.DataFrame([['0','1,2','0'],['1,2','0','1'],['0','1','0']],columns=['d','e','y'])
>>> new_data
     d    e  y
0    0  1,2  0
1  1,2    0  1
2    0    1  0

>>> df.merge(new_data,how='outer') #Merge data
    x  y   z   a   b   c    d    e
0   0  0   0   0   0   0  NaN  NaN
1   0  0   0   0   0   0  NaN  NaN
2   0  0   0   0   0   0  NaN  NaN
3   0  0   0   0   0   0  NaN  NaN
4 NaN  0 NaN NaN NaN NaN    0  1,2
5 NaN  0 NaN NaN NaN NaN    0    1
6 NaN  1 NaN NaN NaN NaN  1,2    0

有很多方法可以合并您显示的数据，您能更详细地解释一下您希望结束数组是什么样子吗

    d    e      y
d   0   '1,2'   0
e  '1,2' 0     '1'
f   0   '1'     0

    x   y   z   d   e   f
x   0   1   0   0   0   0
y   1   0   1,5 0   1   0
z   0   1,5 0   0   0   0
d   0   0   0   0   1,2 0
e   0   1   0   1,2 0   0
f   0   0   0   0   1   0

>>> import numpy as np
>>> import pandas as pd
>>> data=np.zeros((4,5))
>>> df=pd.DataFrame(data,columns=['x','y','z','a','b'])
>>> df
   x  y  z  a  b
0  0  0  0  0  0
1  0  0  0  0  0
2  0  0  0  0  0
3  0  0  0  0  0
>>> df['c']=0  #Add a new column
>>> df
   x  y  z  a  b  c
0  0  0  0  0  0  0
1  0  0  0  0  0  0
2  0  0  0  0  0  0
3  0  0  0  0  0  0

>>> new_data=pd.DataFrame([['0','1,2','0'],['1,2','0','1'],['0','1','0']],columns=['d','e','y'])
>>> new_data
     d    e  y
0    0  1,2  0
1  1,2    0  1
2    0    1  0

>>> df.merge(new_data,how='outer') #Merge data
    x  y   z   a   b   c    d    e
0   0  0   0   0   0   0  NaN  NaN
1   0  0   0   0   0   0  NaN  NaN
2   0  0   0   0   0   0  NaN  NaN
3   0  0   0   0   0   0  NaN  NaN
4 NaN  0 NaN NaN NaN NaN    0  1,2
5 NaN  0 NaN NaN NaN NaN    0    1
6 NaN  1 NaN NaN NaN NaN  1,2    0