how can I create new table based on the previous table in pandas?

Question

following to this post How to reorder indexed rows based on a list in Pandas data frame

import pandas as pd
df = pd.DataFrame({'name' : ['A', 'Z','C'],
                   'company' : ['Apple', 'Yahoo','Amazon'],
                   'height' : [130, 150,173]})

df = df.pivot(index="name", columns="company", values="height").fillna(0)

df.reindex(["Z", "C", "A"])


company Amazon  Apple   Yahoo
name            
   Z     0.0    0.0     150.0
   C.  173.0    0.0      0.0
   A     0.0   130.0     0.0

I wonder if I added more data and do this by following to this link Is there a way to copy only the structure (not the data) of a Pandas DataFrame?

df_1 = pd.DataFrame({'name' : ['A','Z','B','C','D'],
                   'company' : ['Apple','Yahoo','Alebaba','Amazon','Google'],
                   'height' : [130, 150,160,173,180]})

df_1 = df_1.pivot(index="name", columns="company", values="height").fillna(0)

df_1 = df_1.reindex_like(df)

the result is being like below

company Amazon  Apple   Yahoo
    name            
       Z     0.0    0.0     150.0
       C   173.0    0.0      0.0
       A     0.0   130.0     0.0

but I would like to see the result like this

company Amazon  Apple   Yahoo   Alebaba Google
name                    
 Z       0.0    0.0     150.0    0.0    0.0
 C     173.0    0.0       0.0    0.0    0.0
 A       0.0    130.0     0.0    0.0    0.0
 B       0.0    0.0       0.0   160.0   0.0
 D       0.0    0.0       0.0    0.0    180.0

this is fine with the small data but in case of thousand data, how can I fix this issues?

the dataset that will be added to the previous data can locate in any position.

any suggestion? T T

Answer 1

Use Index.difference with Index.append for new Index and columns values with no sorted values and change position by DataFrame.reindex:

print (df_1.index.difference(df.index))
Index(['B', 'D'], dtype='object', name='name')

print (df.index.append(df_1.index.difference(df.index)))
Index(['Z', 'C', 'A', 'B', 'D'], dtype='object', name='name')

---

idx = df.index.append(df_1.index.difference(df.index))
cols = df.columns.append(df_1.columns.difference(df.columns))
df_1 = df_1.reindex(index=idx, columns=cols)
print (df_1)
company  Amazon  Apple  Yahoo  Alebaba  Google
name                                          
Z           0.0    0.0  150.0      0.0     0.0
C         173.0    0.0    0.0      0.0     0.0
A           0.0  130.0    0.0      0.0     0.0
B           0.0    0.0    0.0    160.0     0.0
D           0.0    0.0    0.0      0.0   180.0