Create data frame with missing values

Question

I want to create a table and fill missing values with my data

The data look like this:

    {'A': 0, 'C': 0, 'D': 0, 'E': 0, 'F': 0, 'G': 0}
    {'A': 0, 'B': 0, 'C': 0, 'D': 0, 'E': 0, 'F': 0}

I want to convert the data into pandas data frame with missing values

     A B C D E F G
   1 0 na 0 0 0 0 0
   2 0 0 0 0 0 0 na

I could manually give the missing values (using the below code) and then convert it into data frame. Is there a better way to fill the missing values and convert it into data frame

import pandas as pd

s = (( 0,  'na',  0,  0,  0,  0,  0),
( 0,  0,  0,  0,  0,  0,  'na'))

print (pd.DataFrame(list(s)))

print (pd.DataFrame(list(s), columns=['A', 'B', 'C', 'D', 'E', 'F','G'], index=[1,2]))  

Thanks

Answer 1

If pass list of DataFrame then need only sorting columns names:

L = [ {'A': 0, 'C': 0, 'D': 0, 'E': 0, 'F': 0, 'G': 0},
      {'A': 0, 'B': 0, 'C': 0, 'D': 0, 'E': 0, 'F': 0}]
    
print (pd.DataFrame(L).sort_index(axis=1))
   A    B  C  D  E  F    G
0  0  NaN  0  0  0  0  0.0
1  0  0.0  0  0  0  0  NaN