Update and append new rows based on index value python

Question

I have two dataframes DF1, DF2 which have the same type of data and share some index values but not all

        DF1    
 index, a, b, c
[ abc   1, 3, 6 ]
[ acb   2, 4, 5 ]
[ cab   6, 5, 2 ]
[ bac   3, 6, 2 ]
[ bca   6, 8, 3 ]

        DF2
 index, a, b, d
[ abc   4, 7, 3 ]
[ kde   2, 5, 8 ]
[ lat   7, 2, 6 ]
[ bac   0, 4, 4 ]
[ bca   3, 6, 8 ]

as a result I want to achieve the following

1.) Add column D to DF1 based on the index match

2.) Add index and rows from DF2which are not present in DF1

        RESULT   
 index, a, b, c, d
[ abc   1, 3, 6, 3 ]
[ acb   2, 4, 5, - ]
[ cab   6, 5, 2, - ]
[ bac   3, 6, 2, 4 ]
[ bca   6, 8, 3, 8 ]
[ kde   2, 5, -, 8 ]
[ lat   7, 2, -, 6 ]

Answer 1

Let use combine_first:

Create data:

DF1 = pd.DataFrame({'a':[1,2,6,3,6],'b':[3,4,5,6,8],'c':[6,5,2,2,3]},index=['abc','acb','cab','bac','bca'])
DF2 = pd.DataFrame({'a':[4,2,7,0,3],'b':[7,5,2,4,6],'d':[3,8,6,4,8]},index=['abc','kde','lat','bac','bca'])

df_combo = DF1.combine_first(DF2)
print(df_combo)

       a    b    c    d
abc  1.0  3.0  6.0  3.0
acb  2.0  4.0  5.0  NaN
bac  3.0  6.0  2.0  4.0
bca  6.0  8.0  3.0  8.0
cab  6.0  5.0  2.0  NaN
kde  2.0  5.0  NaN  8.0
lat  7.0  2.0  NaN  6.0