I have two dataframes df1 and df2. df2 is the subset of df1
df1 = df1=[[0,1,0,0],
[1,2,0,0],
[2,0,0,0],
[2,4,0,0]]
df1 = pd.DataFrame(df1,columns=['A','B','C','D'])
>>> df1
A B C D
0 0 1 0 0
1 1 2 0 0
2 2 0 0 0
3 2 4 0 0
>>> df2
C D
2 1 3
3 2 4
I want to append the value of C and D from df2 to df1 based on index.
My expected output:
A B C D
0 0 1 0 0
1 1 2 0 0
2 2 0 1 3
3 2 4 2 4
Use DataFrame.update:
notice: this is inplace, so it overwrites your df1 without having it to assign back
Quoted from the docs:
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
df1.update(df2)
A B C D
0 0 1 0.00 0.00
1 1 2 0.00 0.00
2 2 0 1.00 3.00
3 2 4 2.00 4.00
To convert back to int, use DataFrame.astype:
df1 = df1.astype(int)
A B C D
0 0 1 0 0
1 1 2 0 0
2 2 0 1 3
3 2 4 2 4
Another solution, but less elegant, is to use addition and then fillna:
(df1 + df2).fillna(df1).astype(int)
Or
df1.add(df2).fillna(df1).astype(int)
A B C D
0 0 1 0 0
1 1 2 0 0
2 2 0 1 3
3 2 4 2 4
Another way is using reindex_like and fillna
df_final = df2.reindex_like(df1).fillna(df1).astype(int)
Out[89]:
A B C D
0 0 1 0 0
1 1 2 0 0
2 2 0 1 3
3 2 4 2 4
