如何在索引上合并两个 pandas 数据帧但填充缺失值

Question

I have two dataframes

df

  x
0 1
1 1
2 1
3 1
4 1

df1

  y
1 1
3 1

And I want to merge them on the index, but still keep the indexes that aren't present in df1 . This is my desired output

  x  y
0 1  0
1 1  1
2 1  0
3 1  1
4 1  0

I have tried merging on index, like this

pd.merge(df, df1s, left_index=True, right_index=True)

But that gets rid of the index values not in df1 . For example:

  x y
1 1 1
3 1 1

This is not what I want. I have tried both outer and inner join, to no avail. I have also tried reading through other pandas merge questions, but can't seem to figure out my specific case here. Apologies if the merge questions are redundant, but again, I cannot figure out how to merge the way I would like in this certain scenario. Thanks!

Answer 1

Try to concatenate on rows and fill NaNs with 0

pd.concat([df,df1], axis=1).fillna(0)



  x    y
0  1  0.0
1  1  1.0
2  1  0.0
3  1  1.0
4  1  0.0

Answer 2

No need for any complicated merging, you can just copy the column over directly, fill the NaNs, and set the dtype. You can either do this directly, or with pd.concat() :

pd.concat([df1, df2], axis=1).fillna(0).astype(int)

   x  y
0  1  0
1  1  1
2  1  0
3  1  1
4  1  0