dataframe concatenation by column value (no outer merge)

Question

This is a new question after this, with more information

I want to merge two dataframes like the outer join, but I do not want the cartesian product, but only the concatenation, for example:

with : df3 = df1.merge(df2, left_on=['A'], right_on=['B'], how='outer') I get df3:

      A     B
0   2.0     2
1   2.0     2
2   2.0     2
3   2.0     2
4   2.0     2
5   2.0     2
6   2.0     2
7   2.0     2
8   2.0     2
9   2.0     2
10  3.0     3
11  NaN     1
12  NaN

But I want:

   A    B
0   2.0     2
1   2.0     2
2   2.0     NaN
3   2.0     NaN
4   2.0     NaN
5   3.0     3
6   NaN     1
7   NaN     4

just concatenate the first 'm' of df1 with the m of df2 and dhe remaining values of df1 with a NaN value

score 0 · Answer 1 · answered May 23 '20 at 09:51

0

You might want to try/use the concat method. ex:

result = pd.concat([A, B], axis=1, sort=False)

You can read more here.

answered May 23 '20 at 09:51

JohnDoe_Scientist

590
1
6
18

1

Did you try launching what you wrote? – MarianD May 23 '20 at 09:54
1

No this doesn't give what OP wanted. `print(result)` you'll understand. – Ch3steR May 23 '20 at 09:55

score 0 · Accepted Answer · answered May 23 '20 at 11:05

0

get the cumulative counts of A and B, and use the combination of the counts with A and B as merge conditions :

df1['checker'] = df1.groupby("A").cumcount()

df2['checker'] = df2.groupby("B").cumcount()

res = df1.merge(df2,left_on=['A','checker'],right_on=['B','checker'],how='outer').drop('checker',axis=1)


res 


     A  B
0   2.0 2.0
1   2.0 2.0
2   2.0 NaN
3   2.0 NaN
4   2.0 NaN
5   3.0 3.0
6   NaN 1.0
7   NaN 4.0

answered May 23 '20 at 11:05

sammywemmy

27,093
4
17
31

Thanks! This seems to work correctly. I will make some more test. – linofex May 24 '20 at 07:23

dataframe concatenation by column value (no outer merge)

2 Answers2