How to join pandas dataframe on 2 columns?

Question

Assume the following DataFrames

df1:

id    data1
1     10
2     200
3     3000
4     40000

df2:

id1    id2    data2
1      2      210
1      3      3010
1      4      40010
2      3      3200
2      4      40200
3      4      43000

I want the new df3:

id1    id2    data2    data11    data12        
1      2      210      10        200
1      3      3010     10        3000
1      4      40010    10        40000 
2      3      3200     200       3000
2      4      40200    200       40000
3      4      43000    3000      40000

What is the correct way to achieve this in pandas?

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Edit: Please not the specific data can be arbitrary. I chose this specific data just to show where everything comes from, but every data element has no correlation to any other data element.

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Other dataframes examples, because the first one wasn't clear enough:

df4:

id    data1
1     a
2     b
3     c
4     d

df5:

id1    id2    data2
1      2      e
1      3      f
1      4      g
2      3      h
2      4      i
3      4      j

I want the new df6:

id1    id2    data2    data11    data12        
1      2      e        a         b  
1      3      f        a         c
1      4      g        a         d
2      3      h        b         c
2      4      i        b         d
3      4      j        c         d

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Edit2: Data11 and Data12 are simply a copy of data1 , with the corresponding id id1 or id2

Answer 1

1.First merge both dataframe using id1 and id column
2.rename data1 as data11
3. drop id column
4. Now merge df1 and df3 on id2 and id

df3 = pd.merge(df2,df1,left_on=['id1'],right_on=['id'],how='left')
df3.rename(columns={'data1':'data11'},inplace=True)
df3.drop('id',axis=1,inplace=True)

df3 = pd.merge(d3,df1,left_on=['id2'],right_on=['id'],how='left')
df3.rename(columns={'data1':'data12'},inplace=True)
df3.drop('id',axis=1,inplace=True)

I hope it would solve your problem

Answer 2

Try this:

# merge dataframes, first on id and id1 then on id2
df3 = pd.merge(df1, df2, left_on="id", right_on="id1", how="inner")
df3 = pd.merge(df1, df3, left_on="id", right_on="id2", how="inner")

# rename and reorder columns
cols = [ 'id1', 'id2', 'data2', 'data1_y', 'data1_x']
df3 = df3[cols]

new_cols = ["id1", "id2", "data2", "data11", "data12"]
df3.columns = new_cols

df3.sort_values("id1", inplace=True)

print(df3)

This prints out:

    id1 id2 data2   data11  data12
0   1   2   210     10      200
1   1   3   3010    10      3000
2   1   4   40010   10      40000
3   2   3   3200    200     3000
4   2   4   40200   200     40000
5   3   4   43000   3000    40000

Answer 3

one of the solution to your problem is:

data1 = {'id' : [1,2,3,4],
         'data1' : [10,200,3000,40000]}

data2 = {'id1' : [1,1,1,2,2,3],
         'id2' : [2,3,4,3,4,4],
         'data2' : [210,3010,40010,3200,40200,43000]}

df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

df1:
id    data1
1     10
2     200
3     3000
4     40000

df2:
id1    id2    data2
1      2      210
1      3      3010
1      4      40010
2      3      3200
2      4      40200
3      4      43000

df3 = df2.set_index('id1').join(df1.set_index('id'))
df3.index.names = ['id1']
df3.reset_index(inplace=True)

final = df3.set_index('id2').join(df1.set_index('id'), rsuffix='2')
final.index.names = ['id2']
final.reset_index(inplace=True)

final[['id1','id2','data2','data1','data12']].sort_values('id1')

output df: 

id1 id2 data2   data1   data12
 1   2    210    10     200
 1   3    3010   10     3000
 1   4    40010  10     40000
 2   3    3200   200    3000
 2   4    40200  200    40000
 3   4    43000  3000   40000

I hope this will help you. ​

Answer 4

Using merge in a for loop with range and f-string

One way we can generalise this and to make it more easily expandable when having more than two dataframes, is to use list comprehension and a for loop with range .

After that we drop the duplicate column names:

dfs = [df2.merge(df1, 
                 left_on=f'id{x+1}', 
                 right_on='id', 
                 how='left').rename(columns={'data1':f'data1{x+1}'}) for x in range(2)]

df = pd.concat(dfs, axis=1).drop('id', axis=1)

df = df.loc[:, ~df.columns.duplicated()]

Output

   id1  id2  data2  data11  data12
0    1    2    210      10     200
1    1    3   3010      10    3000
2    1    4  40010      10   40000
3    2    3   3200     200    3000
4    2    4  40200     200   40000
5    3    4  43000    3000   40000

Answer 5

As @tawab_shakeel has mentioned earlier, your primary step is to merge the Dataframes on a particular column based on certain (SQL) join rules; just for you to understand the different approaches to merging on specific column(s), here is a general guide.

Joining Dataframes in Pandas

SQL Join Types

Answer 6

use two left hand merges on column id1 and id2 for dataframe df2

txt="""id,data1 1,a 2,b 3,c 4,d """

from io import StringIO
f = StringIO(txt)
df1 = pd.read_table(f,sep =',')
df1['id']=df1['id'].astype(int)

txt="""id1,id2,data2
1,2,e
1,3,f
1,4,g
2,3,h
2,4,i
3,4,j
"""

f = StringIO(txt)
df2 = pd.read_table(f,sep =',')
df2['id1']=df2['id1'].astype(int)
df2['id2']=df2['id2'].astype(int)

left_on='id1'
right_on='id'
suffix='_1'
df2=df2.merge(df1, how='left', left_on=left_on, right_on=right_on, 
                  suffixes=("", suffix))

left_on='id2'
right_on='id'
suffix='_2'
df2=df2.merge(df1, how='left', left_on=left_on, right_on=right_on, 
                  suffixes=("", suffix))

print(df2)

output

   id1  id2 data2  id data1  id_2 data1_2
0    1    2     e   1     a     2       b
1    1    3     f   1     a     3       c
2    1    4     g   1     a     4       d
3    2    3     h   2     b     3       c
4    2    4     i   2     b     4       d
5    3    4     j   3     c     4       d