Often you may want to merge two pandas DataFrames on multiple columns. Fortunately this is easy to do using the pandas merge() function, which uses the following syntax:

pd.merge(df1, df2, left_on=['col1','col2'], right_on = ['col1','col2'])

This tutorial explains how to use this function in practice.

Example 1: Merge on Multiple Columns with Different Names

Suppose we have the following two pandas DataFrames:

import pandas as pd

#create and view first DataFrame
df1 = pd.DataFrame({'a1': [0, 0, 1, 1, 2],
                   'b': [0, 0, 1, 1, 1],
                   'c': [11, 8, 10, 6, 6]})

print(df1)

   a1  b   c
0   0  0  11
1   0  0   8
2   1  1  10
3   1  1   6
4   2  1   6

#create and view second DataFrame 
df2 = pd.DataFrame({'a2': [0, 1, 1, 1, 3],
                   'b': [0, 0, 0, 1, 1],
                   'd': [22, 24, 25, 33, 37]})

print(df2)

   a2  b   d
0   0  0  22
1   1  0  24
2   1  0  25
3   1  1  33
4   3  1  37

The following code shows how to perform a left join using multiple columns from both DataFrames:

pd.merge(df1, df2, how='left', left_on=['a1', 'b'], right_on = ['a2','b'])


        a1	b	c	a2	d
0	0	0	11	0.0	22.0
1	0	0	8	0.0	22.0
2	1	1	10	1.0	33.0
3	1	1	6	1.0	33.0
4	2	1	6	NaN	NaN

Example 2: Merge on Multiple Columns with Same Names

Suppose we have the following two pandas DataFrames with the same column names:

import pandas as pd

#create DataFrames
df1 = pd.DataFrame({'a': [0, 0, 1, 1, 2],
                   'b': [0, 0, 1, 1, 1],
                   'c': [11, 8, 10, 6, 6]})

df2 = pd.DataFrame({'a': [0, 1, 1, 1, 3],
                   'b': [0, 0, 0, 1, 1],
                   'd': [22, 24, 25, 33, 37]})

In this case we can simplify use on = [‘a’, ‘b’] since the column names are the same in both DataFrames:

pd.merge(df1, df2, how='left', on=['a', 'b'])

	a	b	c	d
0	0	0	11	22.0
1	0	0	8	22.0
2	1	1	10	33.0
3	1	1	6	33.0
4	2	1	6	NaN

How to Merge Two Pandas DataFrames on Index
How to Stack Multiple Pandas DataFrames