Examples

import numpy as np
import pandas as pd

df = pd.DataFrame({'key': ['K0', 'K2', 'K3', 'K4', 'K5', 'K6'],
                   'X': ['X0', 'X2', 'X3', 'X4', 'X5', 'X6']})
df

	key	X
0	K0	X0
1	K2	X2
2	K3	X3
3	K4	X4
4	K5	X5
5	K6	X6

other = pd.DataFrame({'key': ['K0', 'K2', 'K3'],
                      'Y': ['Y0', 'Y2', 'Y3']})
other

	key	Y
0	K0	Y0
1	K2	Y2
2	K3	Y3

Join DataFrames using their indexes.

df.join(other, lsuffix='_caller', rsuffix='_other')

	key_caller	X	key_other	Y
0	K0	X0	K0	Y0
1	K2	X2	K2	Y2
2	K3	X3	K3	Y3
3	K4	X4	NaN	NaN
4	K5	X5	NaN	NaN
5	K6	X6	NaN	NaN

If you want to join using the key columns, you need to set key to be the index in both df and other.
The joined DataFrame will have key as its index.

df.set_index('key').join(other.set_index('key'))

	X	Y
key
K0	X0	Y0
K2	X2	Y2
K3	X3	Y3
K4	X4	NaN
K5	X5	NaN
K6	X6	NaN

Another option to join using the key columns is to use the on parameter.
DataFrame.join always uses other’s index but we can use any column in df.
This method preserves the original DataFrame’s index in the result.

df.join(other.set_index('key'), on='key')

	key	X	Y
0	K0	X0	Y0
1	K2	X2	Y2
2	K3	X3	Y3
3	K4	X4	NaN
4	K5	X5	NaN
5	K6	X6	NaN