import pandas as pd

df1 = pd.DataFrame({
    'key': ['a', 'b', 'c', 'd'],
    'value': [1, 2, 3, 4]
})

df2 = pd.DataFrame({
    'key_column': ['a', 'b', 'd', 'e'],
    'value': [5, 6, 7, 8]
})

result = pd.merge(df1, df2, left_on='key', right_on='key_column')
print(result)

  key  value key_column  value
0   a      1          a      5
1   b      2          b      6
2   d      4          d      7

import pandas as pd

df1 = pd.DataFrame({
    'A1': [1, 2, 3, 4],
    'A2': [5, 6, 7, 8]
})

df2 = pd.DataFrame({
    'B1': [9, 10, 11, 12],
    'B2': [13, 14, 15, 16]
})

df1.columns = df2.columns.str.replace('B', 'A')
result = pd.concat([df1, df2], axis=1)
print(result)

   A1  A2  A3  B1  B2  B3
0   1   5   2   9  13  10
1   2   6   3  10  14  11
2   3   7   4  11  15  12
3   4   8   5  12  16  13

import pandas as pd

left = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8]
}, index=['one', 'two', 'three', 'four'])

right = pd.DataFrame({
    'C': [9, 10, 11, 12],
    'D': [13, 14, 15, 16]
}, index=['two', 'four', 'five', 'six'])

result = left.join(right)
print(result)

       A  B     C     D
one    1  5   NaN   NaN
two    2  6   9.0  13.0
three  3  7   NaN   NaN
four   4  8  10.0  14.0