Pandas DataFrames - Inner Join

Concat Pandas DataFrames with Inner Join

You can inner join two DataFrames during concatenation which results in the intersection of the two DataFrames.

Syntax

The syntax of concat() function to inner join is given below.

pd.concat([df1, df2], axis=1, join='inner')

Inner join results in a DataFrame that has intersection along the given axis to the concatenate function.

Examples

1. Inner join DataFrames along an axis

In this example, we shall take two DataFrames and find their inner join along axis=1.

Python Program

import pandas as pd

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']},
                   index=[0, 1, 2, 3])

df2 = pd.DataFrame({'B': ['B2', 'B3', 'B4', 'B5'],
                    'D': ['D2', 'D3', 'D4', 'D5'],
                    'F': ['F2', 'F3', 'F4', 'F5']},
                   index=[2, 3, 4, 5])

result = pd.concat([df1, df2], axis=1, join='inner')

print(result)

Output

D:\>python example.py
    A   B   C   D   B   D   F
2  A2  B2  C2  D2  B2  D2  F2
3  A3  B3  C3  D3  B3  D3  F3

2. Inner join DataFrames along axis=0

In this example, we shall try the inner join of DataFrames along a different axis from that of the previous example.

Python Program

import pandas as pd

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']},
                   index=[0, 1, 2, 3])

df2 = pd.DataFrame({'B': ['B2', 'B3', 'B4', 'B5'],
                    'D': ['D2', 'D3', 'D4', 'D5'],
                    'F': ['F2', 'F3', 'F4', 'F5']},
                   index=[2, 3, 4, 5])

result = pd.concat([df1, df2], axis=0, join='inner')

print(result)

Output

D:\>python example.py
    B   D
0  B0  D0
1  B1  D1
2  B2  D2
3  B3  D3
2  B2  D2
3  B3  D3
4  B4  D4
5  B5  D5

3. Inner join more than two DataFrames using concat()

In this example, we shall take more than two DataFrames, which is three, and find the inner join of these DataFrames along an axis.

Python Program

import pandas as pd

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']},
                   index=[0, 1, 2, 3])

df2 = pd.DataFrame({'B': ['B2', 'B3', 'B4', 'B5'],
                    'D': ['D2', 'D3', 'D4', 'D5'],
                    'F': ['F2', 'F3', 'F4', 'F5']},
                   index=[2, 3, 4, 5])

df3 = pd.DataFrame({'D': ['D3', 'D4', 'D5'],
                    'G': ['G3', 'G4', 'G5']},
                   index=[3, 4, 5])

result = pd.concat([df1, df2, df3], axis=1, join='inner')

print(result)

Output

D:\>python example.py
    A   B   C   D   B   D   F   D   G
3  A3  B3  C3  D3  B3  D3  F3  D3  G3

Summary

In this tutorial of Python Examples, we learned how to inner join two or more DataFrames using concatenate function along an axis.