Python – Merge two Pandas DataFrame

To merge two Pandas DataFrames, use the merge() function. By default, it performs an inner join on common columns between the DataFrames.

Basic Syntax

pd.merge(left_df, right_df, on='column_name', how='inner')

Creating Sample DataFrames

First, let's create two DataFrames with a common column ?

import pandas as pd

# Create DataFrame1
dataFrame1 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90]
})

print("DataFrame1:")
print(dataFrame1)

DataFrame1:
       Car  Units
0      BMW    100
1    Lexus    150
2     Audi    110
3  Mustang     80
4  Bentley    110
5   Jaguar     90

import pandas as pd

# Create DataFrame2
dataFrame2 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Mercedes', 'Jaguar'],
    "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000]
})

print("DataFrame2:")
print(dataFrame2)

DataFrame2:
       Car  Reg_Price
0      BMW       7000
1    Lexus       1500
2     Audi       5000
3  Mustang       8000
4 Mercedes       9000
5   Jaguar       6000

Merging DataFrames

Now merge both DataFrames using the merge() function. By default, it performs an inner join on common columns ?

import pandas as pd

# Create DataFrames
dataFrame1 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90]
})

dataFrame2 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Mercedes', 'Jaguar'],
    "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000]
})

# Merge DataFrames
mergedRes = pd.merge(dataFrame1, dataFrame2)
print("Merged DataFrame:")
print(mergedRes)

Merged DataFrame:
       Car  Units  Reg_Price
0      BMW    100       7000
1    Lexus    150       1500
2     Audi    110       5000
3  Mustang     80       8000
4   Jaguar     90       6000

Merge Types

You can specify different merge types using the how parameter ?

import pandas as pd

dataFrame1 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley'],
    "Units": [100, 150, 110, 80, 110]
})

dataFrame2 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Mercedes'],
    "Price": [7000, 1500, 5000, 12000, 9000]
})

# Left join - keeps all rows from left DataFrame
left_merge = pd.merge(dataFrame1, dataFrame2, how='left')
print("Left Merge:")
print(left_merge)

Left Merge:
       Car  Units   Price
0      BMW    100  7000.0
1    Lexus    150  1500.0
2     Audi    110  5000.0
3  Mustang     80     NaN
4  Bentley    110     NaN

Key Points

• Inner join (default): Returns only matching rows from both DataFrames
• Left join: Returns all rows from left DataFrame, matched rows from right
• Right join: Returns all rows from right DataFrame, matched rows from left
• Outer join: Returns all rows from both DataFrames

Conclusion

The merge() function combines DataFrames based on common columns. Use the how parameter to control the join type and on to specify merge columns explicitly.