Python Pandas – Merge and create cartesian product from both the DataFrames

To merge Pandas DataFrames and create a cartesian product, use the merge() function with the how="cross" parameter. A cartesian product combines every row from the first DataFrame with every row from the second DataFrame.

Syntax

pd.merge(df1, df2, how="cross")

Creating Sample DataFrames

Let's create two DataFrames to demonstrate the cartesian product ?

import pandas as pd

# Create DataFrame1
dataFrame1 = pd.DataFrame({
    "Car": ['BMW', 'Mustang', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 120]
})

print("DataFrame1:")
print(dataFrame1)

# Create DataFrame2
dataFrame2 = pd.DataFrame({
    "Car": ['BMW', 'Tesla', 'Jaguar'],
    "Reg_Price": [7000, 8000, 9000]
})

print("\nDataFrame2:")
print(dataFrame2)

DataFrame1:
       Car  Units
0      BMW    100
1  Mustang    150
2  Bentley    110
3   Jaguar    120

DataFrame2:
     Car  Reg_Price
0    BMW       7000
1  Tesla       8000
2  Jaguar       9000

Creating Cartesian Product

Now merge the DataFrames using how="cross" to create the cartesian product ?

import pandas as pd

# Create DataFrames
dataFrame1 = pd.DataFrame({
    "Car": ['BMW', 'Mustang', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 120]
})

dataFrame2 = pd.DataFrame({
    "Car": ['BMW', 'Tesla', 'Jaguar'],
    "Reg_Price": [7000, 8000, 9000]
})

# Merge DataFrames with cartesian product
mergedRes = pd.merge(dataFrame1, dataFrame2, how="cross")
print("Merged DataFrame with cartesian product:")
print(mergedRes)

Merged DataFrame with cartesian product:
       Car_x  Units  Car_y  Reg_Price
0        BMW    100    BMW       7000
1        BMW    100  Tesla       8000
2        BMW    100 Jaguar       9000
3    Mustang    150    BMW       7000
4    Mustang    150  Tesla       8000
5    Mustang    150 Jaguar       9000
6    Bentley    110    BMW       7000
7    Bentley    110  Tesla       8000
8    Bentley    110 Jaguar       9000
9     Jaguar    120    BMW       7000
10    Jaguar    120  Tesla       8000
11    Jaguar    120 Jaguar       9000

How It Works

The cartesian product creates all possible combinations:

• DataFrame1 has 4 rows, DataFrame2 has 3 rows
• Result has 4 × 3 = 12 rows
• Columns with same names get suffixes _x and _y
• Every row from df1 is paired with every row from df2

Conclusion

Use pd.merge(df1, df2, how="cross") to create a cartesian product of two DataFrames. This combines every row from the first DataFrame with every row from the second DataFrame, resulting in n×m rows where n and m are the row counts of the input DataFrames.