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How to perform CROSS JOIN with pandas dataframe?

Given a Pandas DataFrame, we have to perform CROSS JOIN with it. By Pranit Sharma Last updated : September 26, 2023

Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and data.

And since a DataFrame consists of rows and column, it is easy to understand and analyse the data or we can say that pandas provides a slippery way to learn and to deliver a product in the form of an analysed data set.

Cartesian Product

Cartesian product is a special product where each row value of one array is multiplied by each column of another array, but in pandas, each element from the row will be added to a new DataFrame column-wise.

Cross Join

Cross Join is similar to the cartesian product which is performed when we have data in form of rows and columns.

Problem statement

Given a Pandas DataFrame, we have to perform CROSS JOIN with it.

CROSS JOIN with pandas dataframe

We can perform cartesian product or Cross Join in pandas by using the dataframe.merge() method with a required parameter called how=cross. Call the merge() method with this dataframe (object of one dataframe) and pass the second dataframe along with this parameter.

Let us understand with the help of an example,

Python program to perform CROSS JOIN with pandas dataframe

# Importing pandas package
import pandas as pd

# Creating a dictionary
d= {'one':[10,20,30],'two':[40,50,60]}

d2 = {'three':[1,2,3]}

# Creating two Dataframes
df1 = pd.DataFrame(d)

df2 = pd.DataFrame(d2)

# Display DataFrames
print("DataFrame 1:\n",df1,"\n")

print("DataFrame 1:\n",df2,"\n")

# Cartesian product
result = df1.merge(df2, how='cross')

# Display result
print("Cartesian product:\n",result)

Output

The output of the above program is:

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