How to Efficiently Calculate the Cartesian Product of DataFrames in Pandas?

Cartesian Product in Pandas

When working with two or more dataframes in Pandas, there may be a need to obtain their cartesian product, which results in a new dataframe with all combinations of rows from the input dataframes.

Pandas >= 1.2

In recent versions of Pandas, the merge function can be utilized to perform this operation with the how='cross' argument. This approach is both concise and efficient:

import pandas as pd

df1 = pd.DataFrame({'col1':[1,2],'col2':[3,4]})
df2 = pd.DataFrame({'col3':[5,6]})

df_cartesian = df1.merge(df2, how='cross')

Pandas < 1.2

For earlier versions of Pandas, a slightly different technique is required. It involves creating a key column that is repeated for each row in both dataframes. Once this key column is added, merge can be used to perform the cartesian product:

import pandas as pd
from pandas import merge

df1 = pd.DataFrame({'key':[1,1], 'col1':[1,2],'col2':[3,4]})
df2 = pd.DataFrame({'key':[1,1], 'col3':[5,6]})

merge(df1, df2,on='key')[['col1', 'col2', 'col3']]

This approach is more involved but works effectively in older versions of Pandas.

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