How to Perform a Cartesian Product of DataFrames in Pandas?

Cartesian Product in Pandas

In data manipulation tasks, it is often necessary to combine rows from multiple dataframes into a single dataframe. One way to achieve this is by performing a cartesian product, which generates all possible combinations of rows from the input dataframes.

For Pandas versions >= 1.2, the merge function provides a built-in method for cartesian product calculations. The following code demonstrates its usage:

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

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

Output:

   col1  col2  col3
0     1     3     5
1     1     3     6
2     2     4     5
3     2     4     6

For Pandas versions < 1.2, an alternative approach using the merge function is available. In this method, a common key is added to each dataframe to facilitate the join:

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

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

Output:

   col1  col2  col3
0     1     3     5
1     1     3     6
2     2     4     5
3     2     4     6

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