Apply function to pandas row-row cross product

Question

I have two pandas DataFrames / Series containing one row each.

df1 = pd.DataFrame([1, 2, 3, 4])
df2 = pd.DataFrame(['one', 'two', 'three', 'four'])

I now want to get all possible combinations into an n*n matrix / DataFrame with values for all cross-products being the output from a custom function.

def my_function(x, y):
    return f"{x}:{y}"

This should therefore result in:

df = pd.DataFrame([['1:one', '2:one', '3:one', '4:one'],
                   ['1:two', '2:two', '3:two', '4:two'],
                   ['1:three', '2:three', '3:three', '4:three'],
                   ['1:four', '2:four', '3:four', '4:four']])

         0        1        2        3
0    1:one    2:one    3:one    4:one
1    1:two    2:two    3:two    4:two
2  1:three  2:three  3:three  4:three
3   1:four   2:four   3:four   4:four

While I can build my own matrix through itertools.product, this seems like a very inefficient way for larger datasets and I was wondering if there is a more pythonic way. Thank you in advance.

Answer 1

You also can use pd.DataFrame constructor with apply:

pd.DataFrame(index=df2.squeeze(), columns=df1.squeeze()).apply(lambda x: x.name.astype(str)+':'+x.index)

Output:

            1        2        3        4                                        
one      1:one    2:one    3:one    4:one
two      1:two    2:two    3:two    4:two
three  1:three  2:three  3:three  4:three
four    1:four   2:four   3:four   4:four

Explanation:

First, with pd.DataFrame constructor, first build and empty dataframe with index and columns defined from df2 and df1 respectively. Using pd.DataFrame.squeeze, we convert those single column dataframes into a pd.Series.

Next, using pd.DataFrame.apply, we can apply a lambda function which adds the strings from the column name with a colon and the dataframe index for each column of the dataframe.

This yeilds a new dataframe with indexing and desired values.