Pandas Two Dataframe Cross Join [Duplicate]

Question

I can't find anything about cross join include the merge/join or some other. I need deal with two dataframe using {my function} as myfunc . the equivalent of :

{
    for itemA in df1.iterrows():
           for itemB in df2.iterrows():
                       t["A"] = myfunc(itemA[1]["A"],itemB[1]["A"])
 }

the equivalent of :

{
 select myfunc(df1.A,df2.A),df1.A,df2.A from df1,df2;
}

but I need more efficient solution: if used apply i will be how to implement them thx;^^

Accepted Answer

Create a common 'key' to cross join the two:

df1['key'] = 0
df2['key'] = 0

df1.merge(df2, on='key', how='outer')

Accepted Answer

For the cross product, see this question.

Essentially, you have to do a normal merge but give every row the same key to join on, so that every row is joined to each other across the frames.

You can then add a column to the new frame by applying your function:

new_df = pd.merge(df1, df2, on=key)
new_df.new_col = new_df.apply(lambda row: myfunc(row['A_x'], row['A_y']), axis=1)

axis=1 forces .apply to work across the rows. 'A_x' and 'A_y' will be the default column names in the resulting frame if the merged frames share a column like in your example above.

Pandas Two Dataframe Cross Join [Duplicate]

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2 Answers

Sarah Jenkins