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I try to find way to apply cross join on pandas. Failed to find useful suggestion in pandas document.
Like SELECT * FROM [GameScores] CROSS JOIN [Departments] or SELECT * FROM [GameScores] JOIN [Departments] on [GameScores].t1 != [Departments].t1
I get one possible answer like: import itertools list=[x for x in itertools.product([a,b,c,d],[1,2,3,4])] df=pd.DataFrame(list)
But I'm wondering without numpy tools it might be cost much time while apply this on two large dataframe or series.
Is there any tools in pandas for this two operations?
The text was updated successfully, but these errors were encountered:
I try to find way to apply cross join on pandas. Failed to find useful suggestion in pandas document.
Like
SELECT * FROM [GameScores] CROSS JOIN [Departments] or SELECT * FROM [GameScores] JOIN [Departments] on [GameScores].t1 != [Departments].t1
I get one possible answer like:
import itertools list=[x for x in itertools.product([a,b,c,d],[1,2,3,4])] df=pd.DataFrame(list)
But I'm wondering without numpy tools it might be cost much time while apply this on two large dataframe or series.
Is there any tools in pandas for this two operations?
The text was updated successfully, but these errors were encountered: