4. Join without Duplicate Columns on Result Ween you join, the resultant frame contains all columns from both DataFrames. since we have dept_id and branch_id on both we will end up with duplicate columns. To get a join result with out duplicate you have to use # Join without duplicate ...
A Left Semi Join in PySpark returns only the rows from the left DataFrame (the first DataFrame mentioned in the join operation) where there is a match with the right DataFrame (the second DataFrame). It does not include any columns from the right DataFrame in the resulting DataFrame. This j...
I can also join by conditions, but it creates duplicate column names if the keys have the same name, which is frustrating. For now, the only way I know to avoid this is to pass a list of join keys as in the previous cell. If I want to make nonequi joins, then I need to rename...
join(probe_test_df.rdd .map(lambda row: (row['id'], float(row['probability'][1]))) .toDF(['id', probe_prob_col]), 'id') .cache()) print(res_test_df.count()) print(time() - t0) 22544 6.297783136367798 The first report shows performance of classification for 'normal' and '...
>>> df.join(df2,'name','inner').drop('age','height').collect()[Row(name=u'Bob')] New in version 1.4. dropDuplicates(subset=None)[source] Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. ...