python - Joining multiple columns in PySpark -

i join 2 dataframes have column names in common.

my dataframes follows:

>>> sample3 dataframe[uid1: string, count1: bigint] >>> sample4 dataframe[uid1: string, count1: bigint]   sample3      uid1  count1 0  john         3 1  paul         4 2  george       5  sample4      uid1  count1 0  john         3 1  paul         4 2  george       5 

(i using same dataframe different name on purpose)

i looked @ jira issue 7197 spark , address how perform join (this inconsistent pyspark documentation). however, method propose produces duplicate columns:

>>> cond = (sample3.uid1 == sample4.uid1) & (sample3.count1 == sample4.count1) >>> sample3.join(sample4, cond) dataframe[uid1: string, count1: bigint, uid1: string, count1: bigint] 

i result keys not appear twice.

i can 1 column:

>>>sample3.join(sample4, 'uid1') dataframe[uid1: string, count1: bigint, count1: bigint] 

however, same syntax not apply method of joining , throws error.

i result:

dataframe[uid1: string, count1: bigint] 

i wondering how possible

you can define join cond use list of keys, in case:

sample3.join(sample4, ['uid1','count1'])