Join two DataFrames where the join key is different and only select some columns

Question

What I would like to do is:

Join two DataFrames A and B using their respective id columns a_id and b_id<

Answer 1

Your pseudocode is basically correct. This slightly modified version would work if the id column existed in both DataFrames:

A_B = A.join(B, on="id").select("A.*", "B.b1", "B.b2")

From the docs for pyspark.sql.DataFrame.join():

If on is a string or a list of strings indicating the name of the join column(s), the column(s) must exist on both sides, and this performs an equi-join.

Since the keys are different, you can just use withColumn() (or withColumnRenamed()) to created a column with the same name in both DataFrames:

A_B = A.withColumn("id", col("a_id")).join(B.withColumn("id", col("b_id")), on="id")\
    .select("A.*", "B.b1", "B.b2")

If your DataFrames have long complicated names, you could also use alias() to make things easier:

A_B = long_data_frame_name1.alias("A").withColumn("id", col("a_id"))\
    .join(long_data_frame_name2.alias("B").withColumn("id", col("b_id")), on="id")\
    .select("A.*", "B.b1", "B.b2")

Answer 2

I think the easier solution is just to join table A to table B with selected columns you want. here is a sample code to do this:

joined_tables = table_A.join(table_B.select('col1', 'col2', 'col3'), ['id'])

the code above join all columns from table_A and columns "col1", "col2", "col3" from table_B.

Answer 3

Try this solution:

A_B = A.join(B,col('B.id') == col('A.id')).select([col('A.'+xx) for xx in A.columns]
      + [col('B.other1'),col('B.other2')])

The below lines in SELECT played the trick of selecting all columns from A and 2 columns from Table B.

[col('a.'+xx) for xx in a.columns] : all columns in a

[col('b.other1'),col('b.other2')] : some columns of b