Join two data frames, select all columns from one and some columns from the other

Solution 1:

Asterisk (*) works with alias. Ex:

from pyspark.sql.functions import *

df1 = df1.alias('df1')
df2 = df2.alias('df2')

df1.join(df2, df1.id == df2.id).select('df1.*')

Solution 2:

Not sure if the most efficient way, but this worked for me:

from pyspark.sql.functions import col

df1.alias('a').join(df2.alias('b'),col('b.id') == col('a.id')).select([col('a.'+xx) for xx in a.columns] + [col('b.other1'),col('b.other2')])

The trick is in:

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

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

Solution 3:

Without using alias.

df1.join(df2, df1.id == df2.id).select(df1["*"],df2["other"])

Solution 4:

Here is a solution that does not require a SQL context, but maintains the metadata of a DataFrame.

a = sc.parallelize([['a', 'foo'], ['b', 'hem'], ['c', 'haw']]).toDF(['a_id', 'extra'])
b = sc.parallelize([['p1', 'a'], ['p2', 'b'], ['p3', 'c']]).toDF(["other", "b_id"])

c = a.join(b, a.a_id == b.b_id)

Then, c.show() yields:

+----+-----+-----+----+
|a_id|extra|other|b_id|
+----+-----+-----+----+
|   a|  foo|   p1|   a|
|   b|  hem|   p2|   b|
|   c|  haw|   p3|   c|
+----+-----+-----+----+