Pyspark crossjoining - pyspark

Question

Let's say, my dataframe has values
col1
|1|
|2|
|3|
So I want to create a new pyspark dataframe which have values
|1x1|
|1x2|
|1x3|
|2x1|
|2x2|
|2x3|
|3x1|
|3x2|
|3x3|
Can anyone explain me how to make this dataframe?
I am using crossJoin right now but it's showing error

Answer 1

try this :
from pyspark.sql import functions as F
df = spark.range(10)
df.show()
# +---+
# | id|
# +---+
# | 0|
# | 1|
# | 2|
# | 3|
# | 4|
# | 5|
# | 6|
# | 7|
# | 8|
# | 9|
# +---+
df_1 = df.alias("df1")
df_2 = df.alias("df2")
df_cross = df_1.crossJoin(df_2)
df_cross.show()
# +---+---+
# | id| id|
# +---+---+
# | 0| 0|
# | 0| 1|
# | 0| 2|
# | 0| 3|
# | 0| 4|
# | 1| 0|
# | 1| 1|
# | 1| 2|
# | 1| 3|
# | 1| 4|
# | 2| 0|
# | 2| 1|
# | 2| 2|
# | 2| 3|
# | 2| 4|
# | 3| 0|
# | 3| 1|
# | 3| 2|
# | 3| 3|
# | 3| 4|
# +---+---+
# only showing top 20 rows
df_cross = df_cross.withColumn(
"concat", F.concat_ws("x", F.col("df1.id"), F.col("df2.id"))
)
df_cross.show()
# +---+---+------+
# | id| id|concat|
# +---+---+------+
# | 0| 0| 0x0|
# | 0| 1| 0x1|
# | 0| 2| 0x2|
# | 0| 3| 0x3|
# | 0| 4| 0x4|
# | 1| 0| 1x0|
# | 1| 1| 1x1|
# | 1| 2| 1x2|
# | 1| 3| 1x3|
# | 1| 4| 1x4|
# | 2| 0| 2x0|
# | 2| 1| 2x1|
# | 2| 2| 2x2|
# | 2| 3| 2x3|
# | 2| 4| 2x4|
# | 3| 0| 3x0|
# | 3| 1| 3x1|
# | 3| 2| 3x2|
# | 3| 3| 3x3|
# | 3| 4| 3x4|
# +---+---+------+
# only showing top 20 rows