combine text from multiple rows in pyspark
One option is to use pyspark.sql.functions.collect_list()
as the aggregate function.
from pyspark.sql.functions import collect_list
grouped_df = spark_df.groupby('category').agg(collect_list('name').alias("name"))
This will collect the values for name
into a list and the resultant output will look like:
grouped_df.show()
#+---------+---------+
#|category |name |
#+---------+---------+
#|A |[A1, A2] |
#|B |[B1, B2] |
#+---------+---------+
Update 2019-06-10:
If you wanted your output as a concatenated string, you can use pyspark.sql.functions.concat_ws
to concatenate the values of the collected list, which will be better than using a udf
:
from pyspark.sql.functions import concat_ws
grouped_df.withColumn("name", concat_ws(", ", "name")).show()
#+---------+-------+
#|category |name |
#+---------+-------+
#|A |A1, A2 |
#|B |B1, B2 |
#+---------+-------+
Original Answer: If you wanted your output as a concatenated string, you'd have to can use a udf
. For example, you can first do the groupBy()
as above and the apply a udf
to join the collected list:
from pyspark.sql.functions import udf
concat_list = udf(lambda lst: ", ".join(lst), StringType())
grouped_df.withColumn("name", concat_list("name")).show()
#+---------+-------+
#|category |name |
#+---------+-------+
#|A |A1, A2 |
#|B |B1, B2 |
#+---------+-------+