combine text from multiple rows in pyspark - pyspark

Question

I created a PySpark dataframe using the following code
testlist = [
{"category":"A","name":"A1"},
{"category":"A","name":"A2"},
{"category":"B","name":"B1"},
{"category":"B","name":"B2"}
]
spark_df = spark.createDataFrame(testlist)
Result:
category name
A A1
A A2
B B1
B B2
I want to make it appear as follows:
category name
A A1, A2
B B1, B2
I tried the following code which does not work
spark_df.groupby('category').agg('name', lambda x:x + ', ')
Can anyone help identify what I am doing wrong and the best way to make this happen ?

Answer 1

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 |
#+---------+-------+

Answer 2

UNIQUE values
If you want unique values then use collect_set instead of collect_list
from pyspark.sql.functions import collect_set
grouped_df = sdf.groupby('category').agg(collect_set('name').alias("unique_name"))
sdf.show(5)

Answer 3

Another option is this
>>> df.rdd.reduceByKey(lambda x,y: x+','+y).toDF().show()
+---+-----+
| _1| _2|
+---+-----+
| A|A1,A2|
| B|B1,B2|
+---+-----+