PySpark groupBy and aggregation functions with multiple columns

I work with a spark Dataframe and I try to create a new table with aggregation using groupby :
My data example :

and this is the desired result :

I tried this code data.groupBy("id1").agg(countDistinct("id2").alias("id2"), sum("value").alias("value"))

Anyone can help please ? Thank you

>Solution :

Try using below code –

from pyspark.sql.functions import *

df = spark.createDataFrame([('id11', 'id21', 1), ('id11', 'id22', 2), ('id11', 'id23', 3), ('id12', 'id21', 2), ('id12', 'id23', 1), ('id13', 'id23', 2), ('id13', 'id21', 8)], ["id1", "id2","value"])

Aggregated Data –

df.groupBy("id1").agg(count("id2"),sum("value")).show()

Output –

+----+----------+----------+
| id1|count(id2)|sum(value)|
+----+----------+----------+
|id11|         3|         6|
|id12|         2|         3|
|id13|         2|        10|
+----+----------+----------+