How to get required dataframe after pyspark pivot?

I have a spark dataframe:

+------+----------------+-------+------+---------+
|name  |           pDate| status|user  |userCount|
+------+----------------+-------+------+---------+
|janani|2022-03-04      |active |qwe123|1        |
|raj   |2022-03-03      |active |qwe123|1        |
|ram   |2022-03-01      |active |qwe123|1        |
|ram   |2022-03-02      |active |qwe123|1        |
+------+----------------+-------+------+---------+

when I pivoted the dataframe with the following code

pvtcountuser = countuser.groupby('pDate','name').pivot('pDate').max('userCount').fillna(0)

I get:

+----------------+------+----------+----------+----------+----------+
|pDate           |name  |2022-03-01|2022-03-02|2022-03-03|2022-03-04|
+----------------+------+----------+----------+----------+----------+
|2022-03-04      |janani|0         |0         |0         |1         |
|2022-03-03      |raj   |0         |0         |1         |0         |
|2022-03-01      |ram   |1         |0         |0         |0         |
|2022-03-02      |ram   |0         |1         |0         |0         |
+----------------+------+----------+----------+----------+----------+

but the required solution needed is:

+----------------+------+----------+----------+----------+----------+
|pDate           |name  |2022-03-01|2022-03-02|2022-03-03|2022-03-04|
+----------------+------+----------+----------+----------+----------+
|2022-03-04      |janani|0         |0         |0         |1         |
|2022-03-03      |raj   |0         |0         |1         |0         |
|2022-03-01      |ram   |1         |1         |0         |0         |
+----------------+------+----------+----------+----------+----------+

Please help on this

>Solution :

Since you want a record per name, the group by should be done only on name

sql_stmt = '''
select *
from   values    ('janani',date '2022-03-04','active','qwe123',1)
                ,('raj'   ,date '2022-03-03','active','qwe123',1)
                ,('ram'   ,date '2022-03-01','active','qwe123',1)
                ,('ram'   ,date '2022-03-02','active','qwe123',1) as t (name,pDate,status,user,userCount)
'''

countuser = spark.sql(sql_stmt)

pvtcountuser = countuser.groupby('name').pivot('pDate').max('userCount').fillna(0)
pvtcountuser.show()

+------+----------+----------+----------+----------+
|  name|2022-03-01|2022-03-02|2022-03-03|2022-03-04|
+------+----------+----------+----------+----------+
|   raj|         0|         0|         1|         0|
|   ram|         1|         1|         0|         0|
|janani|         0|         0|         0|         1|
+------+----------+----------+----------+----------+