Merge rows in spark scala Dataframe and apply aggregate function

I have a following Dataframe:

| notification_id|       el1|       el2|is_deleted| 
+---------------+----------+----------+----------+ 
|notificationId1|element1_1|element1_2|     false| 
|notificationId2|element2_1|element2_2|     false| 
|notificationId3|element3_1|element3_2|     false| 
|notificationId1|      null|      null|      true| 
|notificationId4|      null|      null|      true| 
+---------------+----------+----------+----------+

The primary key in this example is notification_id.

• The rows that have is_deleted = true, always have null values for other column except primary key.
• The rows with is_deleted = false have a unique primary key.

I would like to merge the rows with the same primary key in order to obtain dataframe with merged is_delete column:

| notification_id|       el1|       el2|is_deleted| 
+---------------+----------+----------+----------+ 
|notificationId1|element1_1|element1_2|      true| 
|notificationId2|element2_1|element2_2|     false| 
|notificationId3|element3_1|element3_2|     false| 
|notificationId4|      null|      null|      true| 
+---------------+----------+----------+----------+

You can group by the primary key and use an any() aggregator on the is_deleted column, which will yield true if any of the rows with the same primary key have a true value for is_deleted:

val df_result = df_in.groupBy("notification_id").agg(
  first("el1", ignoreNulls = true).alias("el1"),
  first("el2", ignoreNulls = true).alias("el2"),
  expr("any(is_deleted)").alias("is_deleted")
)