Explanation
DataFrame.repartition(6)
Correct. repartition() always triggers a full shuffle (different from coalesce()).
DataFrame.repartition(12)
No, this would just leave the DataFrame with 12 partitions and not 6.
DataFrame.coalesce(6)
coalesce does not perform a full shuffle of the data. Whenever you see "full shuffle", you know that you are not dealing with coalesce(). While coalesce() can perform a partial shuffle when required, it will try to minimize shuffle operations, so the amount of data that is sent between executors.
Here, 12 partitions can easily be repartitioned to be 6 partitions simply by stitching every two partitions into one.
DataFrame.coalesce(6, shuffle=True) and DataFrame.coalesce(6).shuffle() These statements are not valid Spark API syntax.
More info: Spark Repartition & Coalesce - Explained and Repartition vs Coalesce in Apache Spark - Rock the JVM Blog