DataFrame.coalesce(numPartitions: int) → pyspark.sql.dataframe.DataFrame[source]

Returns a new DataFrame that has exactly numPartitions partitions.

Similar to coalesce defined on an RDD, this operation results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim 10 of the current partitions. If a larger number of partitions is requested, it will stay at the current number of partitions.

However, if you’re doing a drastic coalesce, e.g. to numPartitions = 1, this may result in your computation taking place on fewer nodes than you like (e.g. one node in the case of numPartitions = 1). To avoid this, you can call repartition(). This will add a shuffle step, but means the current upstream partitions will be executed in parallel (per whatever the current partitioning is).

New in version 1.4.0.

Changed in version 3.4.0: Supports Spark Connect.


specify the target number of partitions



>>> df = spark.range(10)
>>> df.coalesce(1).rdd.getNumPartitions()