# pyspark.sql.DataFrame.coalesce¶

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.

Parameters
numPartitionsint

specify the target number of partitions

Examples

>>> df.coalesce(1).rdd.getNumPartitions()
1