- Converting Dataframes

Converting dataframes in PySpark based on application requirements.

Spark-Pandas conversion #

While it’s best to work with Spark dataframes when authoring PySpark workloads, it’s often necessary to translate between different formats based on your use case. For example, you might need to perform a Pandas operation, such as selecting a specific element from a dataframe. When this is required, you can use the toPandas function to pull a Spark dataframe into memory on the driver node. The PySpark snippet below shows how to perform this task, display the results, and then convert the Pandas dataframe back to a Spark dataframe.

In general, it’s best to avoid Pandas when authoring PySpark workflows, because it prevents distribution and scale, but it’s often the best way of expressing a command to execute.

Get hands-on with 1200+ tech skills courses.