Modern Spark operates by a design choice to separate storage and compute. So saving a csv to the river's local disk doesn't make sense for a few reasons:

• the worker nodes don't have access to the driver's disk. They would need to send the data over to the driver, which is slow, burdensome, and could cause memory/IO issues.
• Spark is designed to write to Hadoop-inspired file systems, like DBFS, S3, Azure Blob/Gen2, etc. That way, the workers can write concurrently.

To do it your way, you could just collect the results to the driver, like using yourDF.toPandas() then save out the pandas data frame to local driver disk. Please note, if you take down the cluster you will lose anything on the local disk. Local disk should just be used as a tmp location, if at all.