Renaming Columns in PySpark: withColumnRenamed vs toDF

If you’ve worked with PySpark DataFrames, you’ve probably had to rename columns. Either using withColumnRenamed repeatedly or toDF(). At first glance, both approaches work the same; you get the renamed columns you wanted. But under the hood, they interact with Spark’s Directed Acyclic Graph (DAG) in very different ways. withColumnRenamed creates a new projection layer for each rename, gradually stacking transformations in the logical plan.  toDF(), on the other hand, applies all renames in a single step.  While both are optimized to the same physical execution, their impact on the DAG size, planning overhead, and code readability can make a real difference in larger pipelines.