Explanation
itemsDf.withColumnRenamed("attributes", "feature0").withColumnRenamed("supplier", "feature1") Correct! Spark's DataFrame.withColumnRenamed syntax makes it relatively easy to change the name of a column.
itemsDf.withColumnRenamed(attributes, feature0).withColumnRenamed(supplier, feature1) Incorrect. In this code block, the Python interpreter will try to use attributes and the other column names as variables. Needless to say, they are undefined, and as a result the block will not run.
itemsDf.withColumnRenamed(col("attributes"), col("feature0"), col("supplier"), col("feature1")) Wrong. The DataFrame.withColumnRenamed() operator takes exactly two string arguments. So, in this answer both using col() and using four arguments is wrong.
itemsDf.withColumnRenamed("attributes", "feature0")
itemsDf.withColumnRenamed("supplier", "feature1")
No. In this answer, the returned DataFrame will only have column supplier be renamed, since the result of the first line is not written back to itemsDf.
itemsDf.withColumn("attributes", "feature0").withColumn("supplier", "feature1") Incorrect. While withColumn works for adding and naming new columns, you cannot use it to rename existing columns.
More info: pyspark.sql.DataFrame.withColumnRenamed - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3