Which of the following code blocks returns a new DataFrame where column division from DataFrame storesDF has been replaced and renamed to column state and column managerName from DataFrame storesDF…

Question

Options

• AstoresDF.withColumnRenamed("division", "state")
• BstoresDF.withColumn("state", "division")
• CstoresDF.withColumn("state", col("division"))
• DstoresDF.withColumnRenamed(Seq("division", "state"), Seq("managerName",
• EstoresDF.withColumnRenamed("state", "division")

• A
  90% (52)
• B
  2% (1)
• C
  2% (1)
• D
  5% (3)
• E
  2% (1)

Explanation

To rename columns in PySpark, use withColumnRenamed(existingName, newName). The task requires two renames: 'division' → 'state' and 'managerName' → 'managerFullName'. Option A chains two withColumnRenamed calls: storesDF.withColumnRenamed('division', 'state').withColumnRenamed('managerName', 'managerFullName'). Option B and C use withColumn with a string or Column, which adds/replaces column values but doesn't rename or remove the original column. Option D passes Seq() which is Scala syntax, not valid PySpark. Option E has the argument order reversed (new name before old name).

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