BulkColumnRename

ProphecySparkBasicsPython0.2.11+ProphecySparkBasicsScala0.1.9+ProphecyLibsPython1.8.19ProphecyLibsScala7.1.97Databricks UC Single Cluster14.3+Databricks UC SharedNot SupportedLivy3.0.1+

Use the BulkColumnRename gem to rename multiple columns in your dataset in a systematic way.

Parameters

Parameter	Description
Columns to rename	Select one or more columns to rename from the dropdown.
Method	Choose to add a prefix, add a suffix, or use a custom expression to change column names.

Based on the method you select, you will see an option to enter the prefix, suffix, or expression of your choice.

Examples

Add a prefix

One example is to add the prefix meta_ to tag columns that contain metadata.

Use a custom expression

You can accomplish the same or more complex changes using a custom expression like concat('meta_', column_name).

Example code

tip

To see the compiled code of your project, switch to the Code view in the project header.

Python

def bulk_rename_customer_id(spark: SparkSession, in0: DataFrame) -> DataFrame:
    from prophecy.utils.transpiler.dataframe_fcns import evaluate_expression

    return evaluate_expression(
        in0,
        userExpression = "concat('int_', column_name)",
        selectedColumnNames = ["customer_id"],
        sparkSession = spark
    )

Scala

object bulk_column_rename {
  def apply(context: Context, in: DataFrame): DataFrame = {
    import org.apache.spark.sql.ProphecyDataFrame
    ProphecyDataFrame
      .extendedDataFrame(in)
      .evaluate_expression("concat('c_',column_name)",
                           List(" code"),
                           context.spark
      )
  }
}