Reformat

Transforms one or more column names or values by using expressions and/or functions. It's useful when we need to extract only the required columns or make changes column-wise.

Parameters

Parameter	Description	Required
DataFrame	Input DataFrame on which changes are required	True
Target column	Output column name	False
Expression	Expression to compute target column	Required if a Target column is present

info

If no columns are selected, then all columns are passed through to the output

Example

Spark Code

Reformat converts to a SQL Select or in relational terms into a projection, unlike SchemaTransform Gem which uses underlying withColumn construct

Python

def Reformat(spark: SparkSession, in0: DataFrame) -> DataFrame:
    return in0.select(
        col("id"),
        col("email").alias("email_address"),
        col("name"),
        col("updated_at"),
        concat_ws("$$$", col("address_line1"), col("address_line2"), col("postal_code"))
            .alias("address_string")
    )

Scala

object Reformat {

  def apply(spark: SparkSession, in: DataFrame): DataFrame =
    in.select(
      col("id"),
      col("email").as("email_address"),
      col("name"),
      col("updated_at"),
      expr("concat_ws('$$$', address_line1, address_line2, postal_code)").as("address_string")
    )
 }

Advanced Import

The Advanced Import feature allows you to bulk import statements that are structured similarly to CSV/TSV files. This can be useful if you have your expressions/transformation logic in another format and just want to quickly configure a Reformat Gem based on this logic.

Using Advanced Import

1. Click the Advanced button in the `Reformat UI

2. Enter the expressions into the text area using the format as described below:

3. Use the button at the top (labeled Expressions) to switch back to the expressions view. This will translate the expressions from the CSV format to the table format and will show any errors detected.

Format

The format of these expressions is target_name,target_expr, where target_name is the desired new column name and target_expr is the Spark expression that will be used to generate the new column.

caution

For target_expr values that contain a comma , or span multiple lines, you must surround them by `` on either side. For example:

customer_id,customer_id
full_name,``concat(first_name, ' ', last_name)``