Transform

The Transform node is one of the most important nodes, allowing modification and/or remapping of source data before uploading it to some destination.

Anatomy

1. The name of a column from the source data.

2. The name of a column in the destination data.

3. The type of transformation to perform.

4. The arguments for a given transformation type.

   1. These aren't always required (e.g., one-to-one transformations).

5. Button to import mappings from a JSON file.

6. Button to export mappings as a JSON file.

7. Button that zooms and focuses the view on the node.

8. Button to add a new mapping.

9. Button to delete an existing mapping.

Transformation Types

One-to-One

The one-to-one transformation is very simple: the value of the source column is left untouched and merely mapped to the target column. No arguments are required.

Boolean

The boolean transformation normalizes the value of the source column into either true or false based on the list of defined "Truthy Values", which are formatted as a JSON list.

For example, if the Truthy Values are defined as ["yes", "true"] and the source column has the value no, the target column will have a value of false. Alternatively, if the source column has a value of yes or true, the target column will have a value of true.

While the Truthy Values are case sensitive, the source column value is always compared in lowercase.

Date

The date transformation converts a date from one format to another. It takes two arguments: the "From" date format and the "To" date format. There's a predefined list of common date formats available or, if those don't fit your requirements, you can define any date format using Python's strftime() and strptime() format codes.

For example, if I have the date 2026-01-01 and I want to convert it to the format MM-DD-YY, I'd select YYYY-MM-DD from the list of predefined date formats for the "From" argument, and I'd input %m-%d-%y for the "To" argument.

Python

The source column is unused when using the Python transformation.

The Python transformation executes custom Python code on an entire row, with the final value being written to the target column. There are two special variables available in scope: record_value and retvalue. To access the row, you use the record_value variable, which is a dictionary whose keys are the column names (e.g., record_value["column_1"]). To pass a value to the target column, you set retvalue to that value.

As an example:

count = int(record_value["count"])
product_name = record_value["product_name"]

if count > 1:
   retvalue = product_name + "s"
else:
   retvalue = product_name

Also, there's a restricted list of modules that can be imported. The following are currently available:

• collections

• datetime

• functools

• hashlib

• itertools

• json

• math

• operator

• random

• re

• string

• time

Value Mapping

The Value Mapping transformation, which takes a Value Mapping Set as it's only argument, attempts to match the source column value against a list Value Mapping "From" values and assigns the target column the associated "To" value, if there's a match. If there's no match, and no default value is set on the Value Mapping Set, the transformation will fail.

For more information on Value Mappings and Value Mapping Sets, check out the associated documentation.