What is a field transformation intended to do?

A field transformation is primarily focused on converting existing data into a new format or representation, which often results in a new column with altered values derived from the original data. This process is essential for improving data usability, ensuring that the data aligns with the specific requirements of the analysis or model being developed. It can include various operations, such as aggregation, normalization, or even applying functions to modify the data's appearance or structure.

In this context, creating a new column with different values is a fundamental aspect of data preparation, allowing analysts to derive insights from the data more effectively. For instance, one might transform a numeric field into categories to simplify analysis or enable visualization techniques that require specific data formats.

To clarify the other options: changing data types from numeric to text relates to altering data formats but does not necessarily involve generating new values or insights. Combining multiple columns into one can be a component of data transformation but does not specifically highlight the notion of creating new values. Deleting unneeded columns focuses more on data cleaning than transformation itself, as it does not enhance or modify the data's representation. Thus, the correct understanding of field transformation aligns closely with the intent to generate a new column enriched with different values derived from existing data.