This process involves transforming a dataset by first optionally organizing its rows based on certain criteria. It then identifies unique values or combinations thereof within specified columns, creating groups based on these unique identifiers. For each group, specific functions are applied to the rows to summarize or condense their information. The result is a new dataset where each row represents a unique group, and each column corresponds to the outcome of a distinct summarization function applied across the grouped data. For more information, refer to the pandas melt documentation.

Usage

The following example shows how the step can be used in a recipe.

Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").

Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).