The following examples show how the step can be used in a recipe.
This example groups the dataset by an exact match on the category column and a date component (month level) on the date column, and then aggregates the count of sales and the sum of revenue:
This example groups the dataset by an exact match on the category column and a date component (month level) on the date column, and then aggregates the count of sales and the sum of revenue:
This example uses the simplified by parameter to group by an exact match on category. The aggregation calculates the average of revenue for each group:
General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.
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").
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).
Type of grouping operation.
The type of grouping operation. You can group by exact value match, a date component,
a range of numerical values, or quantiles.
Range size as number for RANGE grouping or number of quantiles.
Specify a range size directly as a number when groupingType is RANGE
or number of quantiles when groupingType is QUANTILES.
Aggregation functions to apply.
An array specifying the aggregation functions to apply on each group.
The array can be empty, in which case no aggregations are performed, but the dataset is still grouped by the specified columns.
Type of aggregation function.
The type of aggregation function to perform on the specified column.
Includes support for standard aggregations (e.g., SUM, COUNT) as well as element-wise aggregations.
