Aggregation

Pivot aggregation

Each Pivot group can be aggregated — that is, transformed into a new value, DataRow, or DataFrame — and then concatenated into a single DataRow composed of these aggregated results, with pivot keys as top-level columns or as column groups.

The following aggregation methods are available:

• frames — returns this Pivot as a DataRow with pivot keys as columns (or column groups) and corresponding groups stored as FrameColumns;

• values — creates a DataRow containing values collected into a single List from all rows of each group for the selected columns (values from column groups are collected into a DataFrame);

• count — creates a DataRow containing the pivot key columns and an additional column with the number of rows in each corresponding group;

• with — creates a DataRow containing values computed using a RowExpression across all rows of each group. Values of the DataRow type are collected into a DataFrame, and the resulting column is a FrameColumn. Values of other types are collected into a List, and the resulting column is a DataColumn of List;

• aggregate — performs a set of custom aggregations using AggregateDsl, allowing computation of one or more derived values per group;

• Various aggregation statistics — predefined shortcuts for common statistical aggregations such as sum, mean, median, and others.

Each of these methods returns a new DataRow with pivot keys as top-level columns (or as column groups) and values representing the aggregated results of each group.

Check out Pivot grammar.

For more information: See "Pivot Aggregation" on the documentation website.