aggregate

Aggregates this Pivot using the provided statistics inside the AggregateDsl.

Returns a new DataRow with the original pivot keys as top-level columns on top level and the corresponding aggregated values in new nested columns.

AggregateDsl allows you to compute statistics on the columns within groups in Pivot and store the results as a new column using into. The given aggregating expression is applied to each group independently.

The resulting DataRow has the same structure as the original Pivot; instead of the groups, there are new columns of aggregated values created with into.

You can use any of DataFrame Aggregation Statistics or any custom aggregation function.

Aggregated values can be either simple values, data rows or even data frames. Including them in the result using into will lead to creating a value column, a column group, or a frame column respectively in the resulting DataRow while preserving the original structure at higher levels.

Check out pivot Grammar for more information.

For more information: See pivot on the documentation website.

Example

df.pivot { city }.aggregate {
  // Сount rows within each pivot group and store the result
  // into a new "total" column (a new sub-column under each key column)
  count() into "total"

  // Compute the maximum in "age" column within each group
  // and store it into a new "maxAge" column
  max { age } into "maxAge"
}

Parameters

Aggregates this GroupBy using the provided statistics inside the AggregateGroupedDsl.

Returns a new DataFrame with the original groupBy key columns and the corresponding aggregated values in new columns.

AggregateGroupedDsl allows you to compute statistics on the columns within groups in GroupBy and store the results as a new column using into. The given aggregating expression is applied to each group independently.

The resulting DataFrame has the same structure as the original GroupBy; instead of the groups, there are new columns of aggregated values created with into.

You can use any of DataFrame Aggregation Statistics or any custom aggregation function.

Aggregated values can be either simple values, data rows or even data frames. Including them in the result using into will lead to creating a value column, a column group, or a frame column respectively in the resulting DataFrame while preserving the original structure at higher levels.

It allows pivoting inside aggregate via corresponding methods:

• pivot

• pivotCounts

• pivotMatches

Pivoting inside aggregate is useful for counting cross-group matrix-like statistics.

Check out groupBy Grammar for more information.

For more information: See groupBy on the documentation website.

Examples

df.groupBy { city }.aggregate {
  // Сount rows within each group and store the result
  // into a new "total" column
  count() into "total"

  // Compute the maximum in "age" column within each group
  // and store it into a new "maxAge" column
  max { age } into "maxAge"
}

df.groupBy { name.firstName }.aggregate {
    // Pivot the "city" column within each group,
    // creating a PivotGroupBy with "name" as grouping keys
    // and "city" as pivoted columns
    pivot { city }.aggregate {
        // Aggregate the mean of "age" column values for each
        // "firstName" × "city" combination group into the "meanAge" column
        mean { age } into "meanAge"

        // Aggregate the size of each `PivotGroupBy` group
        // into the "count" column
        count() into "count"
    }

    // Common `count` aggregation
    // into "total" column
    count() into "total"
}

Parameters