df.pivot { city }.maxFor { age and weight }

Input DataFrame: rowsCount = 7, columnsCount = 5

name		age	city	weight	isHappy
firstName	lastName
Alice	Cooper	15	London	54	true
Bob	Dylan	45	Dubai	87	true
Charlie	Daniels	20	Moscow	null	false
Charlie	Chaplin	40	Milan	null	true
Bob	Marley	30	Tokyo	68	true
Alice	Wolf	20	null	55	false
Charlie	Byrd	30	Moscow	90	true

Step 1: Pivot

London	Dubai	Moscow	Milan	Tokyo	null
DataFrame 1 x 5	DataFrame 1 x 5	DataFrame 2 x 5	DataFrame 1 x 5	DataFrame 1 x 5	DataFrame 1 x 5

Output DataRow

London		Dubai		Moscow		Milan		Tokyo		null
age	weight	age	weight	age	weight	age	weight	age	weight	age	weight
15	54	45	87	30	90	40	null	30	68	20	55

df.groupBy { name }.pivot { city }.median { age }

Input DataFrame: rowsCount = 7, columnsCount = 5

name		age	city	weight	isHappy
firstName	lastName
Alice	Cooper	15	London	54	true
Bob	Dylan	45	Dubai	87	true
Charlie	Daniels	20	Moscow	null	false
Charlie	Chaplin	40	Milan	null	true
Bob	Marley	30	Tokyo	68	true
Alice	Wolf	20	null	55	false
Charlie	Byrd	30	Moscow	90	true

Step 1: GroupBy

name		group
firstName	lastName
Alice	Cooper	DataFrame 1 x 5
Bob	Dylan	DataFrame 1 x 5
Charlie	Daniels	DataFrame 1 x 5
Charlie	Chaplin	DataFrame 1 x 5
Bob	Marley	DataFrame 1 x 5
Alice	Wolf	DataFrame 1 x 5
Charlie	Byrd	DataFrame 1 x 5

Step 2: PivotGroupBy

name		city
firstName	lastName	London	Dubai	Moscow	Milan	Tokyo	null
Alice	Cooper	DataFrame 1 x 5
Bob	Dylan		DataFrame 1 x 5
Charlie	Daniels			DataFrame 1 x 5
Charlie	Chaplin				DataFrame 1 x 5
Bob	Marley					DataFrame 1 x 5
Alice	Wolf						DataFrame 1 x 5
Charlie	Byrd			DataFrame 1 x 5

Output DataFrame: rowsCount = 7, columnsCount = 2

name		city
firstName	lastName	London	Dubai	Moscow	Milan	Tokyo	null
Alice	Cooper	15.0	null	null	null	null	null
Bob	Dylan	null	45.0	null	null	null	null
Charlie	Daniels	null	null	20.0	null	null	null
Charlie	Chaplin	null	null	null	40.0	null	null
Bob	Marley	null	null	null	null	30.0	null
Alice	Wolf	null	null	null	null	null	20.0
Charlie	Byrd	null	null	30.0	null	null	null