df.groupBy { city }.pivot { name.lastName }.mean { 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

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

Step 2: PivotGroupBy

city	name
	lastName
London	{ Cooper: DataFrame [1 x 5], D..., ... }
Dubai	{ Cooper: DataFrame [0 x 0], D..., ... }
Moscow	{ Cooper: DataFrame [0 x 0], D..., ... }
Milan	{ Cooper: DataFrame [0 x 0], D..., ... }
Tokyo	{ Cooper: DataFrame [0 x 0], D..., ... }
null	{ Cooper: DataFrame [0 x 0], D..., ... }

Output DataFrame: rowsCount = 6, columnsCount = 2

city	name
	lastName
London	{ Cooper: 15.0 }
Dubai	{ Dylan: 45.0 }
Moscow	{ Daniels: 20.0, Byrd: 30.0 }
Milan	{ Chaplin: 40.0 }
Tokyo	{ Marley: 30.0 }
null	{ Wolf: 20.0 }

df.groupBy { city }.pivot { name.lastName }.meanOf { age / 2.0 }

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

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

Step 2: PivotGroupBy

city	name
	lastName
London	{ Cooper: DataFrame [1 x 5], D..., ... }
Dubai	{ Cooper: DataFrame [0 x 0], D..., ... }
Moscow	{ Cooper: DataFrame [0 x 0], D..., ... }
Milan	{ Cooper: DataFrame [0 x 0], D..., ... }
Tokyo	{ Cooper: DataFrame [0 x 0], D..., ... }
null	{ Cooper: DataFrame [0 x 0], D..., ... }

Output DataFrame: rowsCount = 6, columnsCount = 2

city	name
	lastName
London	{ Cooper: 7.5 }
Dubai	{ Dylan: 22.5 }
Moscow	{ Daniels: 10.0, Byrd: 15.0 }
Milan	{ Chaplin: 20.0 }
Tokyo	{ Marley: 15.0 }
null	{ Wolf: 10.0 }