df.pivot { city }.groupBy { name }.aggregate { min { age } default 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: 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

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	0	0	0	0
Bob	Dylan	0	45	0	0	0	0
Charlie	Daniels	0	0	20	0	0	0
Charlie	Chaplin	0	0	0	40	0	0
Bob	Marley	0	0	0	0	30	0
Alice	Wolf	0	0	0	0	0	20
Charlie	Byrd	0	0	30	0	0	0

df.pivot { city }.groupBy { name }.default(0).min()

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

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

Step 3: 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	{ age: 15, weight: 54, isHappy: true }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }
Bob	Dylan	{ age: 0, weight: 0, isHappy: 0 }	{ age: 45, weight: 87, isHappy: true }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }
Charlie	Daniels	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 20, weight: 0, isHappy: false }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }
Charlie	Chaplin	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 40, weight: 0, isHappy: true }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }
Bob	Marley	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 30, weight: 68, isHappy: true }	{ age: 0, weight: 0, isHappy: 0 }
Alice	Wolf	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 20, weight: 55, isHappy: false }
Charlie	Byrd	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 30, weight: 90, isHappy: true }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }	{ age: 0, weight: 0, isHappy: 0 }