df.groupBy { city }.max()

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

Output DataFrame: rowsCount = 6, columnsCount = 4

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

df.groupBy { city }.mean()

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

Output DataFrame: rowsCount = 6, columnsCount = 3

city	age	weight
London	15.0	54.0
Dubai	45.0	87.0
Moscow	25.0	90.0
Milan	40.0	NaN
Tokyo	30.0	68.0
null	20.0	55.0

df.groupBy { city }.max { 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

Output DataFrame: rowsCount = 6, columnsCount = 2

city	age
London	15
Dubai	45
Moscow	30
Milan	40
Tokyo	30
null	20

df.groupBy { city }.sum("total weight") { 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: 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

Output DataFrame: rowsCount = 6, columnsCount = 2

city	total weight
London	54
Dubai	87
Moscow	90
Milan	0
Tokyo	68
null	55

df.groupBy { city }.count()

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

Output DataFrame: rowsCount = 6, columnsCount = 2

city	count
London	1
Dubai	1
Moscow	2
Milan	1
Tokyo	1
null	1

df.groupBy { city }.max { name.firstName.length() and name.lastName.length() }

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

Output DataFrame: rowsCount = 6, columnsCount = 2

city	max
London	6
Dubai	5
Moscow	7
Milan	7
Tokyo	6
null	5

df.groupBy { city }.medianFor { 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: 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

Output DataFrame: rowsCount = 6, columnsCount = 3

city	age	weight
London	15.0	54.0
Dubai	45.0	87.0
Moscow	25.0	90.0
Milan	40.0	null
Tokyo	30.0	68.0
null	20.0	55.0

df.groupBy { city }.minFor { (age into "min age") and (weight into "min 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: 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

Output DataFrame: rowsCount = 6, columnsCount = 3

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

df.groupBy { city }.meanOf("mean ratio") { weight?.div(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

Output DataFrame: rowsCount = 6, columnsCount = 2

city	mean ratio
London	3.0
Dubai	1.0
Moscow	3.0
Milan	NaN
Tokyo	2.0
null	2.0