df.groupBy { city }.max()

Input DataFrame: rowsCount = 7, columnsCount = 5

Step 1: GroupBy

Output DataFrame: rowsCount = 6, columnsCount = 4

df.groupBy { city }.mean()

Input DataFrame: rowsCount = 7, columnsCount = 5

Step 1: GroupBy

Output DataFrame: rowsCount = 6, columnsCount = 3

df.groupBy { city }.max { age }

Input DataFrame: rowsCount = 7, columnsCount = 5

Step 1: GroupBy

Output DataFrame: rowsCount = 6, columnsCount = 2

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

Input DataFrame: rowsCount = 7, columnsCount = 5

Step 1: GroupBy

Output DataFrame: rowsCount = 6, columnsCount = 2

df.groupBy { city }.count()

Input DataFrame: rowsCount = 7, columnsCount = 5

Step 1: GroupBy

Output DataFrame: rowsCount = 6, columnsCount = 2

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

Input DataFrame: rowsCount = 7, columnsCount = 5

Step 1: GroupBy

Output DataFrame: rowsCount = 6, columnsCount = 2

df.groupBy { city }.medianFor { age and weight }

Input DataFrame: rowsCount = 7, columnsCount = 5

Step 1: GroupBy

Output DataFrame: rowsCount = 6, columnsCount = 3

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

Input DataFrame: rowsCount = 7, columnsCount = 5

Step 1: GroupBy

Output DataFrame: rowsCount = 6, columnsCount = 3

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

Input DataFrame: rowsCount = 7, columnsCount = 5

Step 1: GroupBy

Output DataFrame: rowsCount = 6, columnsCount = 2