associateBy

The associateBy function builds a Map from a DataFrame by selecting a key for each row using a row expression.
The rows themselves (or values derived from them) become the map values.

If multiple rows produce the same key, only the last row for that key is kept. This matches the behavior of Kotlin’s standard kotlin.collections.associateBy function.

The keys are in the same order as the rows. A key that occurs in several rows appears at the position of its first row, with the row of its last one.

df.associateBy { keySelector }

keySelector: (DataRow) -> Key

toMap — converts a DataFrame into a Map by using column names as keys and their values as map values.
associate — builds a map from key–value pairs produced by transforming each row.

Example

Create a map with names as keys:

df.associateBy { "${name.firstName} ${name.lastName}" }

Output:

{
  Alice Cooper: { name:{ firstName:Alice, lastName:Cooper }, age:15, city:London, weight:54, isHappy:true },
  Bob Dylan: { name:{ firstName:Bob, lastName:Dylan }, age:45, city:Dubai, weight:87, isHappy:true },
  Charlie Daniels: { name:{ firstName:Charlie, lastName:Daniels }, age:20, city:Moscow, isHappy:false },
  Charlie Chaplin: { name:{ firstName:Charlie, lastName:Chaplin }, age:40, city:Milan, isHappy:true },
  Bob Marley: { name:{ firstName:Bob, lastName:Marley }, age:30, city:Tokyo, weight:68, isHappy:true },
  Alice Wolf: { name:{ firstName:Alice, lastName:Wolf }, age:20, weight:55, isHappy:false },
  Charlie Byrd: { name:{ firstName:Charlie, lastName:Byrd }, age:30, city:Moscow, weight:90, isHappy:true },
  Alice Smith: { name:{ firstName:Alice, lastName:Smith }, age:30, city:Milan, weight:52, isHappy:false },
  Bob Brown: { name:{ firstName:Bob, lastName:Brown }, age:15, city:London, weight:60, isHappy:false },
  Charlie Johnson: { name:{ firstName:Charlie, lastName:Johnson }, age:18, city:Dubai, weight:70, isHappy:false }
}

Both rules are visible at once if the city is taken as the key: London comes first, because the first person from London is in the first row, but it holds the last one. A row without a city gives a null key:

df.associateBy { city }

Output:

{
  London: { name:{ firstName:Bob, lastName:Brown }, age:15, city:London, weight:60, isHappy:false },
  Dubai: { name:{ firstName:Charlie, lastName:Johnson }, age:18, city:Dubai, weight:70, isHappy:false },
  Moscow: { name:{ firstName:Charlie, lastName:Byrd }, age:30, city:Moscow, weight:90, isHappy:true },
  Milan: { name:{ firstName:Alice, lastName:Smith }, age:30, city:Milan, weight:52, isHappy:false },
  Tokyo: { name:{ firstName:Bob, lastName:Marley }, age:30, city:Tokyo, weight:68, isHappy:true },
  null: { name:{ firstName:Alice, lastName:Wolf }, age:20, weight:55, isHappy:false }
}

29 September 2026

associateBy

Related functions

Example