mapIndexed

Returns a new DataColumn with the values that transform computes from the values of this column and their positions.

transform is called once for every value of this column, from the first one to the last one, so the new column has as many values as this one, in the same order. The new column has the same name as this one.

Which kind of column you get follows the reified type argument R, and not the computed values: a DataFrame type gives a FrameColumn, a DataRow type gives a ColumnGroup, and any other type gives a ValueColumn. A nullable DataFrame type belongs to the last group, because a FrameColumn cannot hold null.

infer only concerns a ValueColumn: it decides whether the type of that column is the reified type argument R as it is, or the type of the computed values. For a ColumnGroup and a FrameColumn, infer changes nothing.

The computed values have to fit the reified type argument R. A ValueColumn can never have a non-nullable DataFrame type, so a call that would give it one fails with an IllegalArgumentException. That happens when the computed values are dataframes and Infer.Type derives a DataFrame type for them, and also under a nullable DataFrame type when none of the computed values is null: the default Infer.Nulls then drops the nullability and leaves exactly that forbidden type. With at least one null among them the same call succeeds and gives a ValueColumn of the nullable DataFrame type.

The position of the first value is 0.

See also:

• The Map Operation — an overview of the whole map family.

• map — the same, without the positions.

• the overload with an explicit type argument — when the type of the new column is only known at runtime.

Example

// The first names, numbered: "1. Alice", "2. Bob", ...
df.name.firstName.mapIndexed { i, firstName -> "${i + 1}. $firstName" }

For more information: See map on a DataColumn on the documentation website.

Parameters

Returns a new DataColumn of the given type with the values that transform computes from the values of this column and their positions.

transform is called once for every value of this column, from the first one to the last one, so the new column has as many values as this one, in the same order. The new column has the same name as this one.

Which kind of column you get follows the type argument, and not the computed values: a DataFrame type gives a FrameColumn, a DataRow type gives a ColumnGroup, and any other type gives a ValueColumn. A nullable DataFrame type belongs to the last group, because a FrameColumn cannot hold null.

infer only concerns a ValueColumn: it decides whether the type of that column is the type argument as it is, or the type of the computed values. For a ColumnGroup and a FrameColumn, infer changes nothing.

The computed values have to fit the type argument. A ValueColumn can never have a non-nullable DataFrame type, so a call that would give it one fails with an IllegalArgumentException. That happens when the computed values are dataframes and Infer.Type derives a DataFrame type for them, and also under a nullable DataFrame type when none of the computed values is null: the default Infer.Nulls then drops the nullability and leaves exactly that forbidden type. With at least one null among them the same call succeeds and gives a ValueColumn of the nullable DataFrame type.

The position of the first value is 0.

See also:

• The Map Operation — an overview of the whole map family.

• map — the same, without the positions.

• the overload without a type argument — when the type of the new column is known at compile time.

Example

// The positions of the rows, in a column typed `Number` on purpose
df.age.mapIndexed(typeOf<Number>()) { i, _ -> i }

For more information: See map on a DataColumn on the documentation website.

Parameters