convert

convert { columnsSelector }
    .with { rowExpression } | .asFrame { frameExpression } | .perRowCol { rowColExpression } | to<Type>() | to { colExpression }

rowExpression = DataRow.(OldValue) -> NewValue
rowColExpression = (DataRow, DataColumn) -> NewValue
colExpression = DataFrame.(DataColumn) -> DataColumn
frameExpression: DataFrame.(DataFrame) -> DataFrame

df.convert { age }.with { it.toDouble() }
df.convert { colsAtAnyDepth().colsOf<String>() }.with { it.toCharArray().toList() }

df.convert { name }.asFrame { it.add("fullName") { "$firstName $lastName" } }

Similar to replace with operation, columns can be converted in a compiler plugin-friendly fashion whenever you need to perform an operation on the entire column without changing its name. For example, parallel reading.

df.convert { name }.asColumn { col ->
    col.toList().parallelStream().map { it.toString() }.collect(Collectors.toList()).toColumn()
}

convert supports automatic type conversions between the following types:

df.convert { age }.to<Double>()
df.convert { colsOf<Number>() }.to<String>()
df.convert { name.firstName and name.lastName }.asColumn { it.length() }
df.convert { weight }.toFloat()

Automatic conversion from String to enum classes is also supported:

enum class Direction { NORTH, SOUTH, WEST, EAST }

dataFrameOf("direction")("NORTH", "WEST")
    .convert("direction").to<Direction>()

And finally, Value classes can be used with convert too. Both as conversion source and target:

@JvmInline
value class IntClass(val value: Int)

dataFrameOf("value")("1", "2") // note that values are strings; conversion is done automatically
    .convert("value").to<IntClass>()

convert﻿

convert