join
Edit page Last modified: 14 January 2025Joins two DataFrame object by join columns.
join(otherDf, type = JoinType.Inner) [ { joinColumns } ]
joinColumns: JoinDsl.(LeftDataFrame) -> Columns
interface JoinDsl: LeftDataFrame {
val right: RightDataFrame
fun DataColumn.match(rightColumn: DataColumn)
}joinColumns is a column selector that defines column mapping for join:
df.join(other) { name match right.fullName }val name by columnGroup()
val fullName by columnGroup()
df.join(other) { name match fullName }df.join(other) { "name" match "fullName" }If mapped columns have the same name, just select join columns from the left DataFrame:
df.join(other) { name and city }val name by columnGroup()
val city by column<String>()
df.join(other) { name and city }df.join(other, "name", "city")If joinColumns is not specified, columns with the same name from both DataFrame objects will be used as join columns:
df.join(other)Join types
Supported join types:
Inner(default) — only matched rows from left and rightDataFrameobjectsFilter— only matched rows from leftDataFrameLeft— all rows from leftDataFrame, mismatches from rightDataFramefilled withnullRight— all rows from rightDataFrame, mismatches from leftDataFramefilled withnullFull— all rows from left and rightDataFrameobjects, any mismatches filled withnullExclude— only mismatched rows from leftDataFrame
For every join type there is a shortcut operation:
df.innerJoin(other) { name and city }
df.leftJoin(other) { name and city }
df.rightJoin(other) { name and city }
df.fullJoin(other) { name and city }
df.excludeJoin(other) { name and city }val name by columnGroup()
val city by column<String>()
df.innerJoin(other) { name and city }
df.leftJoin(other) { name and city }
df.rightJoin(other) { name and city }
df.fullJoin(other) { name and city }
df.excludeJoin(other) { name and city }df.innerJoin(other, "name", "city")
df.leftJoin(other, "name", "city")
df.rightJoin(other, "name", "city")
df.fullJoin(other, "name", "city")
df.excludeJoin(other, "name", "city")