DataColumn

Properties

• name: String — name of the column; should be unique within containing dataframe

• path: ColumnPath — path to the column; depends on the way column was retrieved from dataframe

• type: KType — type of elements in the column

• hasNulls: Boolean — flag indicating whether column contains null values

• values: Iterable<T> — column data

• size: Int — number of elements in the column

Column kinds

DataColumn instances can be one of three subtypes: ValueColumn, ColumnGroup or FrameColumn

Column accessors

ColumnAccessors are used for typed data access in DataFrame. ColumnAccessor stores column name (for top-level columns) or column path (for nested columns), has type argument that corresponds to type of thep column, but it doesn't contain any actual data.

val age by column<Int>()

// Access fourth cell in the "age" column of dataframe `df`.
// This expression returns `Int` because variable `age` has `ColumnAccessor<Int>` type.
// If dataframe `df` has no column "age" or column "age" has type which is incompatible with `Int`,
// runtime exception will be thrown.
df[age][3] + 5

// Access first cell in the "age" column of dataframe `df`.
df[0][age] * 2

// Returns new dataframe sorted by age column (ascending)
df.sortBy(age)

// Returns new dataframe with the column "year of birth" added
df.add("year of birth") { 2021 - age }

// Returns new dataframe containing only rows with age > 30
df.filter { age > 30 }

Column accessors are created by property delegate column. Column type should be passed as type argument, column name will be taken from the variable name.

val name by column<String>()

To assign column name explicitly, pass it as an argument.

val accessor by column<String>("complex column name")

You can also create column accessors for ColumnGroups and FrameColumns

val columns by columnGroup()
val frames by frameColumn()

To reference nested columns inside ColumnGroups, invoke column<T>() on accessor to parent ColumnGroup:

val name by columnGroup()
val firstName by name.column<String>()

You can also create virtual accessor that doesn't point to a real column but computes some expression on every data access:

val fullName by column(df) { name.firstName + " " + name.lastName }

df[fullName]

val name by columnGroup()
val firstName by name.column<String>()
val lastName by name.column<String>()

val fullName by column { firstName() + " " + lastName() }

df[fullName]

val fullName by column { "name"["firstName"]<String>() + " " + "name"["lastName"]<String>() }

df[fullName]

If expression depends only on one column, you can also use map:

val age by column<Int>()
val year by age.map { 2021 - it }

df.filter { year > 2000 }

To convert ColumnAccessor into DataColumn add values using withValues function:

val age by column<Int>()
val ageCol1 = age.withValues(15, 20)
val ageCol2 = age.withValues(1..10)