DataRow

DataRow represents a single record, one piece of data within a DataFrame

Row functions

index(): Int — sequential row number in DataFrame, starts from 0
prev(): DataRow? — previous row (null for the first row)
next(): DataRow? — next row (null for the last row)
diff(T) { rowExpression }: T / diffOrNull { rowExpression }: T? — difference between the results of a row expression calculated for current and previous rows
explode(columns): DataFrame<T> — spread lists and DataFrames vertically into new rows
values(): List<Any?> — list of all cell values from the current row
valuesOf<T>(): List<T> — list of values of the given type
columnsCount(): Int — number of columns
columnNames(): List<String> — list of all column names
columnTypes(): List<KType> — list of all column types
namedValues(): List<NameValuePair<Any?>> — list of name-value pairs where name is a column name and value is cell value
namedValuesOf<T>(): List<NameValuePair<T>> — list of name-value pairs where value has given type
transpose(): DataFrame<NameValuePair<*>> — dataframe of two columns: name: String is column names and value: Any? is cell values
transposeTo<T>(): DataFrame<NameValuePair<T>> — dataframe of two columns: name: String is column names and value: T is cell values
getRow(Int): DataRow — row from DataFrame by row index
getRows(Iterable<Int>): DataFrame — dataframe with subset of rows selected by absolute row index.
relative(Iterable<Int>): DataFrame — dataframe with subset of rows selected by relative row index: relative(-1..1) will return previous, current and next row. Requested indices will be coerced to the valid range and invalid indices will be skipped
getValue<T>(columnName) — cell value of type T by this row and given columnName
getValueOrNull<T>(columnName) — cell value of type T? by this row and given columnName or null if there's no such column
get(column): T — cell value by this row and given column
String.invoke<T>(): T — cell value of type T by this row and given this column name
ColumnPath.invoke<T>(): T — cell value of type T by this row and given this column path
ColumnReference.invoke(): T — cell value of type T by this row and given this column
df() — DataFrame that current row belongs to

Row expressions

Row expressions provide a value for every row of DataFrame and are used in add, filter, forEach, update and other operations.

// Row expression computes values for a new column
df.add("fullName") { name.firstName + " " + name.lastName }

// Row expression computes updated values
df.update { weight }.at(1, 3, 4).with { prev()?.weight }

// Row expression computes cell content for values of pivoted column
df.pivot { city }.with { name.lastName.uppercase() }

Row expression signature: DataRow.(DataRow) -> T. Row values can be accessed with or without it keyword. Implicit and explicit argument represent the same DataRow object.

Row conditions

Row condition is a special case of row expression that returns Boolean.

// Row condition is used to filter rows by index
df.filter { index() % 5 == 0 }

// Row condition is used to drop rows where `age` is the same as in the previous row
df.drop { diffOrNull { age } == 0 }

// Row condition is used to filter rows for value update
df.update { weight }.where { index() > 4 && city != "Paris" }.with { 50 }

Row condition signature: DataRow.(DataRow) -> Boolean

Row statistics

The following statistics are available for DataRow:

rowMax
rowMin
rowSum
rowMean
rowStd
rowMedian

These statistics will be applied only to values of appropriate types and incompatible values will be ignored. For example, if DataFrame has columns of type String and Int, rowSum() will successfully compute sum of Int values in a row and ignore String values.

To apply statistics only to values of particular type use -Of versions:

rowMaxOf<T>
rowMinOf<T>
rowSumOf<T>
rowMeanOf<T>
rowMedianOf<T>

Last modified: 01 December 2023