Class/Object

com.audienceproject.crossbow

DataFrame

Related Docs: object DataFrame | package crossbow

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class DataFrame extends AnyRef

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Type Members

  1. class GroupedView extends AnyRef

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  2. class TypedView[T] extends Iterable[T]

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Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def addColumn(expr: Expr): DataFrame

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    Add a column to the DataFrame, evaluating to 'expr' at each individual row index.

    Add a column to the DataFrame, evaluating to 'expr' at each individual row index. Use the 'as' method on Expr to give the column a name.

    expr

    the Expr to evaluate as the new column

    returns

    new DataFrame

  5. def apply(columnNames: String*): DataFrame

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    Select a subset of columns from this DataFrame.

    Select a subset of columns from this DataFrame.

    columnNames

    names of columns to select

    returns

    new DataFrame

  6. def apply(range: Range): DataFrame

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    Retrieve a subset of rows from this DataFrame based on range of indices.

    Retrieve a subset of rows from this DataFrame based on range of indices.

    range

    range of row indices to retrieve

    returns

    new DataFrame

  7. def apply(index: Int): Seq[Any]

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    Retrieve a single row by index.

    Retrieve a single row by index.

    index

    row index

    returns

    row as a sequence of values

  8. def as[T](implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[T]): TypedView[T]

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    Typecast this DataFrame to a TypedView of the type parameter 'T'.

    Typecast this DataFrame to a TypedView of the type parameter 'T'. All columns in this DataFrame will have to be accounted for in the given type. A DataFrame with multiple columns will have its rows represented as tuples of the individual types of these columns.

    T

    the type of a row in this DataFrame

    returns

    TypedView on the contents of this DataFrame

  9. final def asInstanceOf[T0]: T0

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  10. def clone(): AnyRef

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  11. final def eq(arg0: AnyRef): Boolean

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  12. def equals(arg0: Any): Boolean

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  13. def filter(expr: Expr): DataFrame

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    Retrieve a subset of rows from this DataFrame based on the boolean evaluation of the given expression.

    Retrieve a subset of rows from this DataFrame based on the boolean evaluation of the given expression.

    expr

    the Expr to evaluate, if 'true' the given row will appear in the output

    returns

    new DataFrame

  14. def finalize(): Unit

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  15. final def getClass(): Class[_]

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  16. def groupBy(keyExprs: Expr*): GroupedView

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    Partition this DataFrame into groups, defined by the given set of expressions.

    Partition this DataFrame into groups, defined by the given set of expressions. The evaluation of each of the 'keyExprs' will appear as a column in the output.

    keyExprs

    the list of Expr that will evaluate to the keys of the groups

    returns

    GroupedView on this DataFrame

  17. def hashCode(): Int

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  18. def isEmpty: Boolean

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  19. final def isInstanceOf[T0]: Boolean

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  20. def iterator: Iterator[Seq[Any]]

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  21. def join(other: DataFrame, joinExpr: Expr, joinType: JoinType = JoinType.Inner): DataFrame

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    Join this DataFrame on another DataFrame, with the key evaluated by 'joinExpr'.

    Join this DataFrame on another DataFrame, with the key evaluated by 'joinExpr'. The resulting DataFrame will contain all the columns of this DataFrame and the other, where the column names of the other will be prepended with "#".

    other

    DataFrame to join with this one

    joinExpr

    Expr to evaluate as join key

    joinType

    JoinType as one of Inner, FullOuter, LeftOuter or RightOuter

    returns

    new DataFrame

    Note

    'joinExpr' must evaluate to a type with a natural ordering

  22. final def ne(arg0: AnyRef): Boolean

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  23. final def notify(): Unit

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  24. final def notifyAll(): Unit

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  25. val numColumns: Int

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  26. def printSchema(): Unit

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  27. def removeColumns(columnNames: String*): DataFrame

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    Remove one or more columns from the DataFrame.

    Remove one or more columns from the DataFrame.

    columnNames

    the names of the columns to remove

    returns

    new DataFrame

  28. def renameColumns(toNewName: (String) ⇒ String): DataFrame

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    Rename the columns of this DataFrame by applying the given function.

    Rename the columns of this DataFrame by applying the given function.

    toNewName

    function to map over the names of the columns

    returns

    new DataFrame

  29. def renameColumns(newNames: String*): DataFrame

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    Rename the columns of this DataFrame.

    Rename the columns of this DataFrame.

    newNames

    list of new names for each column of this DataFrame

    returns

    new DataFrame

  30. val rowCount: Int

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  31. val schema: Schema

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  32. def select(exprs: Expr*): DataFrame

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    Map over this DataFrame, selecting a set of expressions which will become the columns of a new DataFrame.

    Map over this DataFrame, selecting a set of expressions which will become the columns of a new DataFrame. Use the 'as' method on Expr to give names to the new columns. An expression which is only a column accessor will inherit the accessed column's name (unless it is renamed).

    exprs

    the list of Expr to evaluate as a new DataFrame

    returns

    new DataFrame

  33. def sortBy(expr: Expr, givenOrderings: Order*): DataFrame

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    Sort this DataFrame by the evaluation of 'expr'.

    Sort this DataFrame by the evaluation of 'expr'. If a natural ordering exists on this value, it will be used. User-defined orderings on other types or for overwriting the natural orderings with an explicit ordering can be supplied through the 'givenOrderings' argument.

    expr

    the Expr to evaluate as a sort key

    givenOrderings

    explicit Order to use on the sort key, or list of Order if the key is a tuple

    returns

    new DataFrame

  34. final def synchronized[T0](arg0: ⇒ T0): T0

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  35. def toString(): String

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  36. def union(other: DataFrame): DataFrame

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    Union this DataFrame with another DataFrame.

    Union this DataFrame with another DataFrame. Columns will be matched by name, and if matched they must have the same type. Columns that are not present in one or the other DataFrame will contain null-values in the output for the rows of the DataFrame in which the column was not present.

    other

    DataFrame to union with this one

    returns

    new DataFrame

  37. final def wait(): Unit

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  38. final def wait(arg0: Long, arg1: Int): Unit

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  39. final def wait(arg0: Long): Unit

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