class Frame[RX, CX, T] extends NumericOps[Frame[RX, CX, T]]
Frame
is an immutable container for 2D data which is indexed along both
axes (rows, columns) by associated keys (i.e., indexes).
The primary use case is homogeneous data, but a secondary concern is to support heterogeneous data that is homogeneous ony within any given column.
The row index, column index, and constituent value data are all backed ultimately by arrays.
Frame
is effectively a doubly-indexed associative map whose row keys and
col keys each have an ordering provided by the natural (provided) order of
their backing arrays.
Several factory and access methods are provided. In the following examples, assume that:
val f = Frame('a'->Vec(1,2,3), 'b'->Vec(4,5,6))
The apply
method takes a row and col key returns a slice of the original
Frame:
f(0,'a') == Frame('a'->Vec(1))
apply
also accepts a org.saddle.index.Slice:
f(0->1, 'b') == Frame('b'->Vec(4,5)) f(0, *) == Frame('a'->Vec(1), 'b'->Vec(4))
You may slice using the col
and row
methods respectively, as follows:
f.col('a') == Frame('a'->Vec(1,2,3)) f.row(0) == Frame('a'->Vec(1), 'b'->Vec(4)) f.row(0->1) == Frame('a'->Vec(1,2), 'b'->Vec(4,5))
You can achieve a similar effect with rowSliceBy
and colSliceBy
The colAt
and rowAt
methods take an integer offset i into the Frame, and
return a Series indexed by the opposing axis:
f.rowAt(0) == Series('a'->1, 'b'->4)
If there is a one-to-one relationship between offset i and key (ie, no duplicate keys in the index), you may achieve the same effect via key as follows:
f.first(0) == Series('a'->1, 'b'->4) f.firstCol('a') == Series(1,2,3)
The at
method returns an instance of a org.saddle.scalar.Scalar, which
behaves much like an Option
; it can be either an instance of
org.saddle.scalar.NA or a org.saddle.scalar.Value case class:
f.at(0, 0) == scalar.Scalar(1)
The rowSlice
and colSlice
methods allows slicing the Frame for locations
in [i, j) irrespective of the value of the keys at those locations.
f.rowSlice(0,1) == Frame('a'->Vec(1), 'b'->Vec(4))
Finally, the method raw
accesses a value directly, which may reveal the
underlying representation of a missing value (so be careful).
f.raw(0,0) == 1
Frame
may be used in arithmetic expressions which operate on two Frame
s
or on a Frame
and a scalar value. In the former case, the two Frames will
automatically align along their indexes:
f + f.shift(1) == Frame('a'->Vec(NA,3,5), 'b'->Vec(NA,9,11))
- RX
The type of row keys
- CX
The type of column keys
- T
The type of entries in the frame
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- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- def %[B, That](other: B)(implicit op: BinOp[Mod, Frame[RX, CX, T], B, That]): That
Integer modulus of division
Integer modulus of division
- B
type of the other operand
- That
result type of operation
- other
other operand instance (divisor)
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def %=[B](other: B)(implicit op: BinOpInPlace[Mod, Frame[RX, CX, T], B]): Unit
- Definition Classes
- NumericOps
- def &[B, That](other: B)(implicit op: BinOp[BitAnd, Frame[RX, CX, T], B, That]): That
Bit-wise AND
Bit-wise AND
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def &&[B, That](other: B)(implicit op: BinOp[AndOp, Frame[RX, CX, T], B, That]): That
Logical AND
Logical AND
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def *[B, That](other: B)(implicit op: BinOp[Multiply, Frame[RX, CX, T], B, That]): That
Multiplication
Multiplication
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def **[B, That](other: B)(implicit op: BinOp[Power, Frame[RX, CX, T], B, That]): That
Exponentiation
Exponentiation
- B
type of the other operand
- That
result type of operation
- other
other operand instance (exponent)
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def **=[B](other: B)(implicit op: BinOpInPlace[Power, Frame[RX, CX, T], B]): Unit
- Definition Classes
- NumericOps
- def *=[B](other: B)(implicit op: BinOpInPlace[Multiply, Frame[RX, CX, T], B]): Unit
- Definition Classes
- NumericOps
- def +[B, That](other: B)(implicit op: BinOp[Add, Frame[RX, CX, T], B, That]): That
Addition
Addition
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def +=[B](other: B)(implicit op: BinOpInPlace[Add, Frame[RX, CX, T], B]): Unit
- Definition Classes
- NumericOps
- def -[B, That](other: B)(implicit op: BinOp[Subtract, Frame[RX, CX, T], B, That]): That
Subtraction
Subtraction
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def -=[B](other: B)(implicit op: BinOpInPlace[Subtract, Frame[RX, CX, T], B]): Unit
- Definition Classes
- NumericOps
- def /[B, That](other: B)(implicit op: BinOp[Divide, Frame[RX, CX, T], B, That]): That
Division
Division
- B
type of the other operand
- That
result type of operation
- other
other operand instance (divisor)
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def /=[B](other: B)(implicit op: BinOpInPlace[Divide, Frame[RX, CX, T], B]): Unit
- Definition Classes
- NumericOps
- def <[B, That](other: B)(implicit op: BinOp[LtOp, Frame[RX, CX, T], B, That]): That
Less-than comparison operator
Less-than comparison operator
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def <<[B, That](other: B)(implicit op: BinOp[BitShl, Frame[RX, CX, T], B, That]): That
Bit-shift left
Bit-shift left
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def <=[B, That](other: B)(implicit op: BinOp[LteOp, Frame[RX, CX, T], B, That]): That
Less-than-or-equal-to comparison operator
Less-than-or-equal-to comparison operator
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def <>[B, That](other: B)(implicit op: BinOp[NeqOp, Frame[RX, CX, T], B, That]): That
Element-wise inequality operator
Element-wise inequality operator
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- def =?[B, That](other: B)(implicit op: BinOp[EqOp, Frame[RX, CX, T], B, That]): That
Element-wise equality operator
Element-wise equality operator
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def >[B, That](other: B)(implicit op: BinOp[GtOp, Frame[RX, CX, T], B, That]): That
Greater-than comparison operator
Greater-than comparison operator
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def >=[B, That](other: B)(implicit op: BinOp[GteOp, Frame[RX, CX, T], B, That]): That
Greater-than-or-equal-to comparison operator
Greater-than-or-equal-to comparison operator
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def >>[B, That](other: B)(implicit op: BinOp[BitShr, Frame[RX, CX, T], B, That]): That
Bit-shift right (arithmetic)
Bit-shift right (arithmetic)
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def >>>[B, That](other: B)(implicit op: BinOp[BitUShr, Frame[RX, CX, T], B, That]): That
Bit-shift right (logical)
Bit-shift right (logical)
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def T: Frame[CX, RX, T]
The transpose of the frame (swapping the axes)
- def ^[B, That](other: B)(implicit op: BinOp[BitXor, Frame[RX, CX, T], B, That]): That
Bit-wise EXCLUSIVE OR
Bit-wise EXCLUSIVE OR
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def addCol(other: Series[RX, T], how: JoinType): Frame[RX, Int, T]
Add a new column.
Add a new column. Resets column index
The result is a Frame whose row index is the result of the join, and whose column index has been reset to [0, numcols], and whose values are sourced from the original Frame and Series.
- other
Series to join with
- how
How to perform the join
- def addCol(other: Series[RX, T], newColIx: CX, how: JoinType = OuterJoin): Frame[RX, CX, T]
Same as
addCol
, but preserve the column index, adding the specified index value,newColIx
as an index for theother
Series. - def addRow(other: Series[CX, T], how: JoinType): Frame[Int, CX, T]
See addRow; operates row-wise.
- def addRow(other: Series[CX, T], newRowIx: RX, how: JoinType = OuterJoin): Frame[RX, CX, T]
See addCol, operates row-wise.
- def align[U](other: Frame[RX, CX, U], rhow: JoinType = OuterJoin, chow: JoinType = OuterJoin)(implicit arg0: ST[U]): (Frame[RX, CX, T], Frame[RX, CX, U])
Aligns this frame with another frame, returning the left and right frames aligned to each others indexes according to the the provided parameters
Aligns this frame with another frame, returning the left and right frames aligned to each others indexes according to the the provided parameters
- other
Other frame to align with
- rhow
How to perform the join on the row indexes
- chow
How to perform the join on the col indexes
- def apply(rix: Array[RX], cix: Array[CX]): Frame[RX, CX, T]
Slice from by an array of row keys and an array of col keys
Slice from by an array of row keys and an array of col keys
- rix
An array of row keys
- cix
An array of col keys
- def apply(rix: Array[RX], cix: Slice[CX]): Frame[RX, CX, T]
Slice frame by array of row keys and a col slice
Slice frame by array of row keys and a col slice
- rix
An array of row keys
- cix
A col slice
- def apply(rix: Slice[RX], cix: Array[CX]): Frame[RX, CX, T]
Slice frame by row slice and array of column keys
Slice frame by row slice and array of column keys
- rix
A row slice
- cix
An array of column keys
- def apply(rix: Slice[RX], cix: Slice[CX]): Frame[RX, CX, T]
Slice frame by row and column slice specifiers
Slice frame by row and column slice specifiers
- rix
A row slice
- cix
A col slice
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def at(r: Slice[Int], c: Slice[Int]): Frame[RX, CX, T]
Access a slice of the Frame by Slice parameters
Access a slice of the Frame by Slice parameters
- r
Slice to apply to rows
- c
Slice to apply to cols
- def at(r: Int, c: Array[Int]): Series[CX, T]
Access a slice of the Frame by integer offsets
Access a slice of the Frame by integer offsets
- r
Integer row offset
- c
Array of col offsets
- def at(r: Array[Int], c: Int): Series[RX, T]
Access a slice of the Frame by integer offsets
Access a slice of the Frame by integer offsets
- r
Array of row offsets
- c
Integer col offset
- def at(r: Array[Int], c: Array[Int]): Frame[RX, CX, T]
Access a slice of the Frame by integer offsets
Access a slice of the Frame by integer offsets
- r
Array of row offsets
- c
Array of col offsets
- def at(r: Int, c: Int): Scalar[T]
Access a (Scalar-boxed) value from within the Frame
Access a (Scalar-boxed) value from within the Frame
- r
Integer row offset
- c
Integer col offset
- def cbind(other: Frame[RX, CX, T], how: JoinType = OuterJoin): Frame[RX, CX, T]
Same as rconcat.
Same as rconcat. Concatenates two Frames by concatenating their lists of columns A1 A2 rconcat B1 B2 = A1 A2 B1 B2 A3 A4 B3 B4 A3 A4 B3 B4
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- def col(keys: Array[CX]): Frame[RX, CX, T]
Given an array of column keys, slice out the corresponding column(s)
Given an array of column keys, slice out the corresponding column(s)
- keys
Array of keys
- def col(slice: Slice[CX]): Frame[RX, CX, T]
Given a Slice of type of column key, slice out corresponding column(s)
Given a Slice of type of column key, slice out corresponding column(s)
- slice
Slice containing appropriate key bounds
- def col(keys: CX*): Frame[RX, CX, T]
Given one or more column keys, slice out the corresponding column(s)
Given one or more column keys, slice out the corresponding column(s)
- keys
Column key(s) (sequence)
- def colAt(slice: Slice[Int]): Frame[RX, CX, T]
Access frame columns specified by a slice
Access frame columns specified by a slice
- slice
a slice specifier
- def colAt(locs: Array[Int]): Frame[RX, CX, T]
Access frame columns at a particular integer offsets
Access frame columns at a particular integer offsets
- locs
an array of integer offsets
- def colAt(locs: Int*): Frame[RX, CX, T]
Access frame columns at a particular integer offsets
Access frame columns at a particular integer offsets
- locs
a sequence of integer offsets
- def colAt(loc: Int): Series[RX, T]
Access frame column at a particular integer offset
Access frame column at a particular integer offset
- loc
integer offset
- val colIx: Index[CX]
- def colSlice(from: Int, until: Int, stride: Int = 1): Frame[RX, CX, T]
Access frame columns between two integer offsets, [from, until)
Access frame columns between two integer offsets, [from, until)
- from
Beginning offset
- until
One past ending offset
- stride
Optional increment between offsets
- def colSliceBy(from: CX, to: CX, inclusive: Boolean = true): Frame[RX, CX, T]
Slice out a set of columns from the frame
Slice out a set of columns from the frame
- from
Key from which to begin slicing
- to
Key at which to end slicing
- inclusive
Whether to include 'to' key; true by default
- def colSplitAt(c: Int): (Frame[RX, CX, T], Frame[RX, CX, T])
Split Frame into two frames at column position c
Split Frame into two frames at column position c
- c
Position at which to split Frame
- def colSplitBy(k: CX): (Frame[RX, CX, T], Frame[RX, CX, T])
Split Frame into two frames at column key k
Split Frame into two frames at column key k
- k
Key at which to split Frame
k
is included in the right Frame [1,2,3,4] split at 2 yields [1] and [2,3,4]
- def concat(other: Frame[RX, CX, T], how: JoinType = OuterJoin): Frame[RX, CX, T]
Concatenate the Frame instances together (vertically, i.e.
Concatenate the Frame instances together (vertically, i.e. concatenate as lists of rows) whose indexes share the same type of elements, and where there exists some way to join the values of the Frames. For instance, Frame[X, Y, Double]
concat
Frame[X, Y, Int] will promote Int to Double as a result of the implicit existence of a Promoter[Double, Int, Double] instance. The resulting row index will simply be the concatenation of the input row indexes, and the column index will be the joint index (with join type specified as argument).A1 A2 concat B1 B2 = A1 A2 A3 A4 B3 B4 A3 A4 B1 B2 B3 B4
- other
Frame[RX, CX, U] to concat
- def count: Series[CX, Int]
Count of the elements of each column, ignoring NA values
- def cshift(n: Int = 1): Frame[RX, CX, T]
See shift; operates col-wise
- def distinct: Frame[RX, CX, T]
Return the frame with the first occurence of each column key.
Return the frame with the first occurence of each column key. Rows are not changed.
- def dot[B, That](other: B)(implicit op: BinOp[InnerProd, Frame[RX, CX, T], B, That]): That
Dot (inner) product
Dot (inner) product
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def dropNA: Frame[RX, CX, T]
Return Frame excluding any of those columns which have an NA value
- def emptyCol: Series[RX, T]
Return empty series of type equivalent to a column of frame
- def emptyRow: Series[CX, T]
Return empty series of type equivalent to a row of frame
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(other: Any): Boolean
- Definition Classes
- Frame → AnyRef → Any
- def fillNA(fillMethod: FillMethod, limit: Int = 0): Frame[RX, CX, T]
Fill NA values by propagating defined values column-wise.
Fill NA values by propagating defined values column-wise.
- limit
If > 0, propagate over a maximum of
limit
consecutive NA values.
- def fillNA(v: T): Frame[RX, CX, T]
Fill NAs with a defined value.
- def filter(pred: (Series[RX, T]) => Boolean): Frame[RX, CX, T]
Return Frame whose columns satisfy a predicate function operating on that column
Return Frame whose columns satisfy a predicate function operating on that column
- pred
Predicate function from Series[RX, T] => Boolean
- def filterAt(pred: (Int) => Boolean): Frame[RX, CX, T]
Return Frame whose columns satisfy a predicate function operating on the column index offset
Return Frame whose columns satisfy a predicate function operating on the column index offset
- pred
Predicate function from CX => Boolean
- def filterIx(pred: (CX) => Boolean): Frame[RX, CX, T]
Return Frame whose columns satisfy a predicate function operating on the column index
Return Frame whose columns satisfy a predicate function operating on the column index
- pred
Predicate function from CX => Boolean
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable])
- def first(k: RX): Series[CX, T]
Extract first row matching a particular key
Extract first row matching a particular key
- k
Key to match
- def firstCol(k: CX): Series[RX, T]
Extract first col matching a particular key
Extract first col matching a particular key
- k
Key to match
- def flatMap[SX, DX, U](f: ((RX, CX, T)) => Iterable[(SX, DX, U)])(implicit arg0: ST[SX], arg1: ORD[SX], arg2: ST[DX], arg3: ORD[DX], arg4: ST[U]): Frame[SX, DX, U]
Map over each triple (r, c, v) in the Frame, flattening results, and returning a new frame from the resulting triples.
- def get(r: RX, c: CX): Scalar[T]
Return scalar of first row and column found
Return scalar of first row and column found
- r
Row to match
- c
Column to match
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
- def groupBy[Y](ix: Index[Y])(implicit arg0: ST[Y], arg1: ORD[Y]): FrameGrouper[Y, RX, CX, T]
Construct a org.saddle.groupby.FrameGrouper with which further computations, such as combine or transform, may be performed.
Construct a org.saddle.groupby.FrameGrouper with which further computations, such as combine or transform, may be performed. The groups are constructed from the keys of the provided index, with each unique key corresponding to a group.
- Y
Type of elements of ix
- ix
Index with which to perform grouping
- def groupBy[Y](fn: (RX) => Y)(implicit arg0: ST[Y], arg1: ORD[Y]): FrameGrouper[Y, RX, CX, T]
Construct a org.saddle.groupby.FrameGrouper with which further computations, such as combine or transform, may be performed.
Construct a org.saddle.groupby.FrameGrouper with which further computations, such as combine or transform, may be performed. The groups are constructed from the result of the function applied to the keys of the row index; each unique result of calling the function on elements of the row index corresponds to a group.
- Y
Type of function codomain
- fn
Function from RX => Y
- def groupBy: FrameGrouper[RX, RX, CX, T]
Construct a org.saddle.groupby.FrameGrouper with which further computations, such as combine or transform, may be performed.
Construct a org.saddle.groupby.FrameGrouper with which further computations, such as combine or transform, may be performed. The groups are constructed from the keys of the row index, with each unique key corresponding to a group.
- def hashCode(): Int
- Definition Classes
- Frame → AnyRef → Any
- def head(n: Int): Frame[RX, CX, T]
Extract first n rows
Extract first n rows
- n
number of rows to extract
- def headCol(n: Int): Frame[RX, CX, T]
Extract first n columns
Extract first n columns
- n
number of columns to extract
- def isEmpty: Boolean
Returns true if there are no values in the Frame
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- def joinMap[U, V](other: Frame[RX, CX, U], rhow: JoinType = LeftJoin, chow: JoinType = RightJoin)(f: (T, U) => V)(implicit arg0: ST[U], arg1: ST[V]): Frame[RX, CX, V]
Joins two frames along both their indexes and applies a function to each pair of values; when either value is NA, the result of the function is forced to be NA.
Joins two frames along both their indexes and applies a function to each pair of values; when either value is NA, the result of the function is forced to be NA.
- U
The type of other frame values
- V
The result type of the function
- other
Other Frame
- rhow
The type of join to effect on the rows
- chow
The type of join to effect on the cols
- f
The function to apply
- def last(k: RX): Series[CX, T]
Extract last row matching a particular key
Extract last row matching a particular key
- k
Key to match
- def lastCol(k: CX): Series[RX, T]
Extract first col matching a particular key
Extract first col matching a particular key
- k
Key to match
- def map[SX, DX, U](f: ((RX, CX, T)) => (SX, DX, U))(implicit arg0: ST[SX], arg1: ORD[SX], arg2: ST[DX], arg3: ORD[DX], arg4: ST[U]): Frame[SX, DX, U]
Map over each triple (r, c, v) in the Frame, returning a new frame from the resulting triples.
- def mapColIndex[Y](fn: (CX) => Y)(implicit arg0: ST[Y], arg1: ORD[Y]): Frame[RX, Y, T]
Map a function over the col index, resulting in a new Frame
Map a function over the col index, resulting in a new Frame
- Y
Result type of index, ie Index[Y]
- fn
The function CX => Y with which to map
- def mapCols[Y](fn: (CX, Vec[T]) => Vec[Y])(implicit arg0: ST[Y]): Frame[RX, CX, Y]
Map a function over the columns, resulting in a new Frame
Map a function over the columns, resulting in a new Frame
- Y
Result type of mapped value
- fn
The function (CX,Vec[T]) => Vec[Y] with which to map
- def mapRowIndex[Y](fn: (RX) => Y)(implicit arg0: ST[Y], arg1: ORD[Y]): Frame[Y, CX, T]
Map a function over the row index, resulting in a new Frame
Map a function over the row index, resulting in a new Frame
- Y
Result type of index, ie Index[Y]
- fn
The function RX => Y with which to map
- def mapRows[Y](fn: (RX, Vec[T]) => Vec[Y])(implicit arg0: ST[Y]): Frame[RX, CX, Y]
Map a function over the rows, resulting in a new Frame
Map a function over the rows, resulting in a new Frame
- Y
Result type of mapped value
- fn
The function (RX,Vec[T]) => Vec[Y] with which to map
- def mapValues[U](f: (T) => U)(implicit arg0: ST[U]): Frame[RX, CX, U]
Map over the values of the Frame.
Map over the values of the Frame. Applies a function to each (non-na) value in the frame, returning a new frame whose indices remain the same.
- U
The type of the resulting values
- f
Function from T to U
- def mapVec[U](f: (Vec[T]) => Vec[U])(implicit arg0: ST[U]): Frame[RX, CX, U]
Map a function over each column vector and collect the results into a Frame respecting the original indexes.
Map a function over each column vector and collect the results into a Frame respecting the original indexes.
- U
Type of result Vec of the function
- f
Function acting on Vec[T] and producing another Vec
- def mask(m: Vec[Boolean]): Frame[RX, CX, T]
Create a new Frame whose columns follow the rule that, wherever the mask Vec is true, the column value is masked with NA
Create a new Frame whose columns follow the rule that, wherever the mask Vec is true, the column value is masked with NA
- m
Mask Vec[Boolean]
- def mask(f: (T) => Boolean): Frame[RX, CX, T]
Create a new Frame that, whenever the mask predicate function evaluates to true on a value, is masked with NA
Create a new Frame that, whenever the mask predicate function evaluates to true on a value, is masked with NA
- f
Function from T to Boolean
- def max(implicit num: NUM[T]): Series[CX, T]
Max of the elements of each column, ignoring NA values
- def mean(implicit num: NUM[T]): Series[CX, Double]
Mean of each column
- def median(implicit num: NUM[T]): Series[CX, Double]
Median of each column
- def melt[W](implicit melter: Melter[RX, CX, W]): Series[W, T]
Melt stacks the row index of arity N with the column index of arity M to form a result index of arity N + M, producing a 1D Series whose values are from the original Frame as indexed by the corresponding keys.
Melt stacks the row index of arity N with the column index of arity M to form a result index of arity N + M, producing a 1D Series whose values are from the original Frame as indexed by the corresponding keys.
For example, given:
Frame(1 -> Series('a' -> 1, 'b' -> 3), 2 -> Series('a' -> 2, 'b' -> 4)).melt
produces:
res0: org.saddle.Series[(Char, Int),Int] = [4 x 1] a 1 => 1 2 => 2 b 1 => 3 2 => 4
- W
Output type (tuple of arity N + M)
- melter
Implicit evidence for a Melter for the two indexes
- def min(implicit num: NUM[T]): Series[CX, T]
Min of the elements of each column, ignoring NA values
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- def numCols: Int
Number of cols in the Frame
- def numRows: Int
Number of rows in the Frame
- def outer[B, That](other: B)(implicit op: BinOp[OuterProd, Frame[RX, CX, T], B, That]): That
Outer product
Outer product
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def print(nrows: Int = 10, ncols: Int = 10, stream: OutputStream = System.out): Unit
Pretty-printer for Frame, which simply outputs the result of stringify.
Pretty-printer for Frame, which simply outputs the result of stringify.
- nrows
Number of rows to display
- ncols
Number of cols to display
- def prod(implicit num: NUM[T]): Series[CX, T]
Product of the elements of each column, ignoring NA values
- def raw(r: Int, c: Int): T
Access the raw (unboxed) value at an offset within the Frame
Access the raw (unboxed) value at an offset within the Frame
- r
Integer row offset
- c
Integer col offset
- def rbind(other: Frame[RX, CX, T], how: JoinType = OuterJoin): Frame[RX, CX, T]
Same as concat.
Same as concat. Concatenates two Frames by concatenating their lists of rows A1 A2 concat B1 B2 = A1 A2 A3 A4 B3 B4 A3 A4 B1 B2 B3 B4
- def rconcat(other: Frame[RX, CX, T], how: JoinType = OuterJoin): Frame[RX, CX, T]
See concat; operates row-wise.
See concat; operates row-wise. Concetanates two Frames by concatenating their lists of columns A1 A2 rconcat B1 B2 = A1 A2 B1 B2 A3 A4 B3 B4 A3 A4 B3 B4
- def rdistinct: Frame[RX, CX, T]
Return the series with the first occurence of each row key.
Return the series with the first occurence of each row key. Columns are not changed.
- def rdropNA: Frame[RX, CX, T]
See dropNA; operates row-wise
- def reduce[U](f: (Series[RX, T]) => U)(implicit arg0: ST[U]): Series[CX, U]
Apply a function to each column series which results in a single value, and return the series of results indexed by original column index.
Apply a function to each column series which results in a single value, and return the series of results indexed by original column index.
- U
The output type of the function
- f
Function taking a column (series) to a value
- def reindex(rix: Index[RX], cix: Index[CX]): Frame[RX, CX, T]
Create a new Frame whose indexes are formed from the provided arguments, and whose values are derived from the original Frame.
Create a new Frame whose indexes are formed from the provided arguments, and whose values are derived from the original Frame. Keys in the provided indices which do not map to existing values will map to NA in the new Frame.
- rix
Sequence of keys to be the row index of the result Frame
- cix
Sequence of keys to be the col index of the result Frame
- def reindexCol(cix: Index[CX]): Frame[RX, CX, T]
Create a new Frame whose col index is formed of the provided argument, and whose values are derived from the original Frame.
Create a new Frame whose col index is formed of the provided argument, and whose values are derived from the original Frame.
- cix
Sequence of keys to be the col index of the result Frame
- def reindexRow(rix: Index[RX]): Frame[RX, CX, T]
Create a new Frame whose row index is formed of the provided argument, and whose values are derived from the original Frame.
Create a new Frame whose row index is formed of the provided argument, and whose values are derived from the original Frame.
- rix
Sequence of keys to be the row index of the result Frame
- def resetColIndex: Frame[RX, Int, T]
Create a new Frame whose values are the same, but whose col index has been changed to the bound [0, numCols - 1), as in an array.
- def resetRowIndex: Frame[Int, CX, T]
Create a new Frame whose values are the same, but whose row index has been changed to the bound [0, numRows - 1), as in an array.
- def rfilter(pred: (Series[CX, T]) => Boolean): Frame[RX, CX, T]
See filter; operates row-wise
- def rfilterAt(pred: (Int) => Boolean): Frame[RX, CX, T]
See filterAt; operates row-wise
- def rfilterIx(pred: (RX) => Boolean): Frame[RX, CX, T]
See filterIx; operates row-wise
- def rmapVec[U](f: (Vec[T]) => Vec[U])(implicit arg0: ST[U]): Frame[RX, CX, U]
See mapVec; operates row-wise
- def rmask(b: Vec[Boolean]): Frame[RX, CX, T]
See mask; operates row-wise
- def rolling[B](windowSize: Int, f: (Series[RX, T]) => B)(implicit arg0: ST[B]): Frame[RX, CX, B]
Produce a Frame each of whose columns are the result of executing a function on a sliding window of each column series.
Produce a Frame each of whose columns are the result of executing a function on a sliding window of each column series.
- B
Result type of function
- f
Function Series[X, T] => B to operate on sliding window
- def rollingFtoS[B](windowSize: Int, f: (Frame[RX, CX, T]) => B)(implicit arg0: ST[B]): Series[RX, B]
Create a Series by rolling over winSz number of rows of the Frame at a time, and applying a function that takes those rows to a single value.
Create a Series by rolling over winSz number of rows of the Frame at a time, and applying a function that takes those rows to a single value.
- B
Result element type of Series
- f
Function taking the (sub) frame to B
- def row(keys: Array[RX]): Frame[RX, CX, T]
Given an array of row keys, slice out the corresponding row(s)
Given an array of row keys, slice out the corresponding row(s)
- keys
Array of keys
- def row(slice: Slice[RX]): Frame[RX, CX, T]
Given a Slice of type of row key, slice out corresponding row(s)
Given a Slice of type of row key, slice out corresponding row(s)
- slice
Slice containing appropriate key bounds
- def row(keys: RX*): Frame[RX, CX, T]
Given one or more row keys, slice out the corresponding row(s)
Given one or more row keys, slice out the corresponding row(s)
- keys
Row key(s) (sequence)
- def rowAt(slice: Slice[Int]): Frame[RX, CX, T]
Access frame rows specified by a slice
Access frame rows specified by a slice
- slice
a slice specifier
- def rowAt(locs: Array[Int]): Frame[RX, CX, T]
Access frame rows at a particular integer offsets
Access frame rows at a particular integer offsets
- locs
an array of integer offsets
- def rowAt(locs: Int*): Frame[RX, CX, T]
Access frame rows at a particular integer offsets
Access frame rows at a particular integer offsets
- locs
a sequence of integer offsets
- def rowAt(loc: Int): Series[CX, T]
Access frame row at a particular integer offset
Access frame row at a particular integer offset
- loc
integer offset
- def rowIterator: Iterator[(RX, Series[CX, T])]
- val rowIx: Index[RX]
- def rowSlice(from: Int, until: Int, stride: Int = 1): Frame[RX, CX, T]
Access frame rows between two integer offsets, [from, until)
Access frame rows between two integer offsets, [from, until)
- from
Beginning offset
- until
One past ending offset
- stride
Optional increment between offsets
- def rowSliceBy(from: RX, to: RX, inclusive: Boolean = true): Frame[RX, CX, T]
Slice out a set of rows from the frame
Slice out a set of rows from the frame
- from
Key from which to begin slicing
- to
Key at which to end slicing
- inclusive
Whether to include 'to' key; true by default
- def rowSplitAt(r: Int): (Frame[RX, CX, T], Frame[RX, CX, T])
Split Frame into two frames at row position r
Split Frame into two frames at row position r
- r
Position at which to split Frame
- def rowSplitBy(k: RX): (Frame[RX, CX, T], Frame[RX, CX, T])
Split Frame into two frames at row key k
Split Frame into two frames at row key k
- k
Key at which to split Frame
- def rreduce[U](f: (Series[CX, T]) => U)(implicit arg0: ST[U]): Series[RX, U]
See reduce; operates row-wise
- def rsqueeze: Frame[RX, CX, T]
See squeeze; operates row-wise
- def rtransform[U, SX](f: (Series[CX, T]) => Series[SX, U])(implicit arg0: ST[U], arg1: ST[SX], arg2: ORD[SX]): Frame[RX, SX, U]
See transform; operates row-wise
- def rwhere(pred: Vec[Boolean]): Frame[RX, CX, T]
See where; operates row-wise
- def rwhere(pred: Series[_, Boolean]): Frame[RX, CX, T]
See where; operates row-wise
- def setColIndex[Y](newIx: Index[Y])(implicit arg0: ST[Y], arg1: ORD[Y]): Frame[RX, Y, T]
Create a new Frame using the current values but with the new col index.
Create a new Frame using the current values but with the new col index. Positions of the values do not change. Length of new index must be equal to number of cols.
- Y
Type of elements of new Index
- newIx
A new Index
- def setRowIndex[Y](newIx: Index[Y])(implicit arg0: ST[Y], arg1: ORD[Y]): Frame[Y, CX, T]
Create a new Frame using the current values but with the new row index.
Create a new Frame using the current values but with the new row index. Positions of the values do not change. Length of new index must be equal to number of rows.
- Y
Type of elements of new Index
- newIx
A new Index
- def shift(n: Int = 1): Frame[RX, CX, T]
Shift the sequence of values relative to the row index by some offset, dropping those values which no longer associate with a key, and having those keys which no longer associate to a value instead map to NA.
Shift the sequence of values relative to the row index by some offset, dropping those values which no longer associate with a key, and having those keys which no longer associate to a value instead map to NA.
- n
Number to shift
- def sortedCIx: Frame[RX, CX, T]
Create a new Frame whose cols are sorted according to the col index keys
- def sortedCIxReverse: Frame[RX, CX, T]
Create a new Frame whose cols are sorted according to the reveverse of col index keys
- def sortedCols(locs: Int*)(implicit ev: ORD[T]): Frame[RX, CX, T]
Create a new Frame whose cols are sorted primarily on the values in the first row specified in the argument list, and then on the values in the next row, etc.
Create a new Frame whose cols are sorted primarily on the values in the first row specified in the argument list, and then on the values in the next row, etc.
- locs
Location of rows containing values to sort on
- Annotations
- @nowarn()
- def sortedColsBy[Q](f: (Series[RX, T]) => Q)(implicit arg0: ORD[Q]): Frame[RX, CX, T]
Create a new Frame whose cols are sorted by the result of a function acting on each col.
Create a new Frame whose cols are sorted by the result of a function acting on each col.
- Q
Result type of the function
- f
Function from a single col (represented as series) to a value having an ordering
- def sortedRIx: Frame[RX, CX, T]
Create a new Frame whose rows are sorted according to the row index keys
- def sortedRIxReverse: Frame[RX, CX, T]
Create a new Frame whose rows are sorted according to the reverse of row index keys
- def sortedRows(locs: Int*)(implicit ev: ORD[T]): Frame[RX, CX, T]
Create a new Frame whose rows are sorted primarily on the values in the first column specified in the argument list, and then on the values in the next column, etc.
Create a new Frame whose rows are sorted primarily on the values in the first column specified in the argument list, and then on the values in the next column, etc.
- locs
Location of columns containing values to sort on
- Annotations
- @nowarn()
- def sortedRowsBy[Q](f: (Series[CX, T]) => Q)(implicit arg0: ORD[Q]): Frame[RX, CX, T]
Create a new Frame whose rows are sorted by the result of a function acting on each row.
Create a new Frame whose rows are sorted by the result of a function acting on each row.
- Q
Result type of the function
- f
Function from a single row (represented as series) to a value having an ordering
- def squeeze: Frame[RX, CX, T]
Drop all columns from the Frame which have nothing but NA values.
- def stack[O1, O2, V](implicit splt: Splitter[CX, O1, O2], stkr: Stacker[RX, O2, V], ord1: ORD[O1], ord2: ORD[O2], m1: ST[O1], m2: ST[O2]): Frame[V, O1, T]
Stack pivots the innermost column labels to the innermost row labels.
Stack pivots the innermost column labels to the innermost row labels. That is, it splits a col index of tuple keys of arity N into a new col index having arity N-1 and a remaining index C, and forms a new row index by stacking the existing row index with C. The resulting Frame has values as in the original Frame indexed by the corresponding keys. It does the reverse of unstack.
- O1
The N-1 arity column index type
- O2
The 1-arity type of split-out index C
- V
The type of the stacked row index
- splt
An implicit instance of Splitter to do the splitting
- stkr
An implicit instance of Stacker to do the stacking
- def stringify(nrows: Int = 10, ncols: Int = 10): String
Creates a string representation of Frame
Creates a string representation of Frame
- nrows
Max number of rows to include
- ncols
Max number of rows to include
- def sum(implicit num: NUM[T]): Series[CX, T]
Sum of the elements of each column, ignoring NA values
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def tail(n: Int): Frame[RX, CX, T]
Extract last n rows
Extract last n rows
- n
number of rows to extract
- def tailCol(n: Int): Frame[RX, CX, T]
Extract last n columns
Extract last n columns
- n
number of columns to extract
- def toColSeq: IndexedSeq[(CX, Series[RX, T])]
Produce an indexed sequence of pairs of column index value and column Series.
- def toMat: Mat[T]
Extract the Mat embodied in the values of the Frame (dropping any indexing information)
- def toRowSeq: IndexedSeq[(RX, Series[CX, T])]
Produce an indexed sequence of pairs of row index value and row Series
- def toSeq: IndexedSeq[(RX, CX, T)]
Produce an indexed sequence of triples of values in the Frame in row-major order.
- def toString(): String
- Definition Classes
- Frame → AnyRef → Any
- def transform[U, SX](f: (Series[RX, T]) => Series[SX, U])(implicit arg0: ST[U], arg1: ST[SX], arg2: ORD[SX]): Frame[SX, CX, U]
Apply a function to each column series which results in another series (having possibly a different index); return new frame whose row index is the the full outer join of all the intermediately produced series (fast when all series have the same index), and having the original column index.
Apply a function to each column series which results in another series (having possibly a different index); return new frame whose row index is the the full outer join of all the intermediately produced series (fast when all series have the same index), and having the original column index.
- U
Type of values of result series of function
- SX
Type of index of result series of function
- f
Function to operate on each column as a series
- def unstack[O1, O2, V](implicit splt: Splitter[RX, O1, O2], stkr: Stacker[CX, O2, V], ord1: ORD[O1], ord2: ORD[O2], m1: ST[O1], m2: ST[O2]): Frame[O1, V, T]
Unstack pivots the innermost row labels to the innermost col labels.
Unstack pivots the innermost row labels to the innermost col labels. That is, it splits a row index of tuple keys of arity N into a new row index having arity N-1 and a remaining index R, and forms a new col index by stacking the existing col index with R. The resulting Frame has values as in the original Frame indexed by the corresponding keys.
For example:
scala> Frame(Series(Vec(1,2,3,4), Index(('a',1),('a',2),('b',1),('b',2))), Series(Vec(5,6,7,8), Index(('a',1),('a',2),('b',1),('b',2)))) res1: org.saddle.Frame[(Char, Int),Int,Int] = [4 x 2] 0 1 -- -- a 1 -> 1 5 2 -> 2 6 b 1 -> 3 7 2 -> 4 8 scala> res1.unstack res2: org.saddle.Frame[Char,(Int, Int),Int] = [2 x 4] 0 1 1 2 1 2 -- -- -- -- a -> 1 2 5 6 b -> 3 4 7 8
- O1
The N-1 arity row index type
- O2
The 1-arity type of split-out index R
- V
The type of the stacked col index
- splt
An implicit instance of Splitter to do the splitting
- stkr
An implicit instance of Stacker to do the stacking
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- def where(pred: Vec[Boolean]): Frame[RX, CX, T]
Create Frame whose rows satisfy the rule that their keys and values are chosen via a Vec[Boolean] or a Series[_, Boolean] predicate when the latter contains a true value.
Create Frame whose rows satisfy the rule that their keys and values are chosen via a Vec[Boolean] or a Series[_, Boolean] predicate when the latter contains a true value.
- pred
Series[_, Boolean] (or Vec[Boolean] which will implicitly convert)
- def where(pred: Series[_, Boolean]): Frame[RX, CX, T]
Create Frame whose rows satisfy the rule that their keys and values are chosen via a Vec[Boolean] or a Series[_, Boolean] predicate when the latter contains a true value.
Create Frame whose rows satisfy the rule that their keys and values are chosen via a Vec[Boolean] or a Series[_, Boolean] predicate when the latter contains a true value.
- pred
Series[_, Boolean] (or Vec[Boolean] which will implicitly convert)
- def withColIndex(row1: Int, row2: Int)(implicit ordT: ORD[T]): Frame[RX, (T, T), T]
Overloaded method to create hierarchical index from two rows.
- def withColIndex(row: Int)(implicit ordT: ORD[T]): Frame[RX, T, T]
Create a new Frame using the current values but with the new col index specified by the row at a particular offset, and with that row removed from the frame data body.
- def withRowIndex(col1: Int, col2: Int)(implicit ordT: ORD[T]): Frame[(T, T), CX, T]
Overloaded method to create hierarchical index from two cols.
- def withRowIndex(col: Int)(implicit ordT: ORD[T]): Frame[T, CX, T]
Create a new Frame using the current values but with the new row index specified by the column at a particular offset, and with that column removed from the frame data body.
- def xor[B, That](other: B)(implicit op: BinOp[XorOp, Frame[RX, CX, T], B, That]): That
Logical EXCLUSIVE OR
Logical EXCLUSIVE OR
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def |[B, That](other: B)(implicit op: BinOp[BitOr, Frame[RX, CX, T], B, That]): That
Bit-wise OR
Bit-wise OR
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps
- def ||[B, That](other: B)(implicit op: BinOp[OrOp, Frame[RX, CX, T], B, That]): That
Logical OR
Logical OR
- B
type of the other operand
- That
result type of operation
- other
other operand instance
- op
implicit evidence for operation between this and other
- Definition Classes
- NumericOps