saddle-stats/org.saddle.stats/FrameStats

FrameStats

class FrameStats[RX, CX, T](frame: Frame[RX, CX, T])(implicit evidence$1: ScalarTag[T])

Statistical methods made available to operate on columns of a Frame via enrichment.

Type parameters:
CX

Type of the column index elements

RX

Type of the row index elements

T

Type of the elements of the frame

Value parameters:
frame

The frame to enrich

class Object
trait Matchable
class Any

Type members

Types

type S2Stats = () => T

Value members

Concrete methods

def countif(test: T => Boolean): Series[CX, Int]

Conditional count of the elements of each column, ignoring NA values

Conditional count of the elements of each column, ignoring NA values

Value parameters:
test

Function predicate to utilize in count, T => Boolean

def cumCount(implicit ev: Vec[T] => VecExpandingStats[T]): Frame[RX, CX, Int]

Cumulative count for each column; each successive element of the output is the cumulative count from the initial element, ignoring NAs.

Cumulative count for each column; each successive element of the output is the cumulative count from the initial element, ignoring NAs.

def cumMax(implicit ev: Vec[T] => VecExpandingStats[T]): Frame[RX, CX, T]

Cumulative max for each column; each successive element of the output is the cumulative max from the initial element, ignoring NAs.

Cumulative max for each column; each successive element of the output is the cumulative max from the initial element, ignoring NAs.

def cumMin(implicit ev: Vec[T] => VecExpandingStats[T]): Frame[RX, CX, T]

Cumulative min for each column; each successive element of the output is the cumulative min from the initial element, ignoring NAs.

Cumulative min for each column; each successive element of the output is the cumulative min from the initial element, ignoring NAs.

def cumProd(implicit ev: Vec[T] => VecExpandingStats[T]): Frame[RX, CX, T]

Cumulative product for each column; each successive element of the output is the cumulative product from the initial element, ignoring NAs.

Cumulative product for each column; each successive element of the output is the cumulative product from the initial element, ignoring NAs.

def cumSum(implicit ev: Vec[T] => VecExpandingStats[T]): Frame[RX, CX, T]

Cumulative sum for each column; each successive element of the output is the cumulative sum from the initial element, ignoring NAs.

Cumulative sum for each column; each successive element of the output is the cumulative sum from the initial element, ignoring NAs.

def demeaned(implicit ev: Vec[T] => VecStats[T]): Frame[RX, CX, Double]

Demean each column in the frame

Demean each column in the frame

def geomean(implicit ev: S2Stats): Series[CX, Double]

Geometric mean of each column

Geometric mean of each column

def kurt(implicit ev: S2Stats): Series[CX, Double]

Sample kurtosis of each column

Sample kurtosis of each column

def logsum(implicit ev: S2Stats): Series[CX, Double]

Sum of the natural logs of the elements of each column, ignoring NA values.

Sum of the natural logs of the elements of each column, ignoring NA values.

def rollingCount(winSz: Int)(implicit ev: Vec[T] => VecRollingStats[T]): Frame[RX, CX, Int]

Rolling count; compute count of number of elements in columns of Frame over a sliding window, ignoring any NA values.

Rolling count; compute count of number of elements in columns of Frame over a sliding window, ignoring any NA values.

Value parameters:
winSz

Size of the rolling window

def rollingMean(winSz: Int)(implicit ev: Vec[T] => VecRollingStats[T]): Frame[RX, CX, Double]

Rolling mean; compute mean of elements in columns of Frame over a sliding window, ignoring any NA values.

Rolling mean; compute mean of elements in columns of Frame over a sliding window, ignoring any NA values.

Value parameters:
winSz

Size of the sliding window

def rollingMedian(winSz: Int)(implicit ev: Vec[T] => VecRollingStats[T]): Frame[RX, CX, Double]

Rolling median; compute median of elements in columns of Frame over a sliding window, ignoring any NA values.

Rolling median; compute median of elements in columns of Frame over a sliding window, ignoring any NA values.

Value parameters:
winSz

Size of the sliding window

def rollingSum(winSz: Int)(implicit ev: Vec[T] => VecRollingStats[T]): Frame[RX, CX, T]

Rolling sum; compute sum of elements in columns of Frame over a sliding window, ignoring any NA values.

Rolling sum; compute sum of elements in columns of Frame over a sliding window, ignoring any NA values.

Value parameters:
winSz

Size of the sliding window

def skew(implicit ev: S2Stats): Series[CX, Double]

Sample skewness of each column

Sample skewness of each column

def stdev(implicit ev: S2Stats): Series[CX, Double]

Sample standard deviation of each column

Sample standard deviation of each column

def variance(implicit ev: S2Stats): Series[CX, Double]

Sample variance of each column

Sample variance of each column