org.saddle.stats

FrameStats

class FrameStats[RX, CX, T] extends AnyRef

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

RX

Type of the row index elements

CX

Type of the column index elements

T

Type of the elements of the frame

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Instance Constructors

  1. new FrameStats(frame: Frame[RX, CX, T])(implicit arg0: ST[T])

    frame

    The frame to enrich

Type Members

  1. type S2Stats = (org.saddle.Series[_, T]) ⇒ VecStats[T]

Value Members

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

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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  8. def count(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, Int]

    Count of the elements of each column, ignoring NA values

  9. def countif(test: (T) ⇒ Boolean)(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): 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

    test

    Function predicate to utilize in count, T => Boolean

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. def demeaned(implicit ev: (Vec[T]) ⇒ VecStats[T]): Frame[RX, CX, Double]

    Demean each column in the frame

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

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  17. def equals(arg0: Any): Boolean

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  18. def finalize(): Unit

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  19. def geomean(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, Double]

    Geometric mean of each column

  20. final def getClass(): java.lang.Class[_]

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  21. def hashCode(): Int

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  22. final def isInstanceOf[T0]: Boolean

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  23. def kurt(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, Double]

    Sample kurtosis of each column

  24. def logsum(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, Double]

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

  25. def max(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, T]

    Max of the elements of each column, ignoring NA values

  26. def mean(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, Double]

    Sample mean of each column

  27. def median(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, Double]

    Median of each column

  28. def min(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, T]

    Min of the elements of each column, ignoring NA values

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

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  30. final def notify(): Unit

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  31. final def notifyAll(): Unit

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  32. def prod(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, T]

    Product of the elements of each column, ignoring NA values

  33. 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.

    winSz

    Size of the rolling window

  34. 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.

    winSz

    Size of the sliding window

  35. 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.

    winSz

    Size of the sliding window

  36. 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.

    winSz

    Size of the sliding window

  37. def skew(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, Double]

    Sample skewness of each column

  38. def stdev(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, Double]

    Sample standard deviation of each column

  39. def sum(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, T]

    Sum of the elements of each column, ignoring NA values

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

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  41. def toString(): String

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  42. def variance(implicit ev: (org.saddle.Series[_, T]) ⇒ VecStats[T]): Series[CX, Double]

    Sample variance of each column

  43. final def wait(): Unit

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

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

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