DataFrameHistogrammarMethods

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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 Average(quantity: UserFcn[Row, Double]): Averaging[Row]

   

5. def Bag[RANGE](quantity: UserFcn[Row, RANGE], range: String = "")(implicit arg0: ClassTag[RANGE]): Bagging[Row, RANGE]

   

6. def Bin[V <: Container[V] with Aggregation { type Datum >: org.apache.spark.sql.Row }, U <: Container[U] with Aggregation { type Datum >: org.apache.spark.sql.Row }, O <: Container[O] with Aggregation { type Datum >: org.apache.spark.sql.Row }, N <: Container[N] with Aggregation { type Datum >: org.apache.spark.sql.Row }](num: Int, low: Double, high: Double, quantity: UserFcn[Row, Double], value: ⇒ V = Count(), underflow: U = Count(), overflow: O = Count(), nanflow: N = Count()): Binning[Row, V, U, O, N]

   

7. def Branch[C0 <: Container[C0] with Aggregation, C1 <: Container[C1] with Aggregation, C2 <: Container[C2] with Aggregation, C3 <: Container[C3] with Aggregation, C4 <: Container[C4] with Aggregation, C5 <: Container[C5] with Aggregation, C6 <: Container[C6] with Aggregation, C7 <: Container[C7] with Aggregation, C8 <: Container[C8] with Aggregation, C9 <: Container[C9] with Aggregation](i0: C0, i1: C1, i2: C2, i3: C3, i4: C4, i5: C5, i6: C6, i7: C7, i8: C8, i9: C9)(implicit e01: Compatible[C0, C1], e02: Compatible[C0, C2], e03: Compatible[C0, C3], e04: Compatible[C0, C4], e05: Compatible[C0, C5], e06: Compatible[C0, C6], e07: Compatible[C0, C7], e08: Compatible[C0, C8], e09: Compatible[C0, C9]): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

   

8. def Branch[C0 <: Container[C0] with Aggregation, C1 <: Container[C1] with Aggregation, C2 <: Container[C2] with Aggregation, C3 <: Container[C3] with Aggregation, C4 <: Container[C4] with Aggregation, C5 <: Container[C5] with Aggregation, C6 <: Container[C6] with Aggregation, C7 <: Container[C7] with Aggregation, C8 <: Container[C8] with Aggregation](i0: C0, i1: C1, i2: C2, i3: C3, i4: C4, i5: C5, i6: C6, i7: C7, i8: C8)(implicit e01: Compatible[C0, C1], e02: Compatible[C0, C2], e03: Compatible[C0, C3], e04: Compatible[C0, C4], e05: Compatible[C0, C5], e06: Compatible[C0, C6], e07: Compatible[C0, C7], e08: Compatible[C0, C8]): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

   

9. def Branch[C0 <: Container[C0] with Aggregation, C1 <: Container[C1] with Aggregation, C2 <: Container[C2] with Aggregation, C3 <: Container[C3] with Aggregation, C4 <: Container[C4] with Aggregation, C5 <: Container[C5] with Aggregation, C6 <: Container[C6] with Aggregation, C7 <: Container[C7] with Aggregation](i0: C0, i1: C1, i2: C2, i3: C3, i4: C4, i5: C5, i6: C6, i7: C7)(implicit e01: Compatible[C0, C1], e02: Compatible[C0, C2], e03: Compatible[C0, C3], e04: Compatible[C0, C4], e05: Compatible[C0, C5], e06: Compatible[C0, C6], e07: Compatible[C0, C7]): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

   

10. def Branch[C0 <: Container[C0] with Aggregation, C1 <: Container[C1] with Aggregation, C2 <: Container[C2] with Aggregation, C3 <: Container[C3] with Aggregation, C4 <: Container[C4] with Aggregation, C5 <: Container[C5] with Aggregation, C6 <: Container[C6] with Aggregation](i0: C0, i1: C1, i2: C2, i3: C3, i4: C4, i5: C5, i6: C6)(implicit e01: Compatible[C0, C1], e02: Compatible[C0, C2], e03: Compatible[C0, C3], e04: Compatible[C0, C4], e05: Compatible[C0, C5], e06: Compatible[C0, C6]): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

    

11. def Branch[C0 <: Container[C0] with Aggregation, C1 <: Container[C1] with Aggregation, C2 <: Container[C2] with Aggregation, C3 <: Container[C3] with Aggregation, C4 <: Container[C4] with Aggregation, C5 <: Container[C5] with Aggregation](i0: C0, i1: C1, i2: C2, i3: C3, i4: C4, i5: C5)(implicit e01: Compatible[C0, C1], e02: Compatible[C0, C2], e03: Compatible[C0, C3], e04: Compatible[C0, C4], e05: Compatible[C0, C5]): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

    

12. def Branch[C0 <: Container[C0] with Aggregation, C1 <: Container[C1] with Aggregation, C2 <: Container[C2] with Aggregation, C3 <: Container[C3] with Aggregation, C4 <: Container[C4] with Aggregation](i0: C0, i1: C1, i2: C2, i3: C3, i4: C4)(implicit e01: Compatible[C0, C1], e02: Compatible[C0, C2], e03: Compatible[C0, C3], e04: Compatible[C0, C4]): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

    

13. def Branch[C0 <: Container[C0] with Aggregation, C1 <: Container[C1] with Aggregation, C2 <: Container[C2] with Aggregation, C3 <: Container[C3] with Aggregation](i0: C0, i1: C1, i2: C2, i3: C3)(implicit e01: Compatible[C0, C1], e02: Compatible[C0, C2], e03: Compatible[C0, C3]): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

    

14. def Branch[C0 <: Container[C0] with Aggregation, C1 <: Container[C1] with Aggregation, C2 <: Container[C2] with Aggregation](i0: C0, i1: C1, i2: C2)(implicit e01: Compatible[C0, C1], e02: Compatible[C0, C2]): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

    

15. def Branch[C0 <: Container[C0] with Aggregation, C1 <: Container[C1] with Aggregation](i0: C0, i1: C1)(implicit e01: Compatible[C0, C1]): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

    

16. def Branch[C0 <: Container[C0] with Aggregation](i0: C0): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

    

17. def Categorize[V <: Container[V] with Aggregation { type Datum >: org.apache.spark.sql.Row }](quantity: UserFcn[Row, String], value: ⇒ V = Count()): Categorizing[Row, V]

    

18. def CentrallyBin[V <: Container[V] with Aggregation { type Datum >: org.apache.spark.sql.Row }, N <: Container[N] with Aggregation { type Datum >: org.apache.spark.sql.Row }](bins: Iterable[Double], quantity: UserFcn[Row, Double], value: ⇒ V = Count(), nanflow: N = Count()): CentrallyBinning[Row, V, N]

    

19. def Count(transform: UserFcn[Double, Double] = ...): CONTAINER forSome {type CONTAINER <: Container[CONTAINER] with Aggregation { type Datum = org.apache.spark.sql.Row }}

    

20. def Deviate(quantity: UserFcn[Row, Double]): Deviating[Row]

    

21. def Fraction[V <: Container[V] with Aggregation { type Datum >: org.apache.spark.sql.Row }](quantity: UserFcn[Row, Double], value: ⇒ V = Count()): Fractioning[Row, V]

    

22. def Index[V <: Container[V] with Aggregation](values: V*): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

    

23. def IrregularlyBin[V <: Container[V] with Aggregation { type Datum >: org.apache.spark.sql.Row }, N <: Container[N] with Aggregation { type Datum >: org.apache.spark.sql.Row }](bins: Iterable[Double], quantity: UserFcn[Row, Double], value: ⇒ V = Count(), nanflow: N = Count()): IrregularlyBinning[Row, V, N]

    

24. def Label[V <: Container[V] with Aggregation](pairs: (String, V)*): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

    

25. def Maximize(quantity: UserFcn[Row, Double]): Maximizing[Row]

    

26. def Minimize(quantity: UserFcn[Row, Double]): Minimizing[Row]

    

27. def Select[V <: Container[V] with Aggregation { type Datum >: org.apache.spark.sql.Row }](quantity: UserFcn[Row, Double], cut: V = Count()): Selecting[Row, V]

    

28. def SparselyBin[V <: Container[V] with Aggregation { type Datum >: org.apache.spark.sql.Row }, N <: Container[N] with Aggregation { type Datum >: org.apache.spark.sql.Row }](binWidth: Double, quantity: UserFcn[Row, Double], value: ⇒ V = Count(), nanflow: N = Count(), origin: Double = 0.0): SparselyBinning[Row, V, N]

    

29. def Stack[V <: Container[V] with Aggregation { type Datum >: org.apache.spark.sql.Row }, N <: Container[N] with Aggregation { type Datum >: org.apache.spark.sql.Row }](bins: Iterable[Double], quantity: UserFcn[Row, Double], value: ⇒ V = Count(), nanflow: N = Count()): Stacking[Row, V, N]

    

30. def Sum(quantity: UserFcn[Row, Double]): Summing[Row]

    

31. def UntypedLabel[F <: Container[F] with Aggregation](first: (String, F), rest: (String, Container[_] with Aggregation)*): C forSome {type C <: Container[C] with Aggregation { type Datum = org.apache.spark.sql.Row }}

    

32. final def asInstanceOf[T0]: T0

    

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33. def clone(): AnyRef

    

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

    

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35. def equals(arg0: Any): Boolean

    

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36. def finalize(): Unit

    

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

    

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38. def hashCode(): Int

    

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39. def histogrammar[CONTAINER <: Container[CONTAINER] with Aggregation { type Datum = org.apache.spark.sql.Row }](container: CONTAINER)(implicit arg0: ClassTag[CONTAINER]): CONTAINER

    

40. final def isInstanceOf[T0]: Boolean

    

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

    

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42. final def notify(): Unit

    

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43. final def notifyAll(): Unit

    

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

    

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45. def toString(): String

    

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46. final def wait(): Unit

    

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

    

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

    

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