Snowpark Scala API Reference1.19.0 < Back
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-  package snowpark
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-  package types

  This package contains all Snowpark logical types.

  This package contains all Snowpark logical types.

  Definition Classes
    snowpark
  Since

    0.1.0

-  package udaf
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    snowpark
-  package udtf
  Definition Classes
    snowpark
- AggregateFunction
- AsyncJob
- CaseExpr
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c

com.snowflake.snowpark

# DataFrameStatFunctions 

###

#### final class DataFrameStatFunctions extends Logging

Provides eagerly computed statistical functions for DataFrames.

To access an object of this class, use DataFrame.stat.

Since

  0.2.0

Linear Supertypes
Logging, AnyRef, Any
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1. DataFrameStatFunctions
2. Logging
3. AnyRef
4. Any

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### Value Members

1.  final def !=(arg0: Any): Boolean
   Definition Classes
     AnyRef → Any
2.  final def ##(): Int
   Definition Classes
     AnyRef → Any
3.  final def ==(arg0: Any): Boolean
   Definition Classes
     AnyRef → Any
4.  def action[T](funcName: String)(func: ⇒ T): T
   Attributes
     protected
   Annotations
     @inline()
5.  def approxQuantile(cols: Array[String], percentile: Array[Double]): Array[Array[Option[Double]]]

   For an array of numeric columns and an array of desired quantiles, returns a matrix of approximate values for each column at each of the desired quantiles.

   For an array of numeric columns and an array of desired quantiles, returns a matrix of approximate values for each column at each of the desired quantiles. For example, `result(0)(1)` contains the approximate value for column `cols(0)` at quantile `percentile(1)`.

   This function uses the t-Digest algorithm.

   For example, the following code:

       import session.implicits._
       val df = Seq((0.1, 0.5), (0.2, 0.6), (0.3, 0.7)).toDF("a", "b")
       val res = double2.stat.approxQuantile(Array("a", "b"), Array(0, 0.1, 0.6))

   prints out the following result:

       res: Array(Array(Some(0.05), Some(0.15000000000000002), Some(0.25)),
                  Array(Some(0.45), Some(0.55), Some(0.6499999999999999)))

   cols

     An array of column names.

   percentile

     An array of double values greater than or equal to 0.0 and less than 1.0.

   returns

     A matrix with the dimensions `(cols.size * percentile.size)` containing the approximate percentile values. If there is not enough data to calculate the quantile, the method returns None.

   Since

     0.2.0

6.  def approxQuantile(col: String, percentile: Array[Double]): Array[Option[Double]]

   For a specified numeric column and an array of desired quantiles, returns an approximate value for the column at each of the desired quantiles.

   For a specified numeric column and an array of desired quantiles, returns an approximate value for the column at each of the desired quantiles.

   This function uses the t-Digest algorithm.

   For example, the following code:

       import session.implicits._
       val df = Seq(1, 2, 3, 4, 5, 6, 7, 8, 9, 0).toDF("a")
       val res = df.stat.approxQuantile("a", Array(0, 0.1, 0.4, 0.6, 1))

   prints out the following result:

       res: Array(Some(-0.5), Some(0.5), Some(3.5), Some(5.5), Some(9.5))

   col

     The name of the numeric column.

   percentile

     An array of double values greater than or equal to 0.0 and less than 1.0.

   returns

     An array of approximate percentile values, If there is not enough data to calculate the quantile, the method returns None.

   Since

     0.2.0

7.  final def asInstanceOf[T0]: T0
   Definition Classes
     Any
8.  def clone(): AnyRef
   Attributes
     protected[lang]
   Definition Classes
     AnyRef
   Annotations
     @throws( ... ) @native() @HotSpotIntrinsicCandidate()
9.  def corr(col1: String, col2: String): Option[Double]

   Calculates the correlation coefficient for non-null pairs in two numeric columns.

   Calculates the correlation coefficient for non-null pairs in two numeric columns.

   For example, the following code:

       import session.implicits._
       val df = Seq((0.1, 0.5), (0.2, 0.6), (0.3, 0.7)).toDF("a", "b")
       double res = df.stat.corr("a", "b").get

   prints out the following result:

       res: 0.9999999999999991

   col1

     The name of the first numeric column to use.

   col2

     The name of the second numeric column to use.

   returns

     The correlation of the two numeric columns. If there is not enough data to generate the correlation, the method returns None.

   Since

     0.2.0

10.  def cov(col1: String, col2: String): Option[Double]

    Calculates the sample covariance for non-null pairs in two numeric columns.

    Calculates the sample covariance for non-null pairs in two numeric columns.

    For example, the following code:

        import session.implicits._
        val df = Seq((0.1, 0.5), (0.2, 0.6), (0.3, 0.7)).toDF("a", "b")
        double res = df.stat.cov("a", "b").get

    prints out the following result:

        res: 0.010000000000000037

    col1

      The name of the first numeric column to use.

    col2

      The name of the second numeric column to use.

    returns

      The sample covariance of the two numeric columns, If there is not enough data to generate the covariance, the method returns None.

    Since

      0.2.0

11.  def crosstab(col1: String, col2: String): DataFrame

    Computes a pair-wise frequency table (a contingency table) for the specified columns.

    Computes a pair-wise frequency table (a contingency table) for the specified columns. The method returns a DataFrame containing this table.

    In the returned contingency table:

    - The first column of each row contains the distinct values of `col1` .
    - The name of the first column is the name of `col1` .
    - The rest of the column names are the distinct values of `col2` .
    - The counts are returned as Longs.
    - For pairs that have no occurrences, the contingency table contains 0 as the count.

    Note: The number of distinct values in `col2` should not exceed 1000.

    For example, the following code:

        import session.implicits._
        val df = Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2), (3, 3)).toDF("key", "value")
        val ct = df.stat.crosstab("key", "value")
        ct.show()

    prints out the following result:

        ---------------------------------------------------------------------------------------------
        |"KEY"  |"CAST(1 AS NUMBER(38,0))"  |"CAST(2 AS NUMBER(38,0))"  |"CAST(3 AS NUMBER(38,0))"  |
        ---------------------------------------------------------------------------------------------
        |1      |1                          |1                          |0                          |
        |2      |2                          |0                          |1                          |
        |3      |0                          |1                          |1                          |
        ---------------------------------------------------------------------------------------------

    col1

      The name of the first column to use.

    col2

      The name of the second column to use.

    returns

      A DataFrame containing the contingency table.

    Since

      0.2.0

12.  final def eq(arg0: AnyRef): Boolean
    Definition Classes
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13.  def equals(arg0: Any): Boolean
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      AnyRef → Any
14.  final def getClass(): Class[_]
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      @native() @HotSpotIntrinsicCandidate()
15.  def hashCode(): Int
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16.  final def isInstanceOf[T0]: Boolean
    Definition Classes
      Any
17.  def log(): Logger
    Attributes
      protected[internal]
    Definition Classes
      Logging
18.  def logDebug(msg: String, throwable: Throwable): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
19.  def logDebug(msg: String): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
20.  def logError(msg: String, throwable: Throwable): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
21.  def logError(msg: String): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
22.  def logInfo(msg: String, throwable: Throwable): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
23.  def logInfo(msg: String): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
24.  def logTrace(msg: String, throwable: Throwable): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
25.  def logTrace(msg: String): Unit
    Attributes
      protected[internal]
    Definition Classes
      Logging
26.  def logWarning(msg: String, throwable: Throwable): Unit
    Attributes
      protected[internal]
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27.  def logWarning(msg: String): Unit
    Attributes
      protected[internal]
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28.  final def ne(arg0: AnyRef): Boolean
    Definition Classes
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29.  final def notify(): Unit
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      @native() @HotSpotIntrinsicCandidate()
30.  final def notifyAll(): Unit
    Definition Classes
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      @native() @HotSpotIntrinsicCandidate()
31.  def sampleBy[T](col: String, fractions: Map[T, Double]): DataFrame

    Returns a DataFrame containing a stratified sample without replacement, based on a Map that specifies the fraction for each stratum.

    Returns a DataFrame containing a stratified sample without replacement, based on a Map that specifies the fraction for each stratum.

    For example, the following code:

        import session.implicits._
        val df = Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 12)).toDF("name", "age")
        val fractions = Map("Bob" -> 0.5, "Nico" -> 1.0)
        df.stat.sampleBy("name", fractions).show()

    prints out the following result:

        ------------------
        |"NAME"  |"AGE"  |
        ------------------
        |Bob     |17     |
        |Nico    |8      |
        ------------------

    T

      The type of the stratum.

    col

      The name of the column that defines the strata.

    fractions

      A Map that specifies the fraction to use for the sample for each stratum. If a stratum is not specified in the Map, the method uses 0 as the fraction.

    returns

      A new DataFrame that contains the stratified sample.

    Since

      0.2.0

32.  def sampleBy[T](col: Column, fractions: Map[T, Double]): DataFrame

    Returns a DataFrame containing a stratified sample without replacement, based on a Map that specifies the fraction for each stratum.

    Returns a DataFrame containing a stratified sample without replacement, based on a Map that specifies the fraction for each stratum.

    For example, the following code:

        import session.implicits._
        val df = Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 12)).toDF("name", "age")
        val fractions = Map("Bob" -> 0.5, "Nico" -> 1.0)
        df.stat.sampleBy(col("name"), fractions).show()

    prints out the following result:

        ------------------
        |"NAME"  |"AGE"  |
        ------------------
        |Bob     |17     |
        |Nico    |8      |
        ------------------

    T

      The type of the stratum.

    col

      An expression for the column that defines the strata.

    fractions

      A Map that specifies the fraction to use for the sample for each stratum. If a stratum is not specified in the Map, the method uses 0 as the fraction.

    returns

      A new DataFrame that contains the stratified sample.

    Since

      0.2.0

33.  final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
      AnyRef
34.  def toString(): String
    Definition Classes
      AnyRef → Any
35.  def transformation(funcName: String)(func: ⇒ DataFrame): DataFrame
    Attributes
      protected
    Annotations
      @inline()
36.  final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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      @throws( ... )
37.  final def wait(arg0: Long): Unit
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      @throws( ... ) @native()
38.  final def wait(): Unit
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### Deprecated Value Members

1.  def finalize(): Unit
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     @throws( classOf[java.lang.Throwable] ) @Deprecated
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### Inherited from Logging

### Inherited from AnyRef

### Inherited from Any

### Ungrouped