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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.  final def asInstanceOf[T0]: T0

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5.  def calculateCov(df: DataFrame, cols: Seq[String]): Double

   Calculate the covariance of two numerical columns of a DataFrame.

   Calculate the covariance of two numerical columns of a DataFrame.

   df

   The DataFrame

   cols

   the column names

   returns

   the covariance of the two columns.

6.  def clone(): AnyRef

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7.  def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame

   Generate a table of frequencies for the elements of two columns.

8.  final def eq(arg0: AnyRef): Boolean

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9.  def equals(arg0: Any): Boolean

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10.  def finalize(): Unit

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11.  final def getClass(): Class[_]

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12.  def hashCode(): Int

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13.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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14.  def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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15.  final def isInstanceOf[T0]: Boolean

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16.  def isTraceEnabled(): Boolean

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17.  def log: Logger

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18.  def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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19.  def logDebug(msg: ⇒ String): Unit

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20.  def logError(msg: ⇒ String, throwable: Throwable): Unit

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21.  def logError(msg: ⇒ String): Unit

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22.  def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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23.  def logInfo(msg: ⇒ String): Unit

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24.  def logName: String

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25.  def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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26.  def logTrace(msg: ⇒ String): Unit

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27.  def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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28.  def logWarning(msg: ⇒ String): Unit

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29.  def multipleApproxQuantiles(df: DataFrame, cols: Seq[String], probabilities: Seq[Double], relativeError: Double): Seq[Seq[Double]]

    Calculates the approximate quantiles of multiple numerical columns of a DataFrame in one pass.

    Calculates the approximate quantiles of multiple numerical columns of a DataFrame in one pass.

    The result of this algorithm has the following deterministic bound: If the DataFrame has N elements and if we request the quantile at probability p up to error err, then the algorithm will return a sample x from the DataFrame so that the *exact* rank of x is close to (p * N). More precisely,

    floor((p - err) * N) <= rank(x) <= ceil((p + err) * N).

    This method implements a variation of the Greenwald-Khanna algorithm (with some speed optimizations). The algorithm was first present in Space-efficient Online Computation of Quantile Summaries by Greenwald and Khanna.

    df

    the dataframe

    cols

    numerical columns of the dataframe

    probabilities

    a list of quantile probabilities Each number must belong to [0, 1]. For example 0 is the minimum, 0.5 is the median, 1 is the maximum.

    relativeError

    The relative target precision to achieve (greater than or equal 0). If set to zero, the exact quantiles are computed, which could be very expensive. Note that values greater than 1 are accepted but give the same result as 1.

    returns

    for each column, returns the requested approximations

    Note

    null and NaN values will be ignored in numerical columns before calculation. For a column only containing null or NaN values, an empty array is returned.

30.  final def ne(arg0: AnyRef): Boolean

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31.  final def notify(): Unit

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32.  final def notifyAll(): Unit

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33.  def pearsonCorrelation(df: DataFrame, cols: Seq[String]): Double

    Calculate the Pearson Correlation Coefficient for the given columns

34.  def summary(ds: Dataset[_], statistics: Seq[String]): DataFrame

    Calculate selected summary statistics for a dataset

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

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36.  def toString(): String

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37.  final def wait(): Unit

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

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39.  final def wait(arg0: Long): Unit

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