com.snowflake.snowpark_java

Class DataFrameStatFunctions

java.lang.Object

com.snowflake.snowpark_java.DataFrameStatFunctions

public class DataFrameStatFunctions
extends Object

Provides eagerly computed statistical functions for DataFrames.

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

Since:

1.1.0

Method Summary

Modifier and Type	Method and Description
Optional<Double>[][]	approxQuantile(String[] cols, double[] percentile) 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.
Optional<Double>[]	approxQuantile(String col, double[] percentile) For a specified numeric column and an array of desired quantiles, returns an approximate value for the column at each of the desired quantiles.
Optional<Double>	corr(String col1, String col2) Calculates the correlation coefficient for non-null pairs in two numeric columns.
Optional<Double>	cov(String col1, String col2) Calculates the sample covariance for non-null pairs in two numeric columns.
DataFrame	crosstab(String col1, String col2) Computes a pair-wise frequency table (a ''contingency table'') for the specified columns.
DataFrame	sampleBy(Column col, Map<?,Double> fractions) Returns a DataFrame containing a stratified sample without replacement, based on a Map that specifies the fraction for each stratum.
DataFrame	sampleBy(String colName, Map<?,Double> fractions) Returns a DataFrame containing a stratified sample without replacement, based on a Map that specifies the fraction for each stratum.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

corr

public Optional<Double> corr(String col1,
                             String col2)

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

Parameters:

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:

1.1.0

cov

public Optional<Double> cov(String col1,
                            String col2)

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

Parameters:

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:

1.1.0

approxQuantile

public Optional<Double>[] approxQuantile(String col,
                                         double[] percentile)

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.

Parameters:

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:

1.1.0

approxQuantile

public Optional<Double>[][] approxQuantile(String[] cols,
                                           double[] percentile)

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.

Parameters:

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:

1.1.0

crosstab

public DataFrame crosstab(String col1,
                          String col2)

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.

Parameters:

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:

1.1.0

sampleBy

public DataFrame sampleBy(Column col,
                          Map<?,Double> fractions)

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

Parameters:

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:

1.1.0

sampleBy

public DataFrame sampleBy(String colName,
                          Map<?,Double> fractions)

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

Parameters:

colName - 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:

1.1.0