Spark 3.2.2 ScalaDoc - org.apache.spark.sql.DataFrameStatFunctions

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def approxQuantile(cols: Array[String], probabilities: Array[Double], relativeError: Double): Array[Array[Double]]

Calculates the approximate quantiles of numerical columns of a DataFrame.

cols: the names of the numerical columns
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 to 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: the approximate quantiles at the given probabilities of each column

Since: 2.2.0
Note: null and NaN values will be ignored in numerical columns before calculation. For columns only containing null or NaN values, an empty array is returned.
See also: approxQuantile(col:Str* approxQuantile) for detailed description.

def approxQuantile(col: String, probabilities: Array[Double], relativeError: Double): Array[Double]

Calculates the approximate quantiles of a numerical column of a DataFrame.

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.

col: the name of the numerical column
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 to 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: the approximate quantiles at the given probabilities

Since: 2.0.0
Note: null and NaN values will be removed from the numerical column before calculation. If the dataframe is empty or the column only contains null or NaN, an empty array is returned.

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def bloomFilter(col: Column, expectedNumItems: Long, numBits: Long): BloomFilter

Builds a Bloom filter over a specified column.

col: the column over which the filter is built
expectedNumItems: expected number of items which will be put into the filter.
numBits: expected number of bits of the filter.

Since: 2.0.0

def bloomFilter(colName: String, expectedNumItems: Long, numBits: Long): BloomFilter

Builds a Bloom filter over a specified column.

colName: name of the column over which the filter is built
expectedNumItems: expected number of items which will be put into the filter.
numBits: expected number of bits of the filter.

Since: 2.0.0

def bloomFilter(col: Column, expectedNumItems: Long, fpp: Double): BloomFilter

Builds a Bloom filter over a specified column.

col: the column over which the filter is built
expectedNumItems: expected number of items which will be put into the filter.
fpp: expected false positive probability of the filter.

Since: 2.0.0

def bloomFilter(colName: String, expectedNumItems: Long, fpp: Double): BloomFilter

Builds a Bloom filter over a specified column.

colName: name of the column over which the filter is built
expectedNumItems: expected number of items which will be put into the filter.
fpp: expected false positive probability of the filter.

Since: 2.0.0

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def corr(col1: String, col2: String): Double

Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.

col1

the name of the column

col2

the name of the column to calculate the correlation against

returns

The Pearson Correlation Coefficient as a Double.

val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
  .withColumn("rand2", rand(seed=27))
df.stat.corr("rand1", "rand2", "pearson")
res1: Double = 0.613...

Since: 1.4.0

def corr(col1: String, col2: String, method: String): Double

Calculates the correlation of two columns of a DataFrame.

Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in MLlib's Statistics.

col1

the name of the column

col2

the name of the column to calculate the correlation against

returns

The Pearson Correlation Coefficient as a Double.

val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
  .withColumn("rand2", rand(seed=27))
df.stat.corr("rand1", "rand2")
res1: Double = 0.613...

Since: 1.4.0

def countMinSketch(col: Column, eps: Double, confidence: Double, seed: Int): CountMinSketch

Builds a Count-min Sketch over a specified column.

col: the column over which the sketch is built
eps: relative error of the sketch
confidence: confidence of the sketch
seed: random seed
returns: a CountMinSketch over column colName

Since: 2.0.0

def countMinSketch(col: Column, depth: Int, width: Int, seed: Int): CountMinSketch

Builds a Count-min Sketch over a specified column.

col: the column over which the sketch is built
depth: depth of the sketch
width: width of the sketch
seed: random seed
returns: a CountMinSketch over column colName

Since: 2.0.0

def countMinSketch(colName: String, eps: Double, confidence: Double, seed: Int): CountMinSketch

Builds a Count-min Sketch over a specified column.

colName: name of the column over which the sketch is built
eps: relative error of the sketch
confidence: confidence of the sketch
seed: random seed
returns: a CountMinSketch over column colName

Since: 2.0.0

def countMinSketch(colName: String, depth: Int, width: Int, seed: Int): CountMinSketch

Builds a Count-min Sketch over a specified column.

colName: name of the column over which the sketch is built
depth: depth of the sketch
width: width of the sketch
seed: random seed
returns: a CountMinSketch over column colName

Since: 2.0.0

def cov(col1: String, col2: String): Double

Calculate the sample covariance of two numerical columns of a DataFrame.

col1

the name of the first column

col2

the name of the second column

returns

the covariance of the two columns.

val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
  .withColumn("rand2", rand(seed=27))
df.stat.cov("rand1", "rand2")
res1: Double = 0.065...

Since: 1.4.0

def crosstab(col1: String, col2: String): DataFrame

Computes a pair-wise frequency table of the given columns.

Computes a pair-wise frequency table of the given columns. Also known as a contingency table. The number of distinct values for each column should be less than 1e4. At most 1e6 non-zero pair frequencies will be returned. The first column of each row will be the distinct values of col1 and the column names will be the distinct values of col2. The name of the first column will be col1_col2. Counts will be returned as Longs. Pairs that have no occurrences will have zero as their counts. Null elements will be replaced by "null", and back ticks will be dropped from elements if they exist.

col1

The name of the first column. Distinct items will make the first item of each row.

col2

The name of the second column. Distinct items will make the column names of the DataFrame.

returns

A DataFrame containing for the contingency table.

val df = spark.createDataFrame(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()
+---------+---+---+---+
|key_value|  1|  2|  3|
+---------+---+---+---+
|        2|  2|  0|  1|
|        1|  1|  1|  0|
|        3|  0|  1|  1|
+---------+---+---+---+

Since: 1.4.0

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def freqItems(cols: Seq[String]): DataFrame

(Scala-specific) Finding frequent items for columns, possibly with false positives.

(Scala-specific) Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in here, proposed by Karp, Schenker, and Papadimitriou. Uses a default support of 1%.

This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting DataFrame.

cols: the names of the columns to search frequent items in.
returns: A Local DataFrame with the Array of frequent items for each column.

Since: 1.4.0

def freqItems(cols: Seq[String], support: Double): DataFrame

(Scala-specific) Finding frequent items for columns, possibly with false positives.

(Scala-specific) Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in here, proposed by Karp, Schenker, and Papadimitriou.

This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting DataFrame.

cols

the names of the columns to search frequent items in.

returns

A Local DataFrame with the Array of frequent items for each column.

val rows = Seq.tabulate(100) { i =>
  if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
}
val df = spark.createDataFrame(rows).toDF("a", "b")
// find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
// "a" and "b"
val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
freqSingles.show()
+-----------+-------------+
|a_freqItems|  b_freqItems|
+-----------+-------------+
|    [1, 99]|[-1.0, -99.0]|
+-----------+-------------+
// find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
val pairDf = df.select(struct("a", "b").as("a-b"))
val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+----------+
|   freq_ab|
+----------+
|  [1,-1.0]|
|   ...    |
+----------+

Since: 1.4.0

def freqItems(cols: Array[String]): DataFrame

Finding frequent items for columns, possibly with false positives.

Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in here, proposed by Karp, Schenker, and Papadimitriou. Uses a default support of 1%.

This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting DataFrame.

cols: the names of the columns to search frequent items in.
returns: A Local DataFrame with the Array of frequent items for each column.

Since: 1.4.0

def freqItems(cols: Array[String], support: Double): DataFrame

Finding frequent items for columns, possibly with false positives.

Finding frequent items for columns, possibly with false positives. Using the frequent element count algorithm described in here, proposed by Karp, Schenker, and Papadimitriou. The support should be greater than 1e-4.

This function is meant for exploratory data analysis, as we make no guarantee about the backward compatibility of the schema of the resulting DataFrame.

cols

the names of the columns to search frequent items in.

support

The minimum frequency for an item to be considered frequent. Should be greater than 1e-4.

returns

A Local DataFrame with the Array of frequent items for each column.

val rows = Seq.tabulate(100) { i =>
  if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
}
val df = spark.createDataFrame(rows).toDF("a", "b")
// find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
// "a" and "b"
val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
freqSingles.show()
+-----------+-------------+
|a_freqItems|  b_freqItems|
+-----------+-------------+
|    [1, 99]|[-1.0, -99.0]|
+-----------+-------------+
// find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
val pairDf = df.select(struct("a", "b").as("a-b"))
val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+----------+
|   freq_ab|
+----------+
|  [1,-1.0]|
|   ...    |
+----------+

Since: 1.4.0

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def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame

(Java-specific) Returns a stratified sample without replacement based on the fraction given on each stratum.

T: stratum type
col: column that defines strata
fractions: sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as zero.
seed: random seed
returns: a new DataFrame that represents the stratified sample

Since: 3.0.0

def sampleBy[T](col: Column, fractions: Map[T, Double], seed: Long): DataFrame

Returns a stratified sample without replacement based on the fraction given on each stratum.

T

stratum type

col

column that defines strata

fractions

sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as zero.

seed

random seed

returns

a new DataFrame that represents the stratified sample The stratified sample can be performed over multiple columns:

import org.apache.spark.sql.Row
import org.apache.spark.sql.functions.struct

val df = spark.createDataFrame(Seq(("Bob", 17), ("Alice", 10), ("Nico", 8), ("Bob", 17),
  ("Alice", 10))).toDF("name", "age")
val fractions = Map(Row("Alice", 10) -> 0.3, Row("Nico", 8) -> 1.0)
df.stat.sampleBy(struct($"name", $"age"), fractions, 36L).show()
+-----+---+
| name|age|
+-----+---+
| Nico|  8|
|Alice| 10|
+-----+---+

Since: 3.0.0

def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame

Returns a stratified sample without replacement based on the fraction given on each stratum.

T: stratum type
col: column that defines strata
fractions: sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as zero.
seed: random seed
returns: a new DataFrame that represents the stratified sample

Since: 1.5.0

def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame

Returns a stratified sample without replacement based on the fraction given on each stratum.

T

stratum type

col

column that defines strata

fractions

sampling fraction for each stratum. If a stratum is not specified, we treat its fraction as zero.

seed

random seed

returns

a new DataFrame that represents the stratified sample

val df = spark.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
  (3, 3))).toDF("key", "value")
val fractions = Map(1 -> 1.0, 3 -> 0.5)
df.stat.sampleBy("key", fractions, 36L).show()
+---+-----+
|key|value|
+---+-----+
|  1|    1|
|  1|    2|
|  3|    2|
+---+-----+

Since: 1.5.0

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DataFrameStatFunctions

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DataFrameStatFunctions 

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DataFrameStatFunctions