DataFrameStatFunctions class

Functionality for statistical functions with a DataFrame.

Supports Spark Connect

Syntax

DataFrame.stat

Methods

Method	Description
approxQuantile(col, probabilities, relativeError)	Calculates the approximate quantiles of numerical columns of a DataFrame.
corr(col1, col2, method)	Calculates the correlation of two columns as a double value. Currently only supports the Pearson Correlation Coefficient.
cov(col1, col2)	Calculates the sample covariance for the given columns as a double value.
crosstab(col1, col2)	Computes a pair-wise frequency table of the given columns.
freqItems(cols, support)	Finds frequent items for columns, possibly with false positives.
sampleBy(col, fractions, seed)	Returns a stratified sample without replacement based on the fraction given on each stratum.

Examples

Approximate quantiles

data = [(1,), (2,), (3,), (4,), (5,)]
df = spark.createDataFrame(data, ["values"])
df.stat.approxQuantile("values", [0.0, 0.5, 1.0], 0.05)

[1.0, 3.0, 5.0]

Correlation

df = spark.createDataFrame([(1, 12), (10, 1), (19, 8)], ["c1", "c2"])
df.stat.corr("c1", "c2")

-0.3592106040535498

Covariance

df = spark.createDataFrame([(1, 12), (10, 1), (19, 8)], ["c1", "c2"])
df.stat.cov("c1", "c2")

-18.0

Cross tabulation

df = spark.createDataFrame([(1, 11), (1, 11), (3, 10), (4, 8), (4, 8)], ["c1", "c2"])
df.stat.crosstab("c1", "c2").sort("c1_c2").show()

+-----+---+---+---+
|c1_c2| 10| 11|  8|
+-----+---+---+---+
|    1|  0|  2|  0|
|    3|  1|  0|  0|
|    4|  0|  0|  2|
+-----+---+---+---+

Frequent items

from pyspark.sql import functions as sf

df = spark.createDataFrame([(1, 11), (1, 11), (3, 10), (4, 8), (4, 8)], ["c1", "c2"])
df2 = df.stat.freqItems(["c1", "c2"])
df2.select([sf.sort_array(c).alias(c) for c in df2.columns]).show()

+------------+------------+
|c1_freqItems|c2_freqItems|
+------------+------------+
|   [1, 3, 4]| [8, 10, 11]|
+------------+------------+

Stratified sample

from pyspark.sql import functions as sf

dataset = spark.range(0, 100, 1, 5).select((sf.col("id") % 3).alias("key"))
dataset.stat.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0).groupBy("key").count().orderBy("key").show()

+---+-----+
|key|count|
+---+-----+
|  0|    4|
|  1|    9|
+---+-----+