pyspark.sql.functions.regr_syy#

pyspark.sql.functions.regr_syy(y, x)[source]#

Aggregate function: returns REGR_COUNT(y, x) * VAR_POP(y) for non-null pairs in a group, where y is the dependent variable and x is the independent variable.

New in version 3.5.0.

Parameters

yColumn or str: the dependent variable.
xColumn or str: the independent variable.

Returns

Column: REGR_COUNT(y, x) * VAR_POP(y) for non-null pairs in a group.

Examples

Example 1: All pairs are non-null

>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, 2), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_syy("y", "x")).show()
+--------------+
|regr_syy(y, x)|
+--------------+
|           5.0|
+--------------+

Example 2: All pairs’ x values are null

>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, null) AS tab(y, x)")
>>> df.select(sf.regr_syy("y", "x")).show()
+--------------+
|regr_syy(y, x)|
+--------------+
|          NULL|
+--------------+

Example 3: All pairs’ y values are null

>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (null, 1) AS tab(y, x)")
>>> df.select(sf.regr_syy("y", "x")).show()
+--------------+
|regr_syy(y, x)|
+--------------+
|          NULL|
+--------------+

Example 4: Some pairs’ x values are null

>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (3, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_syy("y", "x")).show()
+-----------------+
|   regr_syy(y, x)|
+-----------------+
|4.666666666666...|
+-----------------+

Example 5: Some pairs’ x or y values are null

>>> import pyspark.sql.functions as sf
>>> df = spark.sql("SELECT * FROM VALUES (1, 1), (2, null), (null, 3), (4, 4) AS tab(y, x)")
>>> df.select(sf.regr_syy("y", "x")).show()
+--------------+
|regr_syy(y, x)|
+--------------+
|           4.5|
+--------------+