regr_avgy

Aggregate function: returns the average of the dependent variable for non-null pairs in a group, where y is the dependent variable and x is the independent variable.

For the corresponding Databricks SQL function, see regr_avgy aggregate function.

Syntax

Python

import pyspark.sql.functions as sf

sf.regr_avgy(y=<y>, x=<x>)

Parameters

Parameter	Type	Description
y	pyspark.sql.Column or str	The dependent variable.
x	pyspark.sql.Column or str	The independent variable.

Returns

pyspark.sql.Column: the average of the dependent variable for non-null pairs in a group.

Examples

Example 1: All pairs are non-null.

Python

import pyspark.sql.functions as sf
df = spark.sql("SELECT * FROM VALUES (1, 2), (2, 2), (2, 3), (2, 4) AS tab(y, x)")
df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()

Output

+---------------+------+
|regr_avgy(y, x)|avg(y)|
+---------------+------+
|           1.75|  1.75|
+---------------+------+

Example 2: All pairs' x values are null.

Python

import pyspark.sql.functions as sf
df = spark.sql("SELECT * FROM VALUES (1, null) AS tab(y, x)")
df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()

Output

+---------------+------+
|regr_avgy(y, x)|avg(y)|
+---------------+------+
|           NULL|   1.0|
+---------------+------+

Example 3: All pairs' y values are null.

Python

import pyspark.sql.functions as sf
df = spark.sql("SELECT * FROM VALUES (null, 1) AS tab(y, x)")
df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()

Output

+---------------+------+
|regr_avgy(y, x)|avg(y)|
+---------------+------+
|           NULL|  NULL|
+---------------+------+

Example 4: Some pairs' x values are null.

Python

import pyspark.sql.functions as sf
df = spark.sql("SELECT * FROM VALUES (1, 2), (2, null), (2, 3), (2, 4) AS tab(y, x)")
df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()

Output

+------------------+------+
|   regr_avgy(y, x)|avg(y)|
+------------------+------+
|1.6666666666666...|  1.75|
+------------------+------+

Example 5: Some pairs' x or y values are null.

Python

import pyspark.sql.functions as sf
df = spark.sql("SELECT * FROM VALUES (1, 2), (2, null), (null, 3), (2, 4) AS tab(y, x)")
df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()

Output

+---------------+------------------+
|regr_avgy(y, x)|            avg(y)|
+---------------+------------------+
|            1.5|1.6666666666666...|
+---------------+------------------+