pyspark.sql.functions.regr_avgy#

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

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.

New in version 3.5.0.

Parameters

yColumn or column name: the dependent variable.
xColumn or column name: the independent variable.

Returns

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

See also

pyspark.sql.functions.regr_avgx()
pyspark.sql.functions.regr_count()
pyspark.sql.functions.regr_intercept()
pyspark.sql.functions.regr_r2()
pyspark.sql.functions.regr_slope()
pyspark.sql.functions.regr_sxy()
pyspark.sql.functions.regr_syy()

Examples

Example 1: All pairs are non-null

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

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_avgy("y", "x"), sf.avg("y")).show()
+---------------+------+
|regr_avgy(y, x)|avg(y)|
+---------------+------+
|           NULL|   1.0|
+---------------+------+

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_avgy("y", "x"), sf.avg("y")).show()
+---------------+------+
|regr_avgy(y, x)|avg(y)|
+---------------+------+
|           NULL|  NULL|
+---------------+------+

Example 4: Some pairs’ x values are null

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

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

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