regr_intercept

Aggregate function: returns the intercept of the univariate linear regression line 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_intercept aggregate function.

Syntax

import pyspark.sql.functions as sf

sf.regr_intercept(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 intercept of the univariate linear regression line 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_intercept("y", "x")).show()

+--------------------+
|regr_intercept(y, x)|
+--------------------+
|                 0.0|
+--------------------+

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_intercept("y", "x")).show()

+--------------------+
|regr_intercept(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_intercept("y", "x")).show()

+--------------------+
|regr_intercept(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_intercept("y", "x")).show()

+--------------------+
|regr_intercept(y, x)|
+--------------------+
|                 0.0|
+--------------------+

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_intercept("y", "x")).show()

+--------------------+
|regr_intercept(y, x)|
+--------------------+
|                 0.0|
+--------------------+