pyspark.sql.functions.regr_count#

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

Aggregate function: returns the number of non-null number 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 number of non-null number pairs in a group.

See also

pyspark.sql.functions.regr_avgx()
pyspark.sql.functions.regr_avgy()
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_count("y", "x"), sf.count(sf.lit(0))).show()
+----------------+--------+
|regr_count(y, x)|count(0)|
+----------------+--------+
|               4|       4|
+----------------+--------+

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_count("y", "x"), sf.count(sf.lit(0))).show()
+----------------+--------+
|regr_count(y, x)|count(0)|
+----------------+--------+
|               0|       1|
+----------------+--------+

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_count("y", "x"), sf.count(sf.lit(0))).show()
+----------------+--------+
|regr_count(y, x)|count(0)|
+----------------+--------+
|               0|       1|
+----------------+--------+

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_count("y", "x"), sf.count(sf.lit(0))).show()
+----------------+--------+
|regr_count(y, x)|count(0)|
+----------------+--------+
|               3|       4|
+----------------+--------+

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_count("y", "x"), sf.count(sf.lit(0))).show()
+----------------+--------+
|regr_count(y, x)|count(0)|
+----------------+--------+
|               2|       4|
+----------------+--------+