zhengruifeng commented on code in PR #47343:
URL: https://github.com/apache/spark/pull/47343#discussion_r1683702370
##########
python/pyspark/sql/functions/builtin.py:
##########
@@ -3708,17 +3776,85 @@ def regr_avgy(y: "ColumnOrName", x: "ColumnOrName") ->
Column:
Examples
--------
- >>> from pyspark.sql import functions as sf
- >>> x = (sf.col("id") % 3).alias("x")
- >>> y = (sf.randn(42) + x * 10).alias("y")
- >>> spark.range(0, 1000, 1, 1).select(x, y).select(
- ... sf.regr_avgy("y", "x"), sf.avg("y")
- ... ).show()
- +-----------------+-----------------+
- | regr_avgy(y, x)| avg(y)|
- +-----------------+-----------------+
- |9.980732994136...|9.980732994136...|
- +-----------------+-----------------+
+ Example 1: All paris are non-null
+
+ >>> import pyspark.sql.functions as sf
+ >>> from pyspark.sql.types import IntegerType, StructField, StructType
+ >>> schema = StructType([
+ ... StructField('y', IntegerType(), True),
+ ... StructField('x', IntegerType(), True)
+ ... ])
+ >>> df = spark.createDataFrame([(1, 2), (2, 2), (2, 3), (2, 4)], schema)
+ >>> df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()
+ +---------------+------+
+ |regr_avgy(y, x)|avg(y)|
+ +---------------+------+
+ | 1.75| 1.75|
+ +---------------+------+
+
+ Example 2: All paris's x values are null
+
+ >>> import pyspark.sql.functions as sf
+ >>> from pyspark.sql.types import IntegerType, StructField, StructType
+ >>> schema = StructType([
+ ... StructField('y', IntegerType(), True),
+ ... StructField('x', IntegerType(), True)
+ ... ])
+ >>> df = spark.createDataFrame([(1, None)], schema)
+ >>> df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()
+ +---------------+------+
+ |regr_avgy(y, x)|avg(y)|
+ +---------------+------+
+ | NULL| 1.0|
+ +---------------+------+
+
+ Example 3: All paris's y values are null
+
+ >>> import pyspark.sql.functions as sf
+ >>> from pyspark.sql.types import IntegerType, StructField, StructType
+ >>> schema = StructType([
+ ... StructField('y', IntegerType(), True),
+ ... StructField('x', IntegerType(), True)
+ ... ])
+ >>> df = spark.createDataFrame([(None, 1)], schema)
+ >>> df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()
+ +---------------+------+
+ |regr_avgy(y, x)|avg(y)|
+ +---------------+------+
+ | NULL| NULL|
+ +---------------+------+
+
+ Example 4: Some paris's x values are null
+
+ >>> import pyspark.sql.functions as sf
+ >>> from pyspark.sql.types import IntegerType, StructField, StructType
+ >>> schema = StructType([
+ ... StructField('y', IntegerType(), True),
+ ... StructField('x', IntegerType(), True)
+ ... ])
+ >>> df = spark.createDataFrame([(1, 2), (2, None), (2, 3), (2, 4)], schema)
+ >>> df.select(sf.regr_avgy("y", "x"), sf.avg("y")).show()
+ +------------------+------+
+ | regr_avgy(y, x)|avg(y)|
+ +------------------+------+
+ |1.6666666666666667| 1.75|
Review Comment:
The output may vary with different envs, e.g. different CPUs
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