zhengruifeng commented on PR #36181:
URL: https://github.com/apache/spark/pull/36181#issuecomment-1100033744
@xinrong-databricks I guess the reason is that `NA`s in pandas-on-spark
side will be implicitly converted to `null`s in the `internal.spark_frame`
(correct me if i am wrong) , and the statisticl functions always ignore `null`s.
If we can convert `NA`s to `Double.NaN`s instead of `null`s, then the
results should be `NaN`. But I am not sure whether it is safe to use this
attribute.
```
scala> val df = Seq(("1", 1.0), ("2", 2.0), ("3", 3.0), (null,
Double.NaN)).toDF("a", "b").select(col("a").cast("double").as("a"), col("b"))
df: org.apache.spark.sql.DataFrame = [a: double, b: double]
scala> df.show
+----+---+
| a| b|
+----+---+
| 1.0|1.0|
| 2.0|2.0|
| 3.0|3.0|
|null|NaN|
+----+---+
scala> df.select(avg("a"), avg("b")).show
+------+------+
|avg(a)|avg(b)|
+------+------+
| 2.0| NaN|
+------+------+
scala> df.select(sum("a"), sum("b")).show
+------+------+
|sum(a)|sum(b)|
+------+------+
| 6.0| NaN|
+------+------+
scala> df.select(skewness("a"), skewness("b")).show
+-----------+-----------+
|skewness(a)|skewness(b)|
+-----------+-----------+
| 0.0| NaN|
+-----------+-----------+
```
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