[GitHub] [spark] zhengruifeng commented on pull request #36181: Implement `skipna`s of statistical functions of Series and DataFrame

[GitBox <gi...@apache.org>]

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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