Sounds very good. Is there a jira for this? Would be cool to have in
1.4, because currently cannot use dataframe.describe function with NaN
values, need to filter manually all the columns.
Thanks,
Peter Rudenko
On 2015-04-02 21:18, Reynold Xin wrote:
Incidentally, we were discussing this yesterday. Here are some
thoughts on null handling in SQL/DataFrames. Would be great to get
some feedback.
1. Treat floating point NaN and null as the same "null" value. This
would be consistent with most SQL databases, and Pandas. This would
also require some inbound conversion.
2. Internally, when we see a NaN value, we should mark the null bit as
true, and keep the NaN value. When we see a null value for a floating
point field, we should mark the null bit as true, and update the field
to store NaN.
3. Externally, for floating point values, return NaN when the value is
null.
4. For all other types, return null for null values.
5. For UDFs, if the argument is primitive type only (i.e. does not
handle null) and not a floating point field, simply evaluate the
expression to null. This is consistent with most SQL UDFs and most
programming languages' treatment of NaN.
Any thoughts on this semantics?
On Thu, Apr 2, 2015 at 5:51 AM, Dean Wampler <deanwamp...@gmail.com
<mailto:deanwamp...@gmail.com>> wrote:
I'm afraid you're a little stuck. In Scala, the types Int, Long,
Float,
Double, Byte, and Boolean look like reference types in source
code, but
they are compiled to the corresponding JVM primitive types, which
can't be
null. That's why you get the warning about ==.
It might be your best choice is to use NaN as the placeholder for
null,
then create one DF using a filter that removes those values. Use
that DF to
compute the mean. Then apply a map step to the original DF to
translate the
NaN's to the mean.
dean
Dean Wampler, Ph.D.
Author: Programming Scala, 2nd Edition
<http://shop.oreilly.com/product/0636920033073.do> (O'Reilly)
Typesafe <http://typesafe.com>
@deanwampler <http://twitter.com/deanwampler>
http://polyglotprogramming.com
On Thu, Apr 2, 2015 at 7:54 AM, Peter Rudenko
<petro.rude...@gmail.com <mailto:petro.rude...@gmail.com>>
wrote:
> Hi i need to implement MeanImputor - impute missing values with
mean. If i
> set missing values to null - then dataframe aggregation works
properly, but
> in UDF it treats null values to 0.0. Here’s example:
>
> |val df = sc.parallelize(Array(1.0,2.0, null, 3.0, 5.0, null)).toDF
> df.agg(avg("_1")).first //res45: org.apache.spark.sql.Row = [2.75]
> df.withColumn("d2", callUDF({(value: Double) => value}, DoubleType,
> df("d"))),show() d d2 1.0 1.0 2.0 2.0 null 0.0 3.0 3.0 5.0 5.0
null 0.0 val
> df = sc.parallelize(Array(1.0,2.0, Double.NaN, 3.0, 5.0,
Double.NaN)).toDF
> df.agg(avg("_1")).first //res46: org.apache.spark.sql.Row =
[Double.NaN] |
>
> In UDF i cannot compare scala’s Double to null:
>
> |comparing values of types Double and Null using `==' will
always yield
> false [warn] if (value==null) meanValue else value |
>
> With Double.NaN instead of null i can compare in UDF, but
aggregation
> doesn’t work properly. Maybe it’s related to :
https://issues.apache.org/
> jira/browse/SPARK-6573
>
> Thanks,
> Peter Rudenko
>
>
>
