[
https://issues.apache.org/jira/browse/SPARK-27336?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16920557#comment-16920557
]
daile commented on SPARK-27336:
-------------------------------
I will check this issue
> Incorrect DataSet.summary() result
> ----------------------------------
>
> Key: SPARK-27336
> URL: https://issues.apache.org/jira/browse/SPARK-27336
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Gengliang Wang
> Priority: Major
> Attachments: test.csv
>
>
> There is a single data point in the minimum_nights column that is 1.0E8 out
> of 8k records, but .summary() says it is the 75% and the max.
> I compared this with approxQuantile, and approxQuantile for 75% gave the
> correct value of 30.0.
> To reproduce:
> {code:java}
> scala> val df =
> spark.read.format("csv").load("test.csv").withColumn("minimum_nights",
> '_c0.cast("Int"))
> df: org.apache.spark.sql.DataFrame = [_c0: string, minimum_nights: int]
> scala> df.select("minimum_nights").summary().show()
> +-------+------------------+
> |summary| minimum_nights|
> +-------+------------------+
> | count| 7072|
> | mean| 14156.35407239819|
> | stddev|1189128.5444975856|
> | min| 1|
> | 25%| 2|
> | 50%| 4|
> | 75%| 100000000|
> | max| 100000000|
> +-------+------------------+
> scala> df.stat.approxQuantile("minimum_nights", Array(0.75), 0.1)
> res1: Array[Double] = Array(30.0)
> scala> df.stat.approxQuantile("minimum_nights", Array(0.75), 0.001)
> res2: Array[Double] = Array(30.0)
> scala> df.stat.approxQuantile("minimum_nights", Array(0.75), 0.0001)
> res3: Array[Double] = Array(1.0E8)
> {code}
--
This message was sent by Atlassian Jira
(v8.3.2#803003)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
