Spark 1.6: filtering DataFrames generated by describe()

I have considered a toy dataset I had containing some health disease data

val stddev_tobacco = rawData.describe().rdd.map{ 
    case r : Row => (r.getAs[String]("summary"),r.get(1))
}.filter(_._1 == "stddev").map(_._2).collect

You can select from the dataframe:

from pyspark.sql.functions import mean, min, max
df.select([mean('uniform'), min('uniform'), max('uniform')]).show()
+------------------+-------------------+------------------+
|      AVG(uniform)|       MIN(uniform)|      MAX(uniform)|
+------------------+-------------------+------------------+
|0.5215336029384192|0.19657711634539565|0.9970412477032209|
+------------------+-------------------+------------------+

You can also register it as a table and query the table:

val t = x.describe()
t.registerTempTable("dt")

%sql 
select * from dt