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I would like to calculate group quantiles on a Spark dataframe (using PySpark). Either an approximate or exact result would be fine. I prefer a solution that I can use within the context of groupBy / agg, so that I can mix it with other PySpark aggregate functions. If this is not possible for some reason, a different approach would be fine as well.

I also have access to the percentile_approx Hive UDF but I don't know how to use it as an aggregate function.

For the sake of specificity, suppose I have the following dataframe:

from pyspark import SparkContext
import pyspark.sql.functions as f

sc = SparkContext()   

df = sc.parallelize([
    ['A', 1],
    ['A', 2],
    ['A', 3],
    ['B', 4],
    ['B', 5],
    ['B', 6],
]).toDF(('grp', 'val'))

df_grp = df.groupBy('grp').agg(f.magic_percentile('val', 0.5).alias('med_val'))
df_grp.show()

1 Answer

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Unfortunately, and to the best of my knowledge, it seems that it is not possible to do this with "pure" PySpark commands.

However, I would like to provide you a workaround.

As you mentioned that you have access to percentile_approx, I would suggest you to use it in a SQL command:

from pyspark.sql import SQLContext

sqlContext = SQLContext(sc)

df.registerTempTable("df")

df2 = sqlContext.sql("select grp, percentile_approx(val, 0.5) as med_val from df group by grp")

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