Are the elements count big per group? If not, you can group them and use the code to calculate the median and diff.
Yong ________________________________ From: Craig Ching <craigch...@gmail.com> Sent: Wednesday, March 22, 2017 3:17 PM To: user@spark.apache.org Subject: calculate diff of value and median in a group Hi, When using pyspark, I'd like to be able to calculate the difference between grouped values and their median for the group. Is this possible? Here is some code I hacked up that does what I want except that it calculates the grouped diff from mean. Also, please feel free to comment on how I could make this better if you feel like being helpful :) from pyspark import SparkContext from pyspark.sql import SparkSession from pyspark.sql.types import ( StringType, LongType, DoubleType, StructField, StructType ) from pyspark.sql import functions as F sc = SparkContext(appName='myapp') spark = SparkSession(sc) file_name = 'data.csv' fields = [ StructField( 'group2', LongType(), True), StructField( 'name', StringType(), True), StructField( 'value', DoubleType(), True), StructField( 'group1', LongType(), True) ] schema = StructType(fields) df = spark.read.csv( file_name, header=False, mode="DROPMALFORMED", schema=schema ) df.show() means = df.select([ 'group1', 'group2', 'name', 'value']).groupBy([ 'group1', 'group2' ]).agg( F.mean('value').alias('mean_value') ).orderBy('group1', 'group2') cond = [df.group1 == means.group1, df.group2 == means.group2] means.show() df = df.select([ 'group1', 'group2', 'name', 'value']).join( means, cond ).drop( df.group1 ).drop( df.group2 ).select('group1', 'group2', 'name', 'value', 'mean_value') final = df.withColumn( 'diff', F.abs(df.value - df.mean_value)) final.show() sc.stop() And here is an example dataset I'm playing with: 100,name1,0.43,0 100,name2,0.33,0 100,name3,0.73,0 101,name1,0.29,0 101,name2,0.96,0 101,name3,0.42,0 102,name1,0.01,0 102,name2,0.42,0 102,name3,0.51,0 103,name1,0.55,0 103,name2,0.45,0 103,name3,0.02,0 104,name1,0.93,0 104,name2,0.16,0 104,name3,0.74,0 105,name1,0.41,0 105,name2,0.65,0 105,name3,0.29,0 100,name1,0.51,1 100,name2,0.51,1 100,name3,0.43,1 101,name1,0.59,1 101,name2,0.55,1 101,name3,0.84,1 102,name1,0.01,1 102,name2,0.98,1 102,name3,0.44,1 103,name1,0.47,1 103,name2,0.16,1 103,name3,0.02,1 104,name1,0.83,1 104,name2,0.89,1 104,name3,0.31,1 105,name1,0.59,1 105,name2,0.77,1 105,name3,0.45,1 and here is what I'm trying to produce: group1,group2,name,value,median,diff 0,100,name1,0.43,0.43,0.0 0,100,name2,0.33,0.43,0.10 0,100,name3,0.73,0.43,0.30 0,101,name1,0.29,0.42,0.13 0,101,name2,0.96,0.42,0.54 0,101,name3,0.42,0.42,0.0 0,102,name1,0.01,0.42,0.41 0,102,name2,0.42,0.42,0.0 0,102,name3,0.51,0.42,0.09 0,103,name1,0.55,0.45,0.10 0,103,name2,0.45,0.45,0.0 0,103,name3,0.02,0.45,0.43 0,104,name1,0.93,0.74,0.19 0,104,name2,0.16,0.74,0.58 0,104,name3,0.74,0.74,0.0 0,105,name1,0.41,0.41,0.0 0,105,name2,0.65,0.41,0.24 0,105,name3,0.29,0.41,0.24 1,100,name1,0.51,0.51,0.0 1,100,name2,0.51,0.51,0.0 1,100,name3,0.43,0.51,0.08 1,101,name1,0.59,0.59,0.0 1,101,name2,0.55,0.59,0.04 1,101,name3,0.84,0.59,0.25 1,102,name1,0.01,0.44,0.43 1,102,name2,0.98,0.44,0.54 1,102,name3,0.44,0.44,0.0 1,103,name1,0.47,0.16,0.31 1,103,name2,0.16,0.16,0.0 1,103,name3,0.02,0.16,0.14 1,104,name1,0.83,0.83,0.0 1,104,name2,0.89,0.83,0.06 1,104,name3,0.31,0.83,0.52 1,105,name1,0.59,0.59,0.0 1,105,name2,0.77,0.59,0.18 1,105,name3,0.45,0.59,0.14 Thanks for any help! Cheers, Craig