group-by - pyspark:聚合列中最常见的值

Question

  aggregrated_table = df_input.groupBy('city', 'income_bracket') \
        .agg(
       count('suburb').alias('suburb'),
       sum('population').alias('population'),
       sum('gross_income').alias('gross_income'),
       sum('no_households').alias('no_households'))

想要按城市和收入阶层进行分组，但每个城市内的某些郊区有不同的收入阶层。如何按每个城市最常出现的收入阶层进行分组？

例如:

city1 suburb1 income_bracket_10 
city1 suburb1 income_bracket_10 
city1 suburb2 income_bracket_10 
city1 suburb3 income_bracket_11 
city1 suburb4 income_bracket_10 

将按收入_bracket_10分组

Answer 1

在聚合之前使用窗口函数可能会达到目的:

from pyspark.sql import Window
import pyspark.sql.functions as psf

w = Window.partitionBy('city')
aggregrated_table = df_input.withColumn(
    "count", 
    psf.count("*").over(w)
).withColumn(
    "rn", 
    psf.row_number().over(w.orderBy(psf.desc("count")))
).filter("rn = 1").groupBy('city', 'income_bracket').agg(
   psf.count('suburb').alias('suburb'),
   psf.sum('population').alias('population'),
   psf.sum('gross_income').alias('gross_income'),
   psf.sum('no_households').alias('no_households'))

您还可以在聚合后使用窗口函数，因为您要保存 (city, Income_bracket) 出现次数的计数。