PySpark组中的中位数/分位数 [英] Median / quantiles within PySpark groupBy

问题描述

我想在Spark数据帧上计算组分位数(使用PySpark).近似或精确的结果都可以.我更喜欢可以在groupBy/agg上下文中使用的解决方案，以便可以将其与其他PySpark聚合函数混合使用.如果由于某种原因无法实现，那么也可以使用其他方法.

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.

这个问题是相关的，但没有指出如何将approxQuantile用作聚合函数.

This question is related but does not indicate how to use approxQuantile as an aggregate function.

我也可以访问percentile_approx Hive UDF，但是我不知道如何将其用作聚合函数.

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

为特定起见，假设我具有以下数据框:

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()

预期结果是:

+----+-------+
| grp|med_val|
+----+-------+
|   A|      2|
|   B|      5|
+----+-------+

推荐答案

我想您不再需要它了.但是会将其留给后代使用(例如，下周我忘了我).

I guess you don't need it anymore. But will leave it here for future generations (i.e. me next week when I forget).

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

grp_window = Window.partitionBy('grp')
magic_percentile = F.expr('percentile_approx(val, 0.5)')

df.withColumn('med_val', magic_percentile.over(grp_window))

或者确切地解决您的问题，这也可以:

Or to address exactly your question, this also works:

df.groupBy('grp').agg(magic_percentile.alias('med_val'))

作为奖励，您可以传递一组百分位数:

And as a bonus, you can pass an array of percentiles:

quantiles = F.expr('percentile_approx(val, array(0.25, 0.5, 0.75))')

然后您会得到一个列表.

And you'll get a list in return.

这篇关于PySpark组中的中位数/分位数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！