将窗口功能应用于多列 [英] Apply window function over multiple columns

问题描述

我想执行窗口功能(平均移动平均值)，但是要在数据框的所有列上执行.

I would like to perform window function (concretely moving average), but over all columns of a dataframe.

我可以这样

from pyspark.sql import SparkSession, functions as func

df = ...

df.select([func.avg(df[col]).over(windowSpec).alias(col) for col in df.columns])

但是我担心这不是很有效.有更好的方法吗?

but I'm afraid this isn't very efficient. Is there a better way to do it?

推荐答案

一个更好的选择是创建一个新的df，在其中您可以对Window函数中的列进行分组，并对其余的列应用平均值，然后进行左连接.对于将df溢出到磁盘(或无法持久存储在内存中)的大型数据帧，这绝对是最佳选择.

An alternative which may be better is to create a new df where you Group By the columns in Window function and apply average on the remaining columns then do a left join. For large data frames where the df is being spilled over to disk (or cannot be persisted in memory), this will definitely be more optimal.

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