在多列上应用窗口函数 [英] Apply window function over multiple columns

问题描述

我想执行窗口函数(具体来说是移动平均线)，但要在数据帧的所有列上执行.

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

我可以这样做

from pyspark.sql import SparkSession, functions as func

df = ...

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

但恐怕这不是很有效.有没有更好的方法?

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

推荐答案

另一种可能更好的替代方法是创建一个新的 df，您可以在其中按 Window 函数中的列分组并对其余列应用平均值，然后进行左连接.对于 df 溢出到磁盘(或无法保存在内存中)的大型数据帧，这肯定会更优化.

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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