在 Spark 中分组和标准化 [英] Group By and standardize in spark

问题描述

我有以下数据框:

import pandas as pd
import numpy as np
df = pd.DataFrame([[1,2,3],[1,2,1],[1,2,2],[2,2,2],[2,3,2],[2,4,2]],columns=["a","b","c"])
df = df.set_index("a")
df.groupby("a").mean()
df.groupby("a").std()

我想标准化每个键的数据框，不标准化整个列向量.

I want to standardize the dataframe for each key and NOT standardize the whole column vector.

因此对于以下示例，输出将是:

So for the following example the output would be:

a = 1: 
  Column: b
  (2 - 2) / 0.0
  (2 - 2) / 0.0
  (2 - 2) / 0.0
  Column: c
  (3 - 2) / 1.0
  (1 - 2) / 1.0
  (2 - 2) / 1.0

然后我会对每个组的每个值进行标准化

And then I would get each value standardized per group

我怎样才能在 spark 中做到这一点?

How can I do that in spark?

谢谢

推荐答案

With Spark DataFrame:

sdf = spark.createDataFrame(df)

进口:

from pyspark.sql.functions import *
from pyspark.sql.window import Window

def z_score(c, w):
    return (col(c) - mean(c).over(w)) / stddev(c).over(w)

窗口:

w = Window.partitionBy("a")

解决方案:

sdf.select("a", z_score("b", w).alias("a"), z_score("c", w).alias("b")).show()
+---+----+----+                                                                 
|  a|   a|   b|
+---+----+----+
|  1|null| 1.0|
|  1|null|-1.0|
|  1|null| 0.0|
|  2|-1.0|null|
|  2| 0.0|null|
|  2| 1.0|null|
+---+----+----+

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