仅使用DataFrame API规范Spark DataFrame中多个列的值

Question

我试图通过减去均值并除以每列的stddev来标准化spark数据帧中多列的值。这是我到目前为止的代码：

from pyspark.sql import Row
from pyspark.sql.functions import stddev_pop, avg

df = spark.createDataFrame([Row(A=1, B=6), Row(A=2, B=7), Row(A=3, B=8),
                            Row(A=4, B=9), Row(A=5, B=10)])

exprs = [x - (avg(x)) / stddev_pop(x) for x in df.columns]    
df.select(exprs).show() 

这给了我结果：

+------------------------------+------------------------------+
|(A - (avg(A) / stddev_pop(A)))|(B - (avg(B) / stddev_pop(B)))|
+------------------------------+------------------------------+
|                          null|                          null|
+------------------------------+------------------------------+

我希望的地方：

+------------------------------+------------------------------+
|(A - (avg(A) / stddev_pop(A)))|(B - (avg(B) / stddev_pop(B)))|
+------------------------------+------------------------------+
|                  -1.414213562|                  -1.414213562|
|                  -0.707106781|                  -0.707106781|
|                             0|                             0|
|                   0.707106781|                   0.707106781|
|                   1.414213562|                   1.414213562|
+------------------------------+------------------------------+

我相信我可以使用mllib中的StandardScaler类来做到这一点，但我更愿意在可能的情况下仅使用数据框API来做到这一点-如果只是作为学习练习。

Answer 1

借助这里的答案，我想到了：

from pyspark.sql.functions import stddev_pop, avg, broadcast

cols = df.columns    
stats = (df.groupBy().agg(
        *([stddev_pop(x).alias(x + '_stddev') for x in cols] + 
          [avg(x).alias(x + '_avg') for x in cols])))

df = df.join(broadcast(stats))

exprs = [(df[x] - df[x + '_avg']) / df[x + '_stddev'] for x in cols]
df.select(exprs).show()

+------------------------+------------------------+
|((A - A_avg) / A_stddev)|((B - B_avg) / B_stddev)|
+------------------------+------------------------+
|      -1.414213562373095|      -1.414213562373095|
|     -0.7071067811865475|     -0.7071067811865475|
|                     0.0|                     0.0|
|      0.7071067811865475|      0.7071067811865475|
|       1.414213562373095|       1.414213562373095|
+------------------------+------------------------+