Question

我有一个PySpark DataFrame，df1，看起来像：

CustomerID  CustomerValue
12          .17
14          .15
14          .25
17          .50
17          .01
17          .35

我有第二个PySpark DataFrame，df2，它是由CustomerID分组并由sum函数聚合的df1。它看起来像这样：

 CustomerID  CustomerValueSum
 12          .17
 14          .40
 17          .86

我想为df1添加第三列，即df1 [＆＃39; CustomerValue＆＃39;]除以df2 [＆＃39; CustomerValueSum＆＃39;]为同一个CustomerID。这看起来像是：

CustomerID  CustomerValue  NormalizedCustomerValue
12          .17            1.00
14          .15            .38
14          .25            .62
17          .50            .58
17          .01            .01
17          .35            .41

换句话说，我试图将这个Python / Pandas代码转换为PySpark：

normalized_list = []
for idx, row in df1.iterrows():
    (
        normalized_list
        .append(
            row.CustomerValue / df2[df2.CustomerID == row.CustomerID].CustomerValueSum
        )
    )
df1['NormalizedCustomerValue'] = [val.values[0] for val in normalized_list]

我该怎么做？

Answer 1

代码：

import pyspark.sql.functions as F

df1 = df1\
    .join(df2, "CustomerID")\
    .withColumn("NormalizedCustomerValue", (F.col("CustomerValue") / F.col("CustomerValueSum")))\
    .drop("CustomerValueSum")

输出：

df1.show()

+----------+-------------+-----------------------+
|CustomerID|CustomerValue|NormalizedCustomerValue|
+----------+-------------+-----------------------+
|        17|          0.5|     0.5813953488372093|
|        17|         0.01|   0.011627906976744186|
|        17|         0.35|     0.4069767441860465|
|        12|         0.17|                    1.0|
|        14|         0.15|    0.37499999999999994|
|        14|         0.25|                  0.625|
+----------+-------------+-----------------------+

Answer 2

使用Spark Window函数也可以实现这一点，您无需使用聚合值（df2）创建单独的数据框：

为输入数据框创建数据：

from pyspark.sql import HiveContext
sqlContext = HiveContext(sc)

data =[(12, 0.17), (14, 0.15), (14, 0.25), (17, 0.5), (17, 0.01), (17, 0.35)]
df1 = sqlContext.createDataFrame(data, ['CustomerID', 'CustomerValue'])
df1.show()
+----------+-------------+
|CustomerID|CustomerValue|
+----------+-------------+
|        12|         0.17|
|        14|         0.15|
|        14|         0.25|
|        17|          0.5|
|        17|         0.01|
|        17|         0.35|
+----------+-------------+

定义由CustomerID分区的窗口：

from pyspark.sql import Window
from pyspark.sql.functions import sum

w = Window.partitionBy('CustomerID')

df2 = df1.withColumn('NormalizedCustomerValue', df1.CustomerValue/sum(df1.CustomerValue).over(w)).orderBy('CustomerID')

df2.show()
+----------+-------------+-----------------------+
|CustomerID|CustomerValue|NormalizedCustomerValue|
+----------+-------------+-----------------------+
|        12|         0.17|                    1.0|
|        14|         0.15|    0.37499999999999994|
|        14|         0.25|                  0.625|
|        17|          0.5|     0.5813953488372093|
|        17|         0.01|   0.011627906976744186|
|        17|         0.35|     0.4069767441860465|
+----------+-------------+-----------------------+

ID匹配时，在其他Pyspark Dataframe中逐列分割Pyspark Dataframe

2 个答案: