python - 当 ID 匹配时，在其他 Pyspark Dataframe 中逐列划分 Pyspark Dataframe

Question

我有一个 PySpark DataFrame，df1，它看起来像:

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

我有第二个 PySpark DataFrame df2，它是按 CustomerID 分组并按求和函数聚合的 df1。它看起来像这样:

 CustomerID  CustomerValueSum
 12          .17
 14          .40
 17          .86

我想向 df1 添加第三列，即 df1['CustomerValue'] 除以 df2['CustomerValueSum'] 以获得相同的 CustomerID。这看起来像:

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，我正在尝试将此 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|
+----------+-------------+-----------------------+